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Reflections on the Decline of Science in England, and on Some of Its Causes by Charles Babbage, is part of the HackerNoon Books Series. You can jump to any chapter in this book . Section 3 here SECTION 3. OF THE INFLUENCE OF THE COLLEGES OF PHYSICIANS AND SURGEONS IN THE ROYAL SOCIETY. The honour of belonging to the Royal Society is much sought after by medical men, as contributing to the success of their professional efforts, and two consequences result from it. In the first place, the pages of the Transactions of the Royal Society occasionally contain medical papers of very moderate merit; and, in the second, the preponderance of the medical interest introduces into the Society some of the jealousies of that profession. On the other hand, medicine is intimately connected with many sciences, and its professors are usually too much occupied in their practice to exert themselves, except upon great occasions. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Charles Babbage (1998).Reflections on the Decline of Science in England, and on Some of Its Causes. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/1216/pg1216-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at www.gutenberg.org https://www.gutenberg.org/policy/license.html

OF THE INFLUENCE OF THE COLLEGES OF PHYSICIANS AND SURGEONS

It’s no secret we've stumbled upon a formidable obstacle to our AI-powered future: the staggering energy consumption of our current models. Industry leaders are scrambling to come up with short-term answers to ensure they don’t miss the wave, with ambitious efforts like Microsoft’s reopening of nuclear reactors on Three Mile Island and Google working on “first-of-its-kind” geothermal projects. And while that plays out at the big kid’s table, a flock of new startups are building on the progress made in recent years, re-thinking the fundamentals to see if there are solutions that could serve as the long-term solution. One that doesn’t require hundreds of millions of dollars in infrastructure investment. AI’s Power Demands Are a Ticking Time Bomb Operating large language models in their current iteration is an energy-intensive process that's rapidly approaching unsustainable levels. Training a single AI model can emit as much carbon as five cars over their entire lifetimes. It's not just an environmental concern; it's a scalability nightmare threatening to derail the AI revolution before it fully takes flight. Consider these sobering facts: GPT-3, with its 175 billion parameters, reportedly required 1,287 MWh for a single training run.
The carbon footprint of training a large NLP model is estimated to be around 626,000 pounds of CO2 equivalent.
As models grow, so does their energy consumption – often at a superlinear rate. And as the industry pushes for more advanced AI capabilities, this energy consumption is set to skyrocket. This is not only a problem on an operational level but also in the greater picture, as industry leaders like Google have pledged to achieve net-zero carbon emissions through the purchase of carbon credits from firms doing things like plugging orphaned oil and gas wells - a market where demand is already far out-pacing supply. Efficiency & Minimal Parameter Activation: The New North Star of AI Innovation The solution may be rather simple: smarter, smaller, more efficient models built for a set of specific purposes. Narrowing the scope, if you will. One such example is the open-source Aria model built by Rhymes, which employs minimal parameter activation. While the Aria model boasts a total of 25.3 billion parameters, it only activates a mere 3.9 billion for any given task. Traditional, mainstream models like GPT-3 activate all of their parameters for every task, regardless of complexity, while Aria's approach is like a surgeon using only the necessary instruments for a specific procedure. Many surgeons would tell you they don’t need to deploy the entire operating room's worth of equipment for every operation. Rhyme has deployed this in practice with BeaGo, which they call “a smarter, faster AI search.” Based on my tests, BeaGo’s results were indistinguishable from competing products from Perplexity and other, most energy and time-intensive products. But it’s more than simply narrowing the scope: the startup has built a multimodal open-source mixture-of-experts model that sorts and intelligently manages large, long-context data of all types, including text, video, and images. Rhymes’ solution may be lighting the pathway for AI in 2025 and beyond, all without the hundreds of millions of dollars in infrastructure spending. The Reminder: Innovation Never Stops In the end, the work of companies like Rhymes serves as a reminder that just because we found something that works, doesn’t mean the task of innovating is over. While Microsoft and Google run with our existing large language models, working to productize and bring AI to the mass-market, others cannot stop working on building something even better. I’m heartened by the startup-driven approach that I see here at the end of 2024 – combining multimodal capabilities, selective parameter activation, and open-source collaboration – offers a blueprint for how we can achieve a vision of an AI that both works and works for the planet. It’s no secret we've stumbled upon a formidable obstacle to our AI-powered future: the staggering energy consumption of our current models. Industry leaders are scrambling to come up with short-term answers to ensure they don’t miss the wave, with ambitious efforts like Microsoft’s reopening of nuclear reactors on Three Mile Island and Google working on “first-of-its-kind” geothermal projects . Microsoft’s reopening of nuclear reactors on Three Mile Island Microsoft’s reopening of nuclear reactors on Three Mile Island Google working on “first-of-its-kind” geothermal projects Google working on “first-of-its-kind” geothermal projects And while that plays out at the big kid’s table, a flock of new startups are building on the progress made in recent years, re-thinking the fundamentals to see if there are solutions that could serve as the long-term solution. One that doesn’t require hundreds of millions of dollars in infrastructure investment. doesn’t AI’s Power Demands Are a Ticking Time Bomb Operating large language models in their current iteration is an energy-intensive process that's rapidly approaching unsustainable levels. Training a single AI model can emit as much carbon as five cars over their entire lifetimes. It's not just an environmental concern; it's a scalability nightmare threatening to derail the AI revolution before it fully takes flight. Consider these sobering facts: GPT-3, with its 175 billion parameters, reportedly required 1,287 MWh for a single training run. The carbon footprint of training a large NLP model is estimated to be around 626,000 pounds of CO2 equivalent. As models grow, so does their energy consumption – often at a superlinear rate. GPT-3, with its 175 billion parameters, reportedly required 1,287 MWh for a single training run. The carbon footprint of training a large NLP model is estimated to be around 626,000 pounds of CO2 equivalent. As models grow, so does their energy consumption – often at a superlinear rate. And as the industry pushes for more advanced AI capabilities, this energy consumption is set to skyrocket. This is not only a problem on an operational level but also in the greater picture, as industry leaders like Google have pledged to achieve net-zero carbon emissions through the purchase of carbon credits from firms doing things like plugging orphaned oil and gas wells - a market where demand is already far out-pacing supply. plugging orphaned oil and gas wells Efficiency & Minimal Parameter Activation: The New North Star of AI Innovation The solution may be rather simple: smarter, smaller, more efficient models built for a set of specific purposes. Narrowing the scope, if you will. One such example is the open-source Aria model built by Rhymes , which employs minimal parameter activation. While the Aria model boasts a total of 25.3 billion parameters, it only activates a mere 3.9 billion for any given task. Traditional, mainstream models like GPT-3 activate all of their parameters for every task, regardless of complexity, while Aria's approach is like a surgeon using only the necessary instruments for a specific procedure. Many surgeons would tell you they don’t need to deploy the entire operating room's worth of equipment for every operation. Aria model built by Rhymes Rhyme has deployed this in practice with BeaGo, which they call “a smarter, faster AI search.” Based on my tests, BeaGo’s results were indistinguishable from competing products from Perplexity and other, most energy and time-intensive products. But it’s more than simply narrowing the scope: the startup has built a multimodal open-source mixture-of-experts model that sorts and intelligently manages large, long-context data of all types, including text, video, and images. Rhymes’ solution may be lighting the pathway for AI in 2025 and beyond, all without the hundreds of millions of dollars in infrastructure spending. The Reminder: Innovation Never Stops In the end, the work of companies like Rhymes serves as a reminder that just because we found something that works , doesn’t mean the task of innovating is over. While Microsoft and Google run with our existing large language models, working to productize and bring AI to the mass-market, others cannot stop working on building something even better. works I’m heartened by the startup-driven approach that I see here at the end of 2024 – combining multimodal capabilities, selective parameter activation, and open-source collaboration – offers a blueprint for how we can achieve a vision of an AI that both works and works for the planet.

The AI Energy Crisis & A Newfound Push for Efficiency

Die KI-Energiekrise und ein neuer Drang nach Effizienz

La crise énergétique de l'IA et une nouvelle dynamique en faveur de l'efficacité

משבר האנרגיה של AI ודחיפה חדשה ליעילות

La crisi energetica dell’intelligenza artificiale e una nuova spinta verso l’efficienza

De AI-energiecrisis en een nieuwe drang naar efficiëntie

A crise energética da IA e um novo impulso para a eficiência

Sales Pitch Shake-Up: Part One—3 Pitfalls To Pull The Plug On

Artificial Intelligence (AI) in healthcare has long captured our imagination, fueled by portrayals in movies and TV shows where robotic surgeons and virtual assistants are the norm. But beyond science fiction, AI has profoundly impacted real-world healthcare. As it has evolved over the years, AI has transformed from an ambitious idea into a powerful tool, reshaping how we deliver care. The Three Waves of AI in Healthcare​ The journey of AI in healthcare can be categorized into three distinct waves: 1. The First Wave: Foundations and Early Exploration​ The first wave emerged in the aftermath of World War II with the introduction of Machine Learning and the coining of the term "Artificial Intelligence." During this period, foundational concepts like the Turing Test were developed, laying the groundwork for future advancements. While the early stages of AI were exploratory, they set the stage for what was to come. Colossus was the world's first electronic programmable computer, at Bletchley Park in Bedfordshire. Bletchley Park Trust/Science & Society Picture Library via Chris Monk 2. The Second Wave: Expert Systems and Rule-Based AI​ The second wave, spanning the 1980s, was marked by the development of expert systems such as MYCIN and DXPlain. These systems aimed to mimic human decision-making using large sets of rules to provide medical recommendations. However, their potential was limited by the complexity of managing thousands of interacting rules and the constraints of the data available at the time, which primarily came from textbooks and human experts. DXPlain, one of the pioneering expert systems in healthcare, aimed to emulate human decision-making. 3. The Third Wave: AI Renaissance & ChatGPT​ The third and current wave of AI began in the 2010s, characterized by the introduction of deep learning and neural networks. This era has been driven by the availability of large datasets, advances in computing power (especially GPUs), and innovative architectures like transformers which gave AI the ability to understand bodies of text as a whole versus just sequences of words. The "Attention is All You Need" paper introduced the transformer architecture, revolutionizing natural language processing. The release of models like GPT-3 and ChatGPT has revolutionized natural language processing and catalyzed the development of AI-driven applications in healthcare, enabling conversational agents, predictive analytics, and personalized care. These same technologies are now being deployed to assist clinicians through prepopulating notes, automating repetitive tasks, and providing decision support. Imaging, pathology, and radiology are some of the fields where AI is making significant inroads, helping to reduce the time and cost of diagnosis while improving accuracy. For example, AI can now analyze medical images with remarkable precision, often outperforming human experts in specific tasks like detecting diabetic retinopathy or analyzing radiology scans. They are also capable of operating in the background to find opportunistic diagnoses that may have been missed by human eyes. This concept is often overlooked and will become more important as costs continue to come down. via Avicenna.AI a leading medical imaging AI company One of the most exciting developments is AI’s ability to integrate multimodal data—combining text, images, biosensor data, and even genomic information to provide a more comprehensive understanding of a patient’s condition. This capability allows AI to go beyond simple image recognition and contribute to the overall diagnostic process, taking into account the patient’s medical history and up to date data. The Da Vinci Surgical System is a prime example of how AI is transforming surgery (from their website) While fully autonomous surgery may still be a decade away, the integration of AI into the surgical process is already enhancing precision and outcomes. The da Vinci Surgical System, which combines robotic surgery with AI, is a prime example of how technology is pushing the boundaries of what’s possible. AI in Outreach and Care Delivery​ At CareLaunch, we recognize the transformative power of AI and are integrating advanced AI capabilities into our CRM platform to support healthcare providers. AI is a strong fit for our mission to streamline patient communication, optimize workflows, and provide actionable insights. For instance, our AI-powered features can predict patient needs based on their historical data, enabling proactive outreach through our CRM. This is particularly valuable in managing chronic conditions, where timely interventions can prevent complications and improve quality of life. At the core of any AI or healthcare based initiative is a strong data foundation. The CareLaunch platform leverages HL7 FHIR standards to ensure that data is interoperable and secure. This also allows for the integration of AI models and other third-party tools to enhance the platform’s capabilities. Our CRM system takes the guesswork out of patient communication by automating outreach campaigns with data-driven precision. Whether it's reminding patients about upcoming appointments, sending post-visit follow-ups, or launching preventive care initiatives, CareLaunch ensures that every message is sent at the right time to the right patient. This automation reduces administrative burden on healthcare providers, allowing them to focus more on patient care while ensuring that no critical communication is overlooked. The Mayo Clinic's use of AI in diagnostics offers a real-world example of how AI-driven insights can lead to better patient care—a vision we share and strive to achieve with our platform. At CareLaunch, we’re redefining what’s possible in healthcare outreach and patient relationship management. Our AI-driven CRM platform is designed to help you deliver personalized care, streamline communications, and ultimately improve patient outcomes. Ready to see how CareLaunch can transform your practice? Schedule a demo today and discover how our innovative solutions can help you connect with your patients in more meaningful ways. For more insights into how CareLaunch is leveraging AI to transform healthcare, visit our blog or explore our platform to see how we can help you enhance patient care and operational efficiency. Artificial Intelligence (AI) in healthcare has long captured our imagination, fueled by portrayals in movies and TV shows where robotic surgeons and virtual assistants are the norm. But beyond science fiction, AI has profoundly impacted real-world healthcare. As it has evolved over the years, AI has transformed from an ambitious idea into a powerful tool, reshaping how we deliver care. The Three Waves of AI in Healthcare ​ ​ The journey of AI in healthcare can be categorized into three distinct waves: 1. The First Wave: Foundations and Early Exploration ​ The First Wave: Foundations and Early Exploration ​ The first wave emerged in the aftermath of World War II with the introduction of Machine Learning and the coining of the term "Artificial Intelligence." During this period, foundational concepts like the Turing Test were developed, laying the groundwork for future advancements. While the early stages of AI were exploratory, they set the stage for what was to come. Turing Test Colossus was the world's first electronic programmable computer, at Bletchley Park in Bedfordshire. Bletchley Park Trust/Science & Society Picture Library via Chris Monk Colossus was the world's first electronic programmable computer, at Bletchley Park in Bedfordshire. Bletchley Park Trust/Science & Society Picture Library via Chris Monk Chris Monk 2. The Second Wave: Expert Systems and Rule-Based AI ​ The Second Wave: Expert Systems and Rule-Based AI ​ The second wave, spanning the 1980s, was marked by the development of expert systems such as MYCIN and DXPlain . These systems aimed to mimic human decision-making using large sets of rules to provide medical recommendations. However, their potential was limited by the complexity of managing thousands of interacting rules and the constraints of the data available at the time, which primarily came from textbooks and human experts. MYCIN DXPlain DXPlain, one of the pioneering expert systems in healthcare, aimed to emulate human decision-making. DXPlain, one of the pioneering expert systems in healthcare, aimed to emulate human decision-making. 3. The Third Wave: AI Renaissance & ChatGPT ​ The Third Wave: AI Renaissance & ChatGPT ​ The third and current wave of AI began in the 2010s, characterized by the introduction of deep learning and neural networks. This era has been driven by the availability of large datasets, advances in computing power (especially GPUs), and innovative architectures like transformers which gave AI the ability to understand bodies of text as a whole versus just sequences of words. transformers The "Attention is All You Need" paper introduced the transformer architecture, revolutionizing natural language processing. The "Attention is All You Need" paper introduced the transformer architecture, revolutionizing natural language processing. The release of models like GPT-3 and ChatGPT has revolutionized natural language processing and catalyzed the development of AI-driven applications in healthcare, enabling conversational agents, predictive analytics, and personalized care. These same technologies are now being deployed to assist clinicians through prepopulating notes, automating repetitive tasks, and providing decision support. GPT-3 ChatGPT Imaging, pathology, and radiology are some of the fields where AI is making significant inroads, helping to reduce the time and cost of diagnosis while improving accuracy. For example, AI can now analyze medical images with remarkable precision, often outperforming human experts in specific tasks like detecting diabetic retinopathy or analyzing radiology scans. They are also capable of operating in the background to find opportunistic diagnoses that may have been missed by human eyes. This concept is often overlooked and will become more important as costs continue to come down. via Avicenna.AI a leading medical imaging AI company via Avicenna.AI a leading medical imaging AI company Avicenna.AI One of the most exciting developments is AI’s ability to integrate multimodal data—combining text, images, biosensor data, and even genomic information to provide a more comprehensive understanding of a patient’s condition. This capability allows AI to go beyond simple image recognition and contribute to the overall diagnostic process, taking into account the patient’s medical history and up to date data. The Da Vinci Surgical System is a prime example of how AI is transforming surgery (from their website) The Da Vinci Surgical System is a prime example of how AI is transforming surgery (from their website ) their website While fully autonomous surgery may still be a decade away, the integration of AI into the surgical process is already enhancing precision and outcomes. The da Vinci Surgical System , which combines robotic surgery with AI, is a prime example of how technology is pushing the boundaries of what’s possible. da Vinci Surgical System AI in Outreach and Care Delivery ​ ​ At CareLaunch, we recognize the transformative power of AI and are integrating advanced AI capabilities into our CRM platform to support healthcare providers. AI is a strong fit for our mission to streamline patient communication, optimize workflows, and provide actionable insights. For instance, our AI-powered features can predict patient needs based on their historical data, enabling proactive outreach through our CRM. This is particularly valuable in managing chronic conditions, where timely interventions can prevent complications and improve quality of life. At the core of any AI or healthcare based initiative is a strong data foundation. The CareLaunch platform leverages HL7 FHIR standards to ensure that data is interoperable and secure. This also allows for the integration of AI models and other third-party tools to enhance the platform’s capabilities. Our CRM system takes the guesswork out of patient communication by automating outreach campaigns with data-driven precision. Whether it's reminding patients about upcoming appointments, sending post-visit follow-ups, or launching preventive care initiatives, CareLaunch ensures that every message is sent at the right time to the right patient. This automation reduces administrative burden on healthcare providers, allowing them to focus more on patient care while ensuring that no critical communication is overlooked. The Mayo Clinic's use of AI in diagnostics offers a real-world example of how AI-driven insights can lead to better patient care—a vision we share and strive to achieve with our platform. Mayo Clinic's use of AI in diagnostics At CareLaunch, we’re redefining what’s possible in healthcare outreach and patient relationship management. Our AI-driven CRM platform is designed to help you deliver personalized care, streamline communications, and ultimately improve patient outcomes. Ready to see how CareLaunch can transform your practice? Schedule a demo today and discover how our innovative solutions can help you connect with your patients in more meaningful ways. Schedule a demo For more insights into how CareLaunch is leveraging AI to transform healthcare, visit our blog or explore our platform to see how we can help you enhance patient care and operational efficiency. our blog our platform

The Evolution of AI in Healthcare: From Ambitious Beginnings to What's Next

Wouldn’t it be so useful if we had a robot that could do our laundry? We already have so many conveniences that people a century ago could only wish for. But we still don’t have an automated way of doing laundry, which includes dealing with the washer and dryer, ironing, folding clothes, sorting, and putting everything away in neat piles in their respective drawers or closets. Robotics are already widely used and have been integrated into factory floors and warehouses to improve efficiencies for years. But the robots we have today are largely limited to moving from point A to point B and tackling a handful of tasks. And we are familiar with these robots. We’ve all seen Amazon’s amazing robots that carry shelves of items throughout the warehouse, and Tesla’s red robots that assemble cars on the factory floor. These are great applications, but we can use AI and robots to move to the next level. When it comes to household cleaning, we already have robotic vacuum cleaners and mops, window cleaners, and lawnmowers. All we’re missing is a laundry robot and perhaps a humanoid that can cook a full meal or even just peel potatoes. Pet care is another great niche for robots. Imagine having AI-controlled pet feeders that ensure pets are fed on time, with the correct portions. Pet care robots could play with pets, clean litter boxes, or even take dogs for walks. Where AI humanoids can be especially helpful is in the realm of care for the elderly and infirm. Robots can assist with mobility, providing support for standing, walking, or moving between rooms. They can help with bathing, dressing, and medication management and can also provide emotional support, remind users of appointments, and monitor health vitals. Patient care robots in hospitals, clinics, and old age homes, could monitor vital signs, assist with patient mobility, and help in rehabilitation exercises. Security and surveillance is another area where robots could prove useful. Home security robots could patrol the premises, detect intruders, and alert homeowners or authorities in case of unusual activity. Smart door and window monitors integrated with humanoid robots could greet visitors, verify identity, and control access to the home. It is likely that in the coming decades, we will see the further development of robots that can assist in customer service and simple office jobs, the construction industry, agriculture, medicine and surgery, and of course the military. Customer service robots would be able to assist with reception, greeting customers and clients, answering questions, and directing them to the appropriate service or person. Service robots in restaurants and hotels could clean tables and floors, take orders, deliver food, or provide information, enhancing customer experience. AI robots in the construction industry can assist with tasks like bricklaying, welding, or concrete pouring, increasing efficiency and reducing the risk of accidents. Surveying drones combined with AI could map construction sites, monitor progress, and ensure compliance with safety regulations. Agriculture would surely benefit from AI robotics. Robotic harvesters could pick fruits and vegetables with precision, reducing accidents, costs, and waste while increasing productivity. Special drones could monitor crop health, apply fertilizers, and detect pests. Medical robots could assist in surgeries, providing tools to surgeons or performing precise movements in minimally invasive procedures. Lastly, the military would benefit greatly from the integration of AI robots. Soldiers must often carry heavy packs into battle and robots could act as “mules” in this regard, carrying heavy loads such as weaponry, ammunition, water, and food. Other robots fitted with weapons could be used for offensive purposes such as infiltrating enemy strongholds. This would definitely save lives and enhance the military capabilities of any force using these robots. With the further development of technology, we may also see humanoid robots that project emotion or understand feelings. We have yet to see this but there are hints that we could achieve this level of robotics mastery. Either way, it is clear that we will soon see robots taking over many mundane or difficult jobs, replacing humans and either making our lives easier or exponentially harder as we lose jobs and are forced to find new ways to earn a living. But it is without a doubt that AI humanoids will be a part of our not-too-distant future. Wouldn’t it be so useful if we had a robot that could do our laundry? We already have so many conveniences that people a century ago could only wish for. But we still don’t have an automated way of doing laundry, which includes dealing with the washer and dryer, ironing, folding clothes, sorting, and putting everything away in neat piles in their respective drawers or closets. Robotics are already widely used and have been integrated into factory floors and warehouses to improve efficiencies for years. But the robots we have today are largely limited to moving from point A to point B and tackling a handful of tasks. largely limited And we are familiar with these robots. We’ve all seen Amazon’s amazing robots that carry shelves of items throughout the warehouse, and Tesla’s red robots that assemble cars on the factory floor. These are great applications, but we can use AI and robots to move to the next level. When it comes to household cleaning, we already have robotic vacuum cleaners and mops, window cleaners, and lawnmowers. All we’re missing is a laundry robot and perhaps a humanoid that can cook a full meal or even just peel potatoes. Pet care is another great niche for robots. Imagine having AI-controlled pet feeders that ensure pets are fed on time, with the correct portions. Pet care robots could play with pets, clean litter boxes, or even take dogs for walks. Where AI humanoids can be especially helpful is in the realm of care for the elderly and infirm. Robots can assist with mobility, providing support for standing, walking, or moving between rooms. They can help with bathing, dressing, and medication management and can also provide emotional support, remind users of appointments, and monitor health vitals. Patient care robots in hospitals, clinics, and old age homes, could monitor vital signs, assist with patient mobility, and help in rehabilitation exercises. Security and surveillance is another area where robots could prove useful. Home security robots could patrol the premises, detect intruders, and alert homeowners or authorities in case of unusual activity. Smart door and window monitors integrated with humanoid robots could greet visitors, verify identity, and control access to the home. It is likely that in the coming decades, we will see the further development of robots that can assist in customer service and simple office jobs, the construction industry, agriculture, medicine and surgery, and of course the military. Customer service robots would be able to assist with reception, greeting customers and clients, answering questions, and directing them to the appropriate service or person. Service robots in restaurants and hotels could clean tables and floors, take orders, deliver food, or provide information, enhancing customer experience. AI robots in the construction industry can assist with tasks like bricklaying, welding, or concrete pouring, increasing efficiency and reducing the risk of accidents. Surveying drones combined with AI could map construction sites, monitor progress, and ensure compliance with safety regulations. Agriculture would surely benefit from AI robotics. Robotic harvesters could pick fruits and vegetables with precision, reducing accidents, costs, and waste while increasing productivity. Special drones could monitor crop health, apply fertilizers, and detect pests. Medical robots could assist in surgeries, providing tools to surgeons or performing precise movements in minimally invasive procedures. Lastly, the military would benefit greatly from the integration of AI robots. Soldiers must often carry heavy packs into battle and robots could act as “mules” in this regard, carrying heavy loads such as weaponry, ammunition, water, and food. Other robots fitted with weapons could be used for offensive purposes such as infiltrating enemy strongholds. This would definitely save lives and enhance the military capabilities of any force using these robots. With the further development of technology, we may also see humanoid robots that project emotion or understand feelings. We have yet to see this but there are hints that we could achieve this level of robotics mastery. Either way, it is clear that we will soon see robots taking over many mundane or difficult jobs, replacing humans and either making our lives easier or exponentially harder as we lose jobs and are forced to find new ways to earn a living. But it is without a doubt that AI humanoids will be a part of our not-too-distant future.

Are AI Humanoid’s Part of Our Future?

98% of Data Strategies Fail: Let's Fix It

The age of the Internet began in earnest in April 1993 when it was released into the public domain. Since then, it has grown to become an all-pervading force in our lives, impacting the way we communicate, carry out business and banking transactions, entertain ourselves, spend our leisure time, and even order our food and groceries. All of this was inconceivable not all that long ago, as would be well remembered by the Boomers, Generation X, and even the early Millennials. Though many of them view the pre-digital era that existed till the very early Nineties with rose-tinted nostalgia, one can generally agree that the World Wide Web has done far more good than bad. That the Internet changed life as mankind knew it for thousands of years in a fundamental manner is a fact. It ushered in the digital revolution, which is possibly even more path-breaking in its impact on mankind than the harnessing of fire and the invention of the wheel. We are, in fact, in the throes of a digital revolution that has only about begun. If things weren't interesting enough for us, even as we see our lives transform at a breakneck speed thanks to the changes ushered in by the entry of the Internet into our lives, we need to brace ourselves for another epochal transformation caused by the advent of AI or Artificial Intelligence technology. Where AI is different from the technologies preceding it is in the fact that it is already autonomous of human oversight to some extent in its decision-making and may one day become fully independent, which has led to alarm among many, who feat that its large-scale adoption might lead to a dystopian nightmare straight out of an HG Wells sci-fi thriller. People fear that AI could render everybody from writers and surgeons to lawyers and teachers redundant, leading to an existential crisis on an epic scale. For all the fear-mongering around AI, which admittedly appears justified even to the brightest of human minds, many of whom have expressed their concerns about its dangers, it does offer us myriad benefits across several very practical applications. From preventing fraud and analyzing risks in the investment sector to carrying out complex scientific research by perusing complex data and from automating repetitive mundane tasks to transforming healthcare with its varied abilities, including interpreting data to help identify diseases and assisting in drug development, AI can be of immense service to mankind. AI is still essentially at a very nascent stage and many years away from a time where it is unilaterally and independently able to make major life-and-death decisions of its own volition. That is not to say that it never may, which is why governments, lawmakers, technologists, scientists and philosophers must utilise the time we have to regulate the technology and decide on the ethical framework within which it must be deployed. That the AI age has dawned upon us is a fact, but whether it will turn out to be all pervasive and more is something that we don't yet have the answer to. The Dot Com bubble, for instance, had nearly sunk the Internet revolution until the proponents of the technology proved its usefulness in many other ways. Who knows when the current rash of AI startups pitching a gazillion AI products might go belly up, making people wary of the technology to the extent that they may want to dump it? Alternatively, it may see its ups and downs and finally become the new normal in a much better-regulated and rationalized manner. In any case, we are in for an exciting time ahead. The age of the Internet began in earnest in April 1993 when it was released into the public domain. Since then, it has grown to become an all-pervading force in our lives, impacting the way we communicate, carry out business and banking transactions, entertain ourselves, spend our leisure time, and even order our food and groceries. All of this was inconceivable not all that long ago, as would be well remembered by the Boomers, Generation X, and even the early Millennials. Though many of them view the pre-digital era that existed till the very early Nineties with rose-tinted nostalgia, one can generally agree that the World Wide Web has done far more good than bad. That the Internet changed life as mankind knew it for thousands of years in a fundamental manner is a fact. It ushered in the digital revolution, which is possibly even more path-breaking in its impact on mankind than the harnessing of fire and the invention of the wheel. We are, in fact, in the throes of a digital revolution that has only about begun. If things weren't interesting enough for us, even as we see our lives transform at a breakneck speed thanks to the changes ushered in by the entry of the Internet into our lives, we need to brace ourselves for another epochal transformation caused by the advent of AI or Artificial Intelligence technology. Where AI is different from the technologies preceding it is in the fact that it is already autonomous of human oversight to some extent in its decision-making and may one day become fully independent, which has led to alarm among many, who feat that its large-scale adoption might lead to a dystopian nightmare straight out of an HG Wells sci-fi thriller. People fear that AI could render everybody from writers and surgeons to lawyers and teachers redundant, leading to an existential crisis on an epic scale. For all the fear-mongering around AI, which admittedly appears justified even to the brightest of human minds, many of whom have expressed their concerns about its dangers, it does offer us myriad benefits across several very practical applications. From preventing fraud and analyzing risks in the investment sector to carrying out complex scientific research by perusing complex data and from automating repetitive mundane tasks to transforming healthcare with its varied abilities, including interpreting data to help identify diseases and assisting in drug development, AI can be of immense service to mankind. AI is still essentially at a very nascent stage and many years away from a time where it is unilaterally and independently able to make major life-and-death decisions of its own volition. That is not to say that it never may, which is why governments, lawmakers, technologists, scientists and philosophers must utilise the time we have to regulate the technology and decide on the ethical framework within which it must be deployed. That the AI age has dawned upon us is a fact, but whether it will turn out to be all pervasive and more is something that we don't yet have the answer to. The Dot Com bubble, for instance, had nearly sunk the Internet revolution until the proponents of the technology proved its usefulness in many other ways. Who knows when the current rash of AI startups pitching a gazillion AI products might go belly up, making people wary of the technology to the extent that they may want to dump it? Alternatively, it may see its ups and downs and finally become the new normal in a much better-regulated and rationalized manner. In any case, we are in for an exciting time ahead.

From the Age of the Internet to the Age of AI

Is it strange to think a robot could operate on you one day? Although the healthcare industry is famous for being a late adopter of the latest technology, it’s quickly picked up robotics. The tech advances as professionals explore it, meaning there are plenty of use cases for machine-assisted remote surgeries. Can robotic surgery be done remotely? The short answer is yes — surgeons can use robots to operate on patients even if they’re not in the same place. Remote surgery — also known as telesurgery — uses wireless networking technology and live imaging to connect them to the operating room, regardless of location. Nearly 40% of people believe these machines can perform surgeries — and they’re right. You’re living in a world where tech has advanced to the point where you don’t have to be in the same room as a doctor to receive care. What technology is used in robotic surgery? While the specific tech surgery robots use depends on the type of operation, most share the same basic features. Many use fifth-generation (5G) wireless networking to establish connections. Operators need a console — an interactive interface — to do anything remotely. Most machines have at least one articulated arm to grasp, cut, or guide the operation. They also use a high-definition, 360-degree camera, and live imaging tech to feed visual information to the operator — how else would surgeons perform remote surgery? Some of the latest versions use haptic feedback tech, enabling them to mimic the operators’ movements on the console. Others use artificial intelligence. Considering a convolution neural network model can identify brain tumors with 97.4% accuracy, it’s a good tool. Examples of robots performing medical operations You might be surprised to find out dozens of surgery robots already exist. The da Vinci robot The da Vinci robot got its name because its creators consider the term “robotic-assisted surgery” misleading, saying people perform operations, not the machines. In this context, the callback to Leonardo da Vinci — the famous artist, engineer and sculptor — makes sense. This machine has performed millions of anastomoses — connecting two parts of a blood vessel — while recording data on how surgeons use their instruments to complete their tasks. This way, it can tell them which techniques and wrist positions improve patient outcomes the most. The spacefaring robot Engineers from the University of Nebraska-Lincoln (UNL) did what nobody ever has — they sent a surgical robot to the International Space Station. Weighing only two pounds, it’s 1,000 times lighter than any other telerobotic system. Surgeons on Earth will use the robot’s camera and two articulated arms to perform simulated surgery in space. As impressive as this sounds, there’s no telling how it’ll go. Even though the launch was in January 2024, UNL won’t publish results until 2025 at the earliest. The augmented reality robot In 2020, two doctors performed a lifesaving surgery on a person with cancer. It doesn’t sound out of the ordinary until you learn they never touched him — and were thousands of miles apart. The patient was immunocompromised, so telesurgery was the only safe option during COVID. One surgeon stood six feet from the patient, using the machine’s four articulated arms to insert a camera tube into the problem area. Meanwhile, the other sat in their living room in a pajama robe, using an augmented reality pointer to guide the step-by-step operation. The joystick robot Massachusetts Institute of Technology (MIT) engineers developed a telerobotic system for treating victims of strokes. The operator uses a modified joystick and a live video feed to control an articulated arm, adjusting it with magnets to guide a soft, thin magnetic wire through arteries and vessels to remove blood clots that cause strokes. The goal is to treat patients during a brief window of opportunity where treatment can preserve brain function and save lives. The advantages of robotic medical operations Plenty of people live in healthcare deserts — areas where there aren’t enough medical resources, staff, or facilities to go around. Statistically, you’re probably one of them. In the U.S., over 80% of counties are considered healthcare deserts, forcing residents to deal with long waits and high costs. Robotic-assisted surgery could eliminate this problem by bringing medical professionals wherever they’re needed. For example, a neurovascular surgeon could remotely treat a stroke victim in Atlanta from their office in Chicago. There’s no longer a need for travel, either. If you’ve ever visited a specialist, you probably drove out of your way to see them. Some people with chronic illnesses even move to improve their access to care. Now that telesurgery exists, you may just have to visit your local hospital. Another massive benefit involves patient outcomes — these machines are literally saving lives. They reduce hand tremors, enable early intervention, and are highly precise, lowering the risk of infection, blood loss, and complications. Past robotic-assisted surgeries prove medical professionals can provide care without experiencing software glitches or connection delays, meaning this tech is just as safe as being operated on by a robot-less human. Ways robotics can be used for remote surgery There are three main ways you could use robots for remote surgeries. Articulated robots Articulated robots are among the most common in the medical field because they can do most things human hands can — they can insert tubes, make incisions, or administer medication. Operators can use them to carry out tasks remotely, lending their expertise to operations they otherwise would’ve never been a part of. 2. Autonomous robots Although the healthcare industry is wary of adopting fully autonomous robotics, professionals and researchers are testing the tech. The idea is that a human will remotely oversee the operation while the machine carries out its tasks. Collaborative robots Medical professionals often call on their colleagues or well-known individuals in their field for help — even doctors can’t be experts on everything. In these situations, they usually want guidance on where to make incisions or how to perform complex operations. Using collaborative robotics, their assistant can help them regardless of their location. Will robots replace surgeons in the future? It’s highly unlikely these machines will replace surgeons in the future. Although autonomous versions exist, you can probably agree most people won’t be comfortable being operated on by a machine without some level of human involvement. For now, these inventions are purely assistants. Sources: Kaspersky’s The Future of Jobs report Deep Learning for Tumor Classification Intuitive’s Robotic-Assisted Surgery Technology The Smithsonian’s Report on the ISS Robot Wired Report on Robotic-Assisted Surgery MIT’s Report on the Joystick Robot The GoodRX Report on Healthcare Deserts Telesurgey and Robotics: An Improve and Efficient Era Is it strange to think a robot could operate on you one day? Although the healthcare industry is famous for being a late adopter of the latest technology, it’s quickly picked up robotics. The tech advances as professionals explore it, meaning there are plenty of use cases for machine-assisted remote surgeries. Can robotic surgery be done remotely? Can robotic surgery be done remotely? The short answer is yes — surgeons can use robots to operate on patients even if they’re not in the same place. Remote surgery — also known as telesurgery — uses wireless networking technology and live imaging to connect them to the operating room, regardless of location. Nearly 40% of people believe these machines can perform surgeries — and they’re right. You’re living in a world where tech has advanced to the point where you don’t have to be in the same room as a doctor to receive care. Nearly 40% of people Nearly 40% of people What technology is used in robotic surgery? What technology is used in robotic surgery? While the specific tech surgery robots use depends on the type of operation, most share the same basic features. Many use fifth-generation (5G) wireless networking to establish connections. Operators need a console — an interactive interface — to do anything remotely. Most machines have at least one articulated arm to grasp, cut, or guide the operation. They also use a high-definition, 360-degree camera, and live imaging tech to feed visual information to the operator — how else would surgeons perform remote surgery? Some of the latest versions use haptic feedback tech, enabling them to mimic the operators’ movements on the console. Others use artificial intelligence. Considering a convolution neural network model can identify brain tumors with 97.4% accuracy , it’s a good tool. identify brain tumors with 97.4% accuracy identify brain tumors with 97.4% accuracy Examples of robots performing medical operations Examples of robots performing medical operations You might be surprised to find out dozens of surgery robots already exist. The da Vinci robot The da Vinci robot The da Vinci robot got its name because its creators consider the term “robotic-assisted surgery” misleading, saying people perform operations, not the machines. In this context, the callback to Leonardo da Vinci — the famous artist, engineer and sculptor — makes sense. This machine has performed millions of anastomoses — connecting two parts of a blood vessel — while recording data on how surgeons use their instruments to complete their tasks. This way, it can tell them which techniques and wrist positions improve patient outcomes the most. has performed millions of anastomoses has performed millions of anastomoses The spacefaring robot The spacefaring robot Engineers from the University of Nebraska-Lincoln (UNL) did what nobody ever has — they sent a surgical robot to the International Space Station. Weighing only two pounds, it’s 1,000 times lighter than any other telerobotic system. it’s 1,000 times lighter it’s 1,000 times lighter Surgeons on Earth will use the robot’s camera and two articulated arms to perform simulated surgery in space. As impressive as this sounds, there’s no telling how it’ll go. Even though the launch was in January 2024, UNL won’t publish results until 2025 at the earliest. The augmented reality robot The augmented reality robot In 2020, two doctors performed a lifesaving surgery on a person with cancer. It doesn’t sound out of the ordinary until you learn they never touched him — and were thousands of miles apart. The patient was immunocompromised, so telesurgery was the only safe option during COVID. performed a lifesaving surgery performed a lifesaving surgery One surgeon stood six feet from the patient, using the machine’s four articulated arms to insert a camera tube into the problem area. Meanwhile, the other sat in their living room in a pajama robe, using an augmented reality pointer to guide the step-by-step operation. The joystick robot The joystick robot Massachusetts Institute of Technology (MIT) engineers developed a telerobotic system for treating victims of strokes. The operator uses a modified joystick and a live video feed to control an articulated arm, adjusting it with magnets to guide a soft, thin magnetic wire through arteries and vessels to remove blood clots that cause strokes. The goal is to treat patients during a brief window of opportunity where treatment can preserve brain function and save lives. uses a modified joystick uses a modified joystick The advantages of robotic medical operations The advantages of robotic medical operations Plenty of people live in healthcare deserts — areas where there aren’t enough medical resources, staff, or facilities to go around. Statistically, you’re probably one of them. In the U.S., over 80% of counties are considered healthcare deserts, forcing residents to deal with long waits and high costs. over 80% of counties over 80% of counties Robotic-assisted surgery could eliminate this problem by bringing medical professionals wherever they’re needed. For example, a neurovascular surgeon could remotely treat a stroke victim in Atlanta from their office in Chicago. There’s no longer a need for travel, either. If you’ve ever visited a specialist, you probably drove out of your way to see them. Some people with chronic illnesses even move to improve their access to care. Now that telesurgery exists, you may just have to visit your local hospital. Another massive benefit involves patient outcomes — these machines are literally saving lives. They reduce hand tremors, enable early intervention, and are highly precise, lowering the risk of infection, blood loss, and complications. Past robotic-assisted surgeries prove medical professionals can provide care without experiencing software glitches or connection delays, meaning this tech is just as safe as being operated on by a robot-less human. without experiencing software glitches without experiencing software glitches Ways robotics can be used for remote surgery Ways robotics can be used for remote surgery There are three main ways you could use robots for remote surgeries. Articulated robots Articulated robots Articulated robots Articulated robots are among the most common in the medical field because they can do most things human hands can — they can insert tubes, make incisions, or administer medication. Operators can use them to carry out tasks remotely, lending their expertise to operations they otherwise would’ve never been a part of. 2. Autonomous robots 2. Autonomous robots Although the healthcare industry is wary of adopting fully autonomous robotics, professionals and researchers are testing the tech. The idea is that a human will remotely oversee the operation while the machine carries out its tasks. Collaborative robots Collaborative robots Collaborative robots Medical professionals often call on their colleagues or well-known individuals in their field for help — even doctors can’t be experts on everything. In these situations, they usually want guidance on where to make incisions or how to perform complex operations. Using collaborative robotics, their assistant can help them regardless of their location. Will robots replace surgeons in the future? Will robots replace surgeons in the future? It’s highly unlikely these machines will replace surgeons in the future. Although autonomous versions exist, you can probably agree most people won’t be comfortable being operated on by a machine without some level of human involvement. For now, these inventions are purely assistants. Sources: Kaspersky’s The Future of Jobs report Kaspersky’s The Future of Jobs report Kaspersky’s The Future of Jobs report Deep Learning for Tumor Classification Deep Learning for Tumor Classification Deep Learning for Tumor Classification Intuitive’s Robotic-Assisted Surgery Technology Intuitive’s Robotic-Assisted Surgery Technology Intuitive’s Robotic-Assisted Surgery Technology The Smithsonian’s Report on the ISS Robot The Smithsonian’s Report on the ISS Robot The Smithsonian’s Report on the ISS Robot Wired Report on Robotic-Assisted Surgery Wired Report on Robotic-Assisted Surgery Wired Report on Robotic-Assisted Surgery MIT’s Report on the Joystick Robot MIT’s Report on the Joystick Robot MIT’s Report on the Joystick Robot The GoodRX Report on Healthcare Deserts The GoodRX Report on Healthcare Deserts The GoodRX Report on Healthcare Deserts Telesurgey and Robotics: An Improve and Efficient Era Telesurgey and Robotics: An Improve and Efficient Era Telesurgey and Robotics: An Improve and Efficient Era

How Can Robotics Be Implemented for Remote Surgery?

Hello, I’m Malena Carollo, and I’m an investigative reporter here at The Markup. I write stories about broken systems that deeply affect people’s lives. In March, we published a two-part investigation into the algorithm that determines who gets a life-saving liver transplant. The algorithm was changed three years ago to allow donated livers to travel farther than ever before. Proponents said the change was meant to get the sickest patients a liver no matter where they lived. But those making the new policy dismissed surgeons, advocates, and academics around the country who worried that it would hurt rural areas and the poor while causing more livers to be wasted. My reporting partner Ben Tanen and I found these concerns were valid: the change entrenched long-standing inequities, taking livers away from areas that already struggle with access to health care and sending them to some states that do a poor job of collecting donated organs locally. We also found that the change didn’t happen organically: it was engineered by those whose states would benefit from it. Since our story was published, the U.S. Department of Health and Human Services (HHS) said it would break up the contract that has given one contractor a nearly four-decade hold on the transplant system. And as of this week, Congress is now considering two reform bills to advance this effort. Here’s what you need to know: 1.  The policy produced clear winners and losers Seven states and Puerto Rico lost transplants under the policy. All but one of these losing states were in the South and Midwest. Two—Alabama and Kansas—each have just one hospital within their borders where adults can get a liver transplant. Alabama saw a 44 percent drop in transplants under the policy, while Kansas dropped by 38 percent. The number of losing states is even larger when you look at how many transplants each did compared to how many organs were donated there: 19 states and Puerto Rico saw a decline in transplants as a percentage of donations. Those behind the change said their goal was to save more lives, not to create disparities. But the negatively affected states have lower rates of insured people, fewer transplant hospitals per square mile, and lower median household incomes than those that benefited from the change. The top beneficiary of the shift? New York, which has one of the lowest rates of end-stage liver disease in the country. California also significantly benefited from the change. 2. One in 10 donated livers were discarded during the policy In 2021, the policy change coincided with the highest number of livers discarded in nearly a decade—949. Experts said increased travel time contributed to this. When a donated liver gets matched to someone waiting for a transplant, the recipient’s surgeon may still reject it—maybe there was an unforeseen issue, or perhaps they got a better liver from elsewhere. The rejected liver would go to the next matching patient. But because of how widely livers are now shared, the next person may be quite far away, requiring more travel. And while the liver is traveling, it is also deteriorating: they can only be outside the body for about 12 hours. The issue of discarded organs is particularly important when it comes to livers because a transplant is the only treatment option for end-stage liver disease; there is no dialysis for livers. 3. Nonprofits in charge of the nation’s organ supply are failing—and so are their regulators Battles over who gets a donated liver are a symptom of a much larger problem: there aren’t enough organs to go around. The federal government contracts 56 nonprofits to convince families to donate their dying loved ones’ organs. They are overwhelmingly underperforming at their jobs: 42 percent of all “organ procurement organizations” were in the lowest performance tier in the annual rankings released earlier this month. What those rankings don’t capture are the vast differences in how these organizations perform their crucial work. No regulator looks at how they do their jobs, despite two of the regulators collecting data that would allow them to do so. The significant differences in how each organization performs their duties directly affect how many organs are recovered. 4. Private interests orchestrated the nationwide change The differences in how organ procurement organizations operate contributed to the liver policy change. A lawsuit from six patients in New York, California, and Massachusetts demanded that livers be shared more broadly, and insiders overseeing the nation’s transplant system used this lawsuit to push forth the policy change after other pressure tactics didn’t work. We found that two of the worst-performing organ procurement organizations in the country and a New York hospital trade association formed a lobbying group that recruited the patients who filed the lawsuit, while the trade group bankrolled it. Hospitals in New York were long frustrated with their local organ procurement organization’s performance. The new policy brought many more livers to their areas without having to collect more locally. The New York organ procurement organization, LiveOnNY, was so poor at its job that its federal contract was nearly pulled twice. You can read the audit The Markup obtained outlining LiveOnNY’s widespread deficiencies here. And here’s how those groups and insiders forced the policy change. 5. The federal government wants the national transplant system to change At the helm of the country’s transplant system is the United Network for Organ Sharing (UNOS). It and the organ procurement organizations are the subject of multiple congressional investigations into their performance. The day after we published our investigation, HHS announced a plan to break up UNOS’ longstanding hold on the transplant system, slicing off different portions of the work into separate contracts. Two bills, one in the House and one in the Senate, have since been filed that would give HHS the support to do so. There are scant details available about how exactly the contract would actually be broken up, however. And even if policymaking work were taken from UNOS, there is no guarantee that the liver policy would be revised. Thanks for reading our work, and always feel free to reach me with tips or feedback at malena@themarkup.org or 813-591-0688. Best, Malena Carollo Investigative Reporter The Markup P.S. Markup reporters Aaron Sankin and Leon Yin recently published a guide to investigating the digital divide. If you’d like to learn how to use it, join them for a demo this Tuesday, May 23, at 2:00 p.m. ET. Just sign up at mrkup.org/byod-rsvp. By Malena Carollo Photo by JAFAR AHMED on Unsplash Hello, I’m Malena Carollo, and I’m an investigative reporter here at The Markup. I write stories about broken systems that deeply affect people’s lives. In March, we published a two-part investigation into the algorithm that determines who gets a life-saving liver transplant. investigation The algorithm was changed three years ago to allow donated livers to travel farther than ever before. Proponents said the change was meant to get the sickest patients a liver no matter where they lived. But those making the new policy dismissed surgeons, advocates, and academics around the country who worried that it would hurt rural areas and the poor while causing more livers to be wasted. My reporting partner Ben Tanen and I found these concerns were valid: the change entrenched long-standing inequities, taking livers away from areas that already struggle with access to health care and sending them to some states that do a poor job of collecting donated organs locally. We also found that the change didn’t happen organically: it was engineered by those whose states would benefit from it. was engineered Since our story was published, the U.S. Department of Health and Human Services (HHS) said it would break up the contract that has given one contractor a nearly four-decade hold on the transplant system. And as of this week, Congress is now considering two reform bills to advance this effort. Here’s what you need to know: 1.  The policy produced clear winners and losers Seven states and Puerto Rico lost transplants under the policy . All but one of these losing states were in the South and Midwest. Two—Alabama and Kansas—each have just one hospital within their borders where adults can get a liver transplant. Alabama saw a 44 percent drop in transplants under the policy, while Kansas dropped by 38 percent. The number of losing states is even larger when you look at how many transplants each did compared to how many organs were donated there: 19 states and Puerto Rico saw a decline in transplants as a percentage of donations. Seven states and Puerto Rico lost transplants under the policy Those behind the change said their goal was to save more lives, not to create disparities. But the negatively affected states have lower rates of insured people, fewer transplant hospitals per square mile, and lower median household incomes than those that benefited from the change. The top beneficiary of the shift? New York, which has one of the lowest rates of end-stage liver disease in the country. California also significantly benefited from the change. 2. One in 10 donated livers were discarded during the policy In 2021, the policy change coincided with the highest number of livers discarded in nearly a decade—949. Experts said increased travel time contributed to this. When a donated liver gets matched to someone waiting for a transplant, the recipient’s surgeon may still reject it—maybe there was an unforeseen issue, or perhaps they got a better liver from elsewhere. The rejected liver would go to the next matching patient. But because of how widely livers are now shared, the next person may be quite far away, requiring more travel. And while the liver is traveling, it is also deteriorating: they can only be outside the body for about 12 hours. The issue of discarded organs is particularly important when it comes to livers because a transplant is the only treatment option for end-stage liver disease; there is no dialysis for livers. 3. Nonprofits in charge of the nation’s organ supply are failing—and so are their regulators Battles over who gets a donated liver are a symptom of a much larger problem: there aren’t enough organs to go around. The federal government contracts 56 nonprofits to convince families to donate their dying loved ones’ organs. They are overwhelmingly underperforming at their jobs: 42 percent of all “organ procurement organizations” were in the lowest performance tier in the annual rankings released earlier this month. What those rankings don’t capture are the vast differences in how these organizations perform their crucial work . No regulator looks at how they do their jobs, despite two of the regulators collecting data that would allow them to do so. The significant differences in how each organization performs their duties directly affect how many organs are recovered. vast differences in how these organizations perform their crucial work 4. Private interests orchestrated the nationwide change The differences in how organ procurement organizations operate contributed to the liver policy change. A lawsuit from six patients in New York, California, and Massachusetts demanded that livers be shared more broadly, and insiders overseeing the nation’s transplant system used this lawsuit to push forth the policy change after other pressure tactics didn’t work. We found that two of the worst-performing organ procurement organizations in the country and a New York hospital trade association formed a lobbying group that recruited the patients who filed the lawsuit, while the trade group bankrolled it. Hospitals in New York were long frustrated with their local organ procurement organization’s performance. The new policy brought many more livers to their areas without having to collect more locally. The New York organ procurement organization, LiveOnNY, was so poor at its job that its federal contract was nearly pulled twice . You can read the audit The Markup obtained outlining LiveOnNY’s widespread deficiencies here . And here’s how those groups and insiders forced the policy change . federal contract was nearly pulled twice outlining LiveOnNY’s widespread deficiencies here here’s how those groups and insiders forced the policy change 5. The federal government wants the national transplant system to change At the helm of the country’s transplant system is the United Network for Organ Sharing (UNOS). It and the organ procurement organizations are the subject of multiple congressional investigations into their performance. The day after we published our investigation, HHS announced a plan to break up UNOS’ longstanding hold on the transplant system , slicing off different portions of the work into separate contracts. Two bills, one in the House and one in the Senate , have since been filed that would give HHS the support to do so. announced a plan to break up UNOS’ longstanding hold on the transplant system House Senate There are scant details available about how exactly the contract would actually be broken up, however. And even if policymaking work were taken from UNOS, there is no guarantee that the liver policy would be revised. Thanks for reading our work, and always feel free to reach me with tips or feedback at malena@themarkup.org or 813-591-0688. malena@themarkup.org Best, Malena Carollo Investigative Reporter The Markup P.S. Markup reporters Aaron Sankin and Leon Yin recently published a guide to investigating the digital divide . If you’d like to learn how to use it, join them for a demo this Tuesday, May 23, at 2:00 p.m. ET. Just sign up at mrkup.org/byod-rsvp . a guide to investigating the digital divide mrkup.org/byod-rsvp By Malena Carollo By Malena Carollo Malena Carollo Photo by JAFAR AHMED on Unsplash Photo by JAFAR AHMED on Unsplash JAFAR AHMED Unsplash

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