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1952 witnessed the world’s first computer that could learn while it was running. It was a game of checkers developed by Arthur Samuel. It has barely been half a century since then and we’re already having a conversation about whether we should commercialize self-driving cars or not. Machine Learning gave birth to some of these great advancements in technology and we’re going to dive deep into everything it can do to make our lives much easier than ever before. What can Machine Learning do Today? A lot of things, really. Machine Learning is being termed as a bridge which will help us leap into the future, transforming almost every single industry along the way. But before you get overwhelmed, let’s only focus on things that are currently popular among the business owners:- = Analyze Sales Data At present, business organizations can use analytical data so as to figure out the most valuable customers, i.e. the potential consumers that are most likely to convert into actual consumers. These companies know what consumes are browsing on social media platforms and other websites, and they target just the right ads to them. Not only can Machine Learning speed up the process of data collection, but can also analyze the data and present it to you in an understandable manner. There are many tools for sales analytics that are available online, all of which are able to generate insights from sales data, trends, and metrics to set targets and forecast future sales performance. These Machine Learning tools will help you develop strategies for improving your sales performance in both the short- and long-term. You may read more about the sales metrics where you can find the right tools for getting started with the sales analytics. = Real-Time Mobile Personalisation According to CIO Dive , up to 70% of contribution to web traffic comes from mobile devices. A recent suggests that 96% of active Facebook user accounts were accessed via smartphones and tablets. These numbers indicate a great degree of consumer inclination towards any form of mobile devices. Mobile marketers and App Developers are looking for a way to leverage it so that they can develop a highly personalized mobile experience, which is where Machine Learning comes into play. Flybits is one company that helps other companies deliver real-time mobile app personalization. This allows you to have instant cloud access to internal and external data and enables you to develop personalized mobile channels. = Image source web design stat Detect Frauds Did you know that 2017 was a record year for the rise in e-commerce fraud? The suggests that e-commerce frauds in the US rose by 30% in 2017, compared to 2016. Considering that e-commerce sales only rose by 16%, it’s really unfortunate that these frauds rose by almost twice as much as the sales did, which is an alarming statistic. Many e-commerce platforms like Amazon and Paypal have their own Machine Learning tools in order to prevent frauds ranging from abuse of promo codes to other payment-related frauds. You may read more about the kind of frauds that Machine Learning can protect you from. The benefits of Machine Learning outrank the downsides in so many ways that can save your business as well as help you gain more consumer trust. = Image source data released by Experian Personalized Recommendations Who doesn’t like scrolling through the home page of an e-commerce website just to have a look at the product recommendations section for them? Well, nowadays, consumers even want to get notified on their smartphones about the latest offers available on their favourite product. Even if many of these consumers are indifferent, it skyrockets your chances of conversion since they’re being constantly reminded of their favourite products. Amazon and Alibaba were one of the first e-commerce ventures to introduce an algorithm that improves the product recommendation process. John Bates, Senior Product Manager at Adobe, believes that by leveraging the power of Machine Learning and predictive analytics, brands can look beyond what consumers are looking for, and have them connect those dots for themselves about what they’re likely to want. = Differential Pricing Brace yourself, for the negotiation culture has finally reached the internet. I bet most of you didn’t know that the travel and retail industries are the masters of dynamic pricing, i.e. different prices for different consumers. This is another way for companies to make more money by changing the prices in accordance with the level of demand. If you didn’t guess it already, there aren’t any humans sitting behind the scenes and changing these prices in real-time, it is Machine Learning that is changing the game for these industries. Both Uber and Airbnb use Machine Learning to create different prices for each person who is using their services. Uber also uses it for minimizing the waiting time and to optimize the ride-sharing aspect of its services. Applications of Machine Learning on Different Industries , the tech giant, believes that current AI technology can boost your business’ productivity by up to 40%. Even , a popular Research and Advisory firm, predicts that by 2020, 85% of the customer interactions will be handled without a human. Accenture Gartner These machines have really profound algorithms that are designed to process a huge amount of information and to make decisions based on logic, enabling the machines to learn and to complete the task without any further programming. It goes without saying that Machine Learning has widened its spectrum and is taking over many industries. Let’s talk about some of these industries where Machine Learning has instantaneously grown popular:- = Machine Learning Application in Transportation Industry You know how much we all hate sitting in our vehicles, waiting for the lights to turn green, especially when there aren’t any vehicles coming in from the opposite side, but the traffic lights aren’t that smart, or are they? Well, Artificial Intelligence and Machine Learning algorithms seem to be taking over the streets of many countries and they’re efficiently able to predict, monitor, and manage the traffic. = Traffic Regulation Just in case you didn’t get the memo, Self-Driving Cars are the future of transportation, and companies like Tesla and Google have already started doing their test runs. In fact, Tesla automobiles already have an autopilot mode that gives their cars semi-autonomous abilities. Once activated, the autopilot mode can regulate the car’s speed, change lanes, and park it without the driver’s assistance. = Automated Driving Healthcare Industry Machine Learning Application in The diagnostics running through Artificial Intelligence collects patient’s data to diagnose and suggest possible ways to prevent/cure the disease. Bear in mind that it’s only able to project a path with existing medication and not come up with new solutions. It just has the ability to detect medical problems much faster than a human can, which is pretty huge in itself when it comes to saving lives. = Diagnostics Dr. Ziad Obermeyer, an assistant professor at Harvard Medical School, did an interview with , where he made a bold claim saying that in 20 years, radiologists might look more like cyborgs, supervising algorithms while reading thousands of studies per minute. Don’t know how true that comes out to be but companies like DeepMind and UCLH are currently working on applying Machine Learning to help speed up the segmentation process and increase accuracy in radiotherapy planning. = Radiology and Radiotherapy Stat News Finance Industry Machine Learning Application in If you’re a finance news reader or are into finance industry yourself, then you may have heard of Robo-Advisors. Robo-advisors is an online application that provides automated financial guidance. They provide portfolio management services that use Machine Learning algorithms and statistics to automatically establish and manage the investment portfolio of a client. It not only simplifies the investment process but is also cheaper than consulting a human financial advisor. = High-frequency trading, commonly termed as HFT, is a program trading platform that uses heavy duty computers to perform hundreds of thousands of trades at speeds way beyond human comprehension. According to , during 2009-2010, about 60% to 70% of U.S. trading was attributed to HFT. It means that most investment banks, pension funds, and mutual funds, make use of HFT. It uses complex algorithms that analyze multiple markets and execute orders based on market conditions, which gives an edge to master investors as they are able to benefit from minute price differences that exist only for a fraction of a second. Image Source = Portfolio Management High-Frequency Trading (HFT) Investopedia Also Read: Tips to hire PHP developer for your business Machine Learning Application in Agriculture Industry You know that these state-of-the-art approaches have come so far when we are able to predict crop yields based on historical data, and optimize productivity by multi-parametric approach, i.e. by analyzing the crops, weather conditions and even economic conditions to make the most of the yield. The same technology is being used to understand the crop quality, identify diseases and detect weeds. = Field conditions are important for the quality and quantity of the yield, which further facilitates the need for soil and water management. Machine Learning is able to study soil moisture and temperature to understand the dynamics of ecosystems and other hindrances in agriculture. Machine Learning based apps are also connected with daily, weekly and monthly estimates of evapotranspiration, enabling better use of irrigation systems. = Crop Management Field Conditions Management Machine Learning Application in Education Industry Just like you have personalized recommendations on YouTube, the education industry is bringing about changes that will only show specific content to you based on your previous courses you took. We’re talking about the learning platforms that generally offer a systemized academic lessons like Udemy, Teachable, WizIQ, and many more. These are the platforms that are capitalizing on Machine Learning for the same, with the help of which the sellers, i.e. the online coaches, are able to target the right market. = Personalized Learning Have you ever heard of the saying, “When one door closes, another door opens”? Well, times have changed, and so have the problems. We have way too many doors open for us. The problem of the present day and age is to find the right door, on top of which comes a lot of scheduling and management of related routines. which is time consuming, and that’s exactly where Machine Learning comes into play. It makes educators more efficient by completing tasks such as classroom management and scheduling, meanwhile, humans can go focus on tasks these machines can’t perform. = Increasing Efficiency Additional Information Is Machine Learning Different From Artificial Intelligence? Before you read this article further, you must realize that Artificial Intelligence and Machine Learning are quite interrelated and both of these terms will often be used together, which means, whenever I talk about Machine Learning, Artificial Intelligence will most likely be a part of that conversation. Cool? Awesome. Let’s talk about how they seem to be different:- is a technology that makes machines capable of performing tasks in ways that are "intelligent." These machines are programmed to perform more than just a single, repetitive motion. In fact, they have the ability to adapt to different situations. Artificial Intelligence is technically a branch of Artificial Intelligence, but it's a lot more specific than the overall concept. Machine learning is based on the idea that we can build machines to process data, with the help of which they can learn on their own, without requiring constant supervision. Machine learning Understanding with a Recent Example:- Elon Musk’s company Neuralink has created a chip that people can get transplanted inside their heads, which will offer them the ability to not just think really fast, but to control computer devices with just their thoughts, and even interact with AI directly and way more efficiently. But the chip is so small that no human can possibly perform that operation. Keeping that in mind, they developed a neurosurgical robot that has a job to attach very precise threads into our brain. So, the Robot that’s performing the task autonomously is because of Machine Learning but its ability to learn more and adapt accordingly is because of Artificial Intelligence. Let's Wrap Up There may be numerous applications of this technology, and surprisingly, it hasn’t even begun yet, but having made a leap that huge, that too, in such a short time span opens up so many possibilities in the present business models.  No doubt, the rise of machine learning also brings the demand of mobile applications; therefore most of the businesses and agencies & for integrating machine learning features in them. hire Android developers hire iPhone app developers Please share your feedback about this article in the comment section. If you want to add some more information, you can do this in the comment section. And, don't forget to share the article with your friends and family members.

Machine Learning Applications Across Different Industries

Despite the significant advances made in computer science in the last decades, which are daily improving medical services and research, patient care has always been about human-to-human interaction and empathy. However, through artificial intelligence, medical professionals can obtain more accurate patient information and make better decisions. The application of sophisticated mathematical algorithms is going far beyond the collection of information. Artificial intelligence now has the ability to learn, distinguish patterns and find substantive inconsistencies usually invisible to the human eye. IBM Watson, the supercomputer, is one of the best examples of the practical application of AI in healthcare. The program helps researchers and show genetic abnormalities. to determine suitable treatments for patients that deal with cancer IBM’s Avicenna, a program assisting radiologists, is another software development worth mentioning. It helps and choose the most efficient option of treatment. to examine medical images utilizing complex image-processing algorithms Distinguishing levels and types of AI The levels of AI an early evolution stage, at which smart systems are programmed to perform predefined functions Artificial Narrow Intelligence (ANI) – a stage, which assumes computer programs will sometime be able to think and make their own conclusions Artificial General Intelligence (AGI) – a hypothesis about robots being able to surpass human beings in their intellectual abilities Artificial Superintelligence – The types of AI Reactive machines Reactive machines are able to perform simple tasks. They can’t learn from past experiences. Limited memory Limited memory refers to trained machines, which can process information and make accurate predictions. Such programs can provide nursing and surgical assistance, medical diagnosis, predictive analysis, and clinical decision support. The theory of mind Such technologies are not there yet. But in the future, smart machines might understand human behavior. Self-aware machines Self-aware machines are just a hypothesis. If healthcare AI companies will ever be able to reach such progress, smart machines might consider themselves as living organisms with thoughts and feelings. The branches of AI Machine and deep learning in healthcare Here’s a quick guide on how to distinguish between ML and DL: Machine learning allows computers to predict future outcomes Deep learning allows machines to think using algorithms structured like a human brain ML requires less programming DL requires less surveillance Machine learning is used to forecast future events, identify patterns, recognize photos and videos, develop chatbots, and more Deep learning is used to discover new drugs, analyze genomes and mitigate health risks Expert systems Expert systems are simple computer algorithms emulating the decision-making ability of a human specialist. Despite the many disadvantages, expert systems are still being utilized in the healthcare industry for clinical decision support. Natural language processing is a popular branch of AI, which combines computer science and linguistics. It can be used to classify clinical documentation and analyze clinical notes. Natural language processing (NLP) Robotics This branch of AI deals with the development of robots. It is being used to assist surgeons, caregivers, and even disabled people. How is Artificial Intelligence being utilized in healthcare and medicine Medical diagnosis Technological advances help clinicians: Analyze large volumes of medical data, including symptoms, medical images__ __ and habits , Draw more precise conclusions Choose the most suitable treatments Consider risks Drug discovery Drug discovery is another great example of the practical application of sophisticated mathematical algorithms. Cutting-edge technology is analyzing data patterns, identifying targets, mapping pathways, identifying novel binding sites, and more. Clinical trials Smart assistants can be used for: Design of clinical trials Candidate recruitment and enrollment Candidate monitoring, treatment adherence, and documentation Data  analytics Pain management Pain management is still an emergent research field. Researchers found that introducing simulated realities might help distract patients from their pain source and even help with opioid resistance. Radiology In radiology, utilizing AI-based software solutions can significantly contribute to: Identifying Neurological Abnormalities Classifying Brain Tumor Recognizing Breast Cancer Radiomics Detecting Fractures Quantitative imaging Cardiology In cardiology, smart assistants can be applied in: Echocardiography Electrophysiology Nuclear Cardiology CAD Detection and Prognosis Coronary Angiography Interventional Cardiology Heart failure Hospitalization Prevention Heart failure Population Management Oncology In oncology, smart tools can be used for: Risk stratification Radiation and chemotherapy treatment-related toxicity prediction Survival & disease recurrence Therapy response Surgery In surgery, today’s AI-driven software solutions can facilitate: Preoperative planning (anatomical classification, detection, segmentation, and registration) Intraoperative guidance (depth estimation, visual odometry, structural mapping, feature tracking) Artificial Intelligence in healthcare: Pros and cons summarized The benefits of AI in healthcare Increased accessibility to high-quality medical care In developing, low-income countries, there’s almost no access to high-quality medical services and medication. AI powered telemedicine applications can facilitate an efficient healthcare ecosystem and provide disadvantaged people and communities with necessary medical services. One example of how advanced technology increases the accessibility of healthcare is , the smart health app. The application helps patients monitor symptoms and conditions, consult clinicians, and get medical guidance. Ada Real-time data Even today, some doctors are struggling with accessing patient information and providing medical guidance. By adopting AI solutions, patient records can be seamlessly collected, stored, and instantly accessed. An example of how scientific advancements are providing real-time data is , a solution by Google. The application helps access relevant information to early recognize diseases and choose more accurate treatment options. DeepMind Streamlined tasks The automation of some routine processes can increase efficiency and assist overwhelmed clinicians. Artificial intelligence facilitates assisted , early recognition of risks, accurate decision-making, and treatment. scientific research One of the examples worth mentioning is , the application, which automates routine tasks, freeing up medical personnel. Olive Time and cost efficiency Predicting clinical, financial, and other risks by analyzing business data can improve time and cost efficiency. From primarily to diagnosis and treatment, artificial intelligence can be game-changing. patient registration and examination Another solution worth mentioning is , a system forecasting possible business risks. KenSci The pitfalls of AI in medicine Human surveillance Machine and deep learning, natural language processing, artificial neural networks, and the software solutions driven by these technologies are improving medical services and research, but are not yet 100% trustworthy. This means healthcare professionals must question the logic behind suggestions made by smart assistants. Stakeholder complexities Advanced technology cannot be successfully adopted without the active contribution of all parties involved. Resistance and the lack of cooperation lead to potential failure. Regulatory compliance Healthcare providers have to introduce technologies, which are regulatory compliant (HIPAA, FDA, and others). This point is crucial to ensure patient safety and avoid legal consequences. Data privacy Patient data is sensitive by nature. Healthcare providers must ensure that the implemented technology provides personal data protection. The future of AI in healthcare Artificial intelligence is one of the most significant scientific achievements of the last decades. Technological advancements are facilitating medical diagnosis, drug discovery, clinical trials, pain management, and even doctor-to-patient contact. Among many other things, artificial intelligence is simplifying medical insurance, notifying about unpaid bills, and more. Modern technology won’t replace human specialists. Some experts confidently state that AI will soon be able to use collected data in the same way as marketing. That means complex algorithms will provide relevant recommendations, based on our age, medical history, and lifestyle. Evidence-based care will also be benefited from AI. With NLP, modern tools will be able to recognize improper medical care and alert responsible professionals to take the required action. Practical application of AI in healthcare We summarized the advantages and disadvantages of introducing artificial intelligence into the medical field. Now let’s talk about how the advanced technology is already being applied by looking at some existing apps. Here are some notable use cases of AI in the medical field: iCAD – Breast density monitoring iCAD is a tool designed to help monitor breast density via mammography to support oncology professionals. QuantX – Breast lesion detection QuantX is a workstation developed to provide an accurate computer-aided diagnosis to help radiology specialists in detecting breast abnormalities. Zebra Medical – Coronary calcium scoring Zebra Medical is developed to help cardiologists detect calcium deposits to predict cardiovascular events. Bay Labs – Echocardiogram EF determination Bay Labs is a tool utilized for the simplified selection of the left ventricular ejection fraction. The influence of AI in healthcare is major. But to drive innovation and facilitate the introduction of technology into the medical field, facilities, clinicians, and providers of custom software development have to focus on working together. To encourage the adoption and development of AI in healthcare, involved parties have to: Cooperate to create striking success stories and increase the popularity of AI Rethink education to arouse more interest in AI and its various branches Strengthening medical data quality to improve data collection, storage and information accessibility Focus on clinical leadership, which means identifying possible success stories, thought-out prioritizing, and more Final words Healthcare research improves rapidly, facilitating the accessibility of high-quality medical care all around the world. The demand is driven by multiple social factors, which include ever-rising life expectancy, major shifts in the lifestyle choices of patients, and more. Artificial intelligence notably improves medical diagnosis, drug discovery, clinical trials, and even routine tasks. Smart assistants are applied in various healthcare branches, including radiology, cardiology, oncology, as well as surgery. Today, adopting artificial intelligence to facilitate medical care can be game-changing for all parties involved. These include medical facilities, medical personnel, and patients. Important factor decision-makers should consider is approaching reliable software development providers. Tech companies with wide healthcare portfolios, experience in business automation (adapting machine and deep learning models), telemedicine and telehealth solutions, predictive analytics, prescriptive analytics, recommendation systems, and natural language processing.

How Artificial Intelligence Can Help in Improving Healthcare

The milestones covered by Artificial Intelligence, or AI as it is fondly known, has brought the world on its toes. The level of acceleration of growth in various industries has been pretty quick and sometimes, totally unpredictable. So, what is AI? AI is a collection of multiple technologies that mimic human’s cognitive functions. Apart from all the industries it has been touching, you can say that the has been receiving paramount importance. There has been a paradigm shift in the way patients are treated by doctors because they now have inordinate amounts of data in their hands, and a good amount of this data can be put to good use. modern healthcare industry It is possible to apply AI to both structured and unstructured data, with techniques including machine learning and natural language processing. The technology is widely used in all kinds of health related aspects, but it is also important to note that the largest concentrated usage is cardiology, neurology and oncology. Nurses and doctors have started adopting the technology to reduce manual work, and to provide more accurate service and impactful interventions to patients. With the volume of data going up at a staggering rate, traditional analytic methods have been revamped and there has been a change in the clinical decision making techniques. This allows the decision makers to gain unprecedented insights while making a diagnosis, planning the care process, treatment variability and finally, patient outcomes. The usage of computers is definitely not new by any means, but the amalgamation of the human mind and technology without the usage of monitors or keyboards or other paraphernalia has led to significant changes in the medical industry, and this doesn’t include just medical care, but automating administrative tasks as well. The promise of AI in the matters of health, including that of life and death critical issues is highly impressive. How AI is Changing Healthcare AI is easily the game changer in the healthcare industry. It supports human physicians, and the doctors’ roles are also changing. According to reports by Frost & Sullivan, the consulting firm, the healthcare AI market is likely to experience a compound annual growth rate of 40% by 2021, and it has the potential to change healthcare outcomes by 30–40% and cut treatment costs in half. An analysis by Accenture says that key AI applications in healthcare can create $150 billion dollars in annual savings for the US healthcare economy by 2026. The expertise of trained clinicians can now be augmented in full by the additional layer of Artificial Intelligence as it adds an extra layer through which it is possible to cut down errors in the field of health care. 1. Electronic Health Records Electronic Health Records or EHR brought out strategic changes in healthcare, but it was not a smooth transition because problems like cognitive overload, user burnout and endless documentation caused certain limitations. But enter Artificial Intelligence, and you can actually automate these routine processes and make the interfaces more intuitive. Clinical documentation is an area that takes up so much time but with the possibility of voice recognition and dictation, combined with natural language processing, a lot of time and effort can be saved. This is a major boon for doctors because information retrieval is a strong aspect in AI. And there is an even intuitive UI to make it easier for storing information. There is also a major change in the way patients are treated. Doctors no longer need to worry about drug overdose or wrong combinations or allergies because this information will all be saved in the cloud, to be relied on and acted upon at the right time. Patients with history of various infections can also benefit from the technology because it is now easier to identify the patterns and send notifications. Apart from information storage and retrieval, and identification of patterns, AI can handle routine requests as well. Here are some scenarios: - Suppose a patient has an overdue lab test, it would send notifications to the concerned patient. - A patient is about to run out of his medication. With AI, the request for medicine refill will be sent promptly. - Identify which patient out of many needs immediate care and prioritize them accordingly. 2. Medical Imaging Diagnostics Artificial Intelligence plays a major role in enabling intelligence in the radiology images obtained through scanning machines. X-rays, CT, scanners and MRI machines give glimpses of the body’s inner workings, but they are not foolproof because they are not always able to give an accurate diagnosis on their own. Doctors often had to rely on alternate or supplement methods (for example, biopsies) to take better decisions on what is wrong with the patient. But AI has completely revamped the negativities of the scanning machines by providing highly accurate inputs on the body. The diagnostic imaging team, the pathologist and the doctors can reach a unanimous decision on the mode of treatment, and the chances of overcoming hurdles are very, very high. It can sift through the different diagnostic images and check for anomalies. Using deep learning algorithms, it is now possible to differentiate between cancerous and non-cancerous cells in a much more accurate manner. The radiologists can now zoom into the problem, and study accurately, and do something more than what the human eyes could do, even with the help of high-end scanning machines and detect infections, tumors and bleeds. By checking the affected area deeply, doctors can reach a better conclusion on whether the treatment will affect the nearby areas or how deep the infection could go and reveal probabilities of the disease. Its capability in examining the affected area and going deeper into it helps the surgeons too while they are performing surgeries because they are now able to get better inputs on how to access the surgical area. 3. Virtual Health Assistance It is possible to raise patient engagement to the next level through Intelligent Virtual Assistant (IVA) and Medical Virtual Assistant (MVA). Now medical assistance has gone beyond wearables by urging patients to not just manage their goals, but also to actually help them look after their health like a real assistant would, and perhaps even more. There are health monitors and other devices that have AI incorporated in them. Here are some ways in which that can happen: - Remind patients to take their medication at the appointed time - Provide medical advice when they have common ailments or complaints - Suggest diet and eating habits for people with diet restrictions - Remind when they are about to run out of medicines and order prescriptions - Remind them of doctor appointments and manage bookings - Allow virtual interaction with doctors There are chatbots that would advice a family member or caregiver to provide primary healthcare to patients who need emergency medical help, at least until paramedics take over. It is also possible for patients to enter medical websites and chat with the chatbots, discuss symptoms, and ask health related questions. The chatbots themselves are so intelligent that they are tweaked to understand and respond to user sentiments. They can behave almost like a doctor if the need arises. True, the chatbots can never replace the typical doctor, but they can at least help alleviate the stress and fears faced by the patient. Companies like IBM, Microsoft, and Amazon already have that can respond to voice or text-based questions through apps downloaded on mobile devices. This technology can be incorporated to increase patient engagement, and the patients’ self-management skills can prevent chronic situations from getting worse. intelligent conversational systems 4. Robotic Assistance You might not be comfortable with robots performing a surgery on you. You would feel much better on the operating table when you have a competent surgeon doing the procedures. But how about converging the skills of the competent surgeon and the technical brilliance of a robot? That makes for a surgery with impressive levels of precision, steadiness and accuracy. And when you have artificial intelligence guiding the hand of the surgeon through the help of robots, it opens the doors to extremely high levels of precision, and better patient outcomes. The AI assistant can instantly provide information on the patient’s past and present health and make suggestions that would help in the diagnosis. Surgeries have become minimally invasive techniques whereby hospital stay is considerably reduced. There are surgical bots that use computer vision to perform surgeries after calculating the measurements of the human body accurately. AI can help with surgeries of various capacities, including procedures with varying levels of difficulties. And this can have huge implications on hospital stay, and thereby recovery of patient. When a surgeon performs a complex surgery, AI provides him/her with real time data to identify and reduce risk, and improve quality. Highly precise movements are made the robot hands so any tremors in the surgeon’s hands will be neutralized completely, enabling the progress and success of microsurgeries. And the best part of all, the AI system will continue monitoring the patient and his health levels, long after the doctors and nurses have gone to sleep. Human restrictions will never be a problem in generating commendable patient outcome. 5. Proactive Medical Care In traditional medical treatment, the trend was to treat the patient after the disease is detected. For example, if a patient goes to a doctor with certain symptoms, the doctor might do certain tests, and then discover the patient has cancer. Treatment like radiation and chemotherapy are started afterwards. Similarly, a patient goes to a doctor with symptoms of diabetes, and the doctor does the tests before prescribing insulin shots. This kind of treatment is called reactive medical care. With Artificial Intelligence, there has been a shift in this trend because reactive medical care became proactive medical care. In this kind of care, the patient’s complete medical history is studied and high risk markers for various diseases are highlighted. At risk patients are then monitored for any change in their conditions, and if anything seems alarming enough, then the app can suggest medical intervention. There are apps that drive the patient to be an active participant in their own health scenario. For example, the app, PeerWell encourages people to take a proactive role in their health, by saying “here are some things you can do today to improve your health and provide a good outcome”, and provide suggestions. Similarly, there are condition-specific applications for AI like palliative care, congenital heart diseases and diabetes management. The idea is to make the patient do most of the things, and avoid having to wait for a doctor to do it for them. Bringing it All Together The flowchart above makes it evident that there are mainly two kinds of AI devices: ML (Machine Language) and NLP (Natural Language Processing). The first category is Machine Learning where the technique is to analyze structured data like imaging, genetic and electrophysiology data. In the medical scenario, this means to cluster patient’s traits or check for probability of diseases. The second category is , where unstructured data is collected from various sources like clinical notes and medical journals. This data would then be converted into machine-readable structured data, which would then be analyzed by ML techniques. Natural Language Processing The chart explains the journey from clinical data generation through NLP and eventually ML data analysis, and how all of this leads to clinical decision making. The chart begins and ends with activities related to clinical practices, where through powerful AI activities, patients can enjoy accurate and timely clinical decision making. This is done through early diagnosis, where the patient is screened through imaging and tests and then by using EMR or Electronic Medical Records. EMR makes AI powerful because the notes made by doctors are saved in electronic formats. Similarly, X-rays, scans and other imaging tests are also digitized. Later, using Machine Learning and AI techniques, the medical practitioners will find a suitable diagnosis and treatment starts. Benefits of Incorporating AI in Healthcare Healthcare is definitely improving through AI. Here are the major benefits enjoyed by both the patient and medical practitioners. Predictive healthcare will lead to an evolving treatment model wherein the patient data is reviewed constantly to check for any anomalies, followed by suggestions of medical intervention. Predictive medical care — AI makes it possible for patients to have personalized care based on their body constitution and past medical history. In 2017, an elderly lady was treated for a rare form of leukemia by the University of Tokyo. The treatment was suggested by Watson, a cognitive supercomputer by IBM. The suggestion was made within 10 minutes and by scouring 20 million cancer research writings. Personalized medication — Fast research and cross-referencing of data leads to better diagnosis of diseases. The data also includes handwritten notes, geospatial and sensor data and test results. Environment (both human and natural) factors are also considered. Better diagnosis — New treatment methods are generated and introduced, including robotic surgery, cell biology, stem therapy, genomics and proteomics. Here are other examples: Advanced treatment plans — - Eye drops to dissolve cataracts, instead of eye surgeries. - Artificial pancreas to balance blood glucose levels and administer insulin. - Wound healing by printing skin cells, 3D printing of human body parts and blood vessel reproduction are all made possible through AI. Continuous monitoring of patients would ensure timely care and treatment and even reduced hospital stay. The AI based app can check for the patient’s health and vital signs in case of critically ill patients before alerting for medical intervention. Non stop monitoring means lower liability for hospital — AI can make healthcare both efficient and affordable as it helps in guiding treatment choice, making more efficient diagnosis, helps the patients make better decisions regarding their health and makes important decisions in drug development. Long term cost savings for both patient and medical care provider — Challenges that Need to be Addressed There are some thorny challenges that stand in the way of AI. Here they are: There is always a benefit of doubt; what if the smart algorithms make a wrong prediction. Will the AI have sufficient safety features so it will not turn back on the humans? Doctors must always be ready for new and exceptional cases that have not yet been recorded by AI. And what happens when there is a security breach? These risks have to be addressed. Considerable risk involved — The idea of machines and apps looking after your health can be a little intimidating to the patient, while the doctors might feel threatened. They might feel dictated by a machine. Doctor and patient comfort — AI can meaningfully improve people’s lives, but the patients should have some level of experience in using the applications. This could be a bit tough in certain cases. Training and expertise to use AI systems — However safe a system might be, hackers might find a way to get around it. So unless security systems are perfected, the patient’s life might be at risk. The hackers could alter or erase the data, wreak havoc or steal private information. Patient data security — To gain traction in healthcare, AI must comply with government healthcare standards, especially when it comes to patient privacy and safety. This is crucial with information about the patient being readily available in the cloud. Complying with government healthcare standards — Conclusion AI is changing healthcare. It changes the role of the doctors, it even changes the role of the patient. There are some challenges that need addressing, but the benefits outweigh them, and AI is here to grow and expand. It will change the medical word — in diagnosis, in treatment, in disease detection, in treatment disciplines and more. Want to know more about how Artificial Intelligence can impact your healthcare enterprise? We can help you! Contact Us Today! Originally published at Cabot Solutions on September 24, 2018.

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