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.

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