The healthcare sector has long been an early adopter of and benefited greatly from technological advances. These days, machine learning (a subset of artificial intelligence) plays a key role in many health-related realms, including the development of new medical procedures, the handling of patient data and records and the treatment of chronic diseases. As computer scientist Sebastian Thrum told the New Yorker in a recent article titled "A.I. Versus M.D., "Just as machines made human muscles a thousand times stronger, machines will make the human brain a thousand times more powerful." Despite warnings from some doctors that things are moving too fast, the rate of progress keeps increasing. And for many, that's as it should be. "AI is the future of healthcare," Fatima Paruk, CMO of Chicago-based Allscripts Analytics, said in 2017. She went on to explain how critical it would be in the ensuing few years and beyond -- in the care management of prevalent chronic diseases; in the leveraging of "patient-centered health data with external influences such as pollution exposure, weather factors and economic factors to generate precision medicine solutions customized to individual characteristics"; in the use of genetic information "within care management and precision medicine to uncover the best possible medical treatment plans." "AI will affect physicians and hospitals, as it will play a key role in clinical decision support, enabling earlier identification of disease, and tailored treatment plans to ensure optimal outcomes," Paruk explained. "It can also be used to demonstrate and educate patients on potential disease pathways and outcomes given different treatment options.
In a world where patients are getting more and more involved in their own health, the problems of manual processes are many. Having medical records, research documents, lab reports, doctor prescriptions, etc. on paper restricts seamless understanding and sharing of important health information that ultimately affects care outcome. Although advancements in healthcare technology have been remarkable, the information they provide is not sufficient to make improved healthcare decisions. What is required, in my opinion, is for healthcare information to be enhanced by the power of analytics and machine learning. Through advanced analytics, machine learning can help provide better information to doctors at the point of patient care.
Machine learning, the most fundamental form of artificial intelligence, has started infiltrating the medical field, and it seems machines can play a crucial role in improving our health. A study of over 50 executives in the healtcare sector by TechEmergence revealed that by 2025 AI will be adopted on a broader scale. If there's one thing the healthcare industry has in abundance, it's undoubtedly data. And machine learning algorithms work better if they are exposed to more data. The savings would also be huge.
One of the hottest tech trends these days is artificial intelligence (AI), with researchers looking into the use of AI for applications ranging from autonomous vehicles to financial management, to healthcare. The healthcare industry is often at the forefront of innovation and technological advances due to the wealth of medical devices, equipment and processes that permeate the industry. But AI in particular seems poised to transform the way we collect, understand and use data on patient health, healthcare services and historical health data to revolutionize medical diagnostics, treatment and research. What makes AI so suitable for use in medical research and the healthcare industry? Largely, the appeal of AI is its ability to collect, analyze and make sense of vast amounts of unstructured and variable data--especially text, statistical numbers, and visual images--quickly and often more accurately than a human being.
When it comes to effectiveness of machine learning, more data almost always yields better results--and the healthcare sector is sitting on a data goldmine. McKinsey estimates that big data and machine learning in pharma and medicine could generate a value of up to $100B annually, based on better decision-making, optimized innovation, improved efficiency of research/clinical trials, and new tool creation for physicians, consumers, insurers, and regulators. Where does all this data come from? If we could look at labeled data streams, we might see research and development (R&D); physicians and clinics; patients; caregivers; etc. The array of (at present) disparate origins is part of the issue in synchronizing this information and using it to improve healthcare infrastructure and treatments.