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Artificial Intelligence in Healthcare: A Clinician's Perspective
By Prof K. Ganapathy, Director, Apollo Telemedicine Networking Foundation and Apollo Tele Health Services, India
The term Artificial Intelligence or AI was first introduced by Mcarthy in 1956. As a clinician trained in the BC era, I believe that the A in AI should stand for Augmenting, Amplifying, Accelerating and Assisting in an ambient milieu. What is artificial in AI? AI is only an extension of Natural Intelligence. Augmented Intelligence helps expand the role of a domain expert. Accelerated engineering and analysis can help expedite the processing of data-rich workflows. AI is today enabling a constellation of mainstream technologies, having a substantial impact on our everyday lives. AI is an example of pole vaulting, not just leap frogging.
Tomorrow’s 5P (Predictive, Personalised, Precision, Participatory and Preventive) Medicine when fully functional, will have AI as a major component. Precision Medicine presupposes the availability of massive computing power and algorithms that can learn by themselves, at an unprecedented rate. As 80 percent of the 41 Zetabytes (410 trillion GB) of digital information currently available is unstructured AI will be required to detect patterns and trends, which our grey matter is unable to decipher. Even after making allowance for an unprecedented hype, it is an undeniable fact that, in the coming decade, deployment of Artificial Intelligence (AI) will cause a paradigm shift in the delivery of healthcare.
Powerful AI techniques can unlock clinically relevant information, hidden in massive amounts of data. Translating technical computational success to meaningful clinical impact is however a challenge. AI requires thorough and systematic evaluation, prior to integration in clinical care.
Clinicians need to be future ready, to use AI in their practice
In a world where algorithms can make diagnoses, wearables can track vital signs and robots can be remotely controlled, to perform surgical procedures, will clinicians of tomorrow eventually become an endangered species. However, translating technical success to meaningful clinical impact is a challenge. Stephen Hawking had opined that the development of full AI could spell the end of the human race. Elon Musk concurred adding “a fleet of AI enhanced robots is capable of destroying mankind”. The Gartner Hype cycle for emerging technologies describes the different phases of innovation trigger, peak of inflated expectations, trough of disillusionment and finally scope of enlightenment and plateau of productivity. Obviously we are nearer the beginning of the Hype cycle.
“The good physician treats the disease; the great physician treats the patient who has the disease— Medicine is a science of uncertainty and an art of probability—listen, listen, listen—the patient is telling you the diagnosis”. One wonders how Sir William Osler, author of the above statements would have reacted to the introduction of AI in Healthcare. For centuries, the essence of practicing medicine has been a physician obtaining as much data about the patient’s health or disease as possible and taking decisions. Wisdom presupposed experience, judgement, and problem-solving skills using rudimentary tools and limited resources. Charles Dickens began his immortal “Tale of Two Cities” with the statement: “It was the best of times, it was the worst of times, it was the age of wisdom, it was the age of foolishness, it was the spring of hope, it was the winter of despair, we had everything before us, we had nothing before us”. He could very well have been referring to AI. Good and evil are two sides of the same coin. Time alone will tell if AI in healthcare will be a bane or a boon. A clinician will adopt AI when there is evidence that AI betters outcomes, and reduces costs. We are in a stage of transition. All transitions offer great opportunities. AI will never ever replace a commiserating clinician. Hopefully the AI enabled clinician will now spend more time empathising with his patient rather than getting drowned in voluminous data.