AI in Healthcare? …it’s already there!

Creative destruction

An Austrian economist Joseph Schumpeter noted in 1940’s that businesses which fail to add value to existing business process shall be destroyed. He called this phenomenon “Creative destruction”. While his prediction has generally come true; (Nokia, Kodak, Compaq) health care has been artificially shielded from that business model.

Only a fraction of what physicians do is based on solid evidence from Grade-A randomized, controlled trials; the rest is based instead on weak or no evidence & on subjective judgment. Most physicians work in virtually data free environment, devoid of feedback on correctness of their practice. They may not probably know about correctness of the quality and outcomes of their diagnosis & treatment decisions. And there is no data indicating that they should change what they are doing.

Today we have access to humongous amount of data from disparate sources. Physicians may have to change their perspective. Medical informatics & Artificial intelligence may quite possibly change all this. Firstly the term artificial intelligence in healthcare is highly misleading. AI (Artificial Intelligence) will not reproduce intelligence, but use algorithms to find patterns, similarities & features from large data sets.

The road less traveled…

We will try to chart a roadmap to AI implementation in healthcare. AI as in Artificial intelligence will have three milestones.

    1. AI as in “Augmented intelligence”. It is the lowest end of the spectrum. It automates the basic tasks in care-giving through work-flows. The Work-flows can be triggered by alarms, other workflows or by the care-givers. AI as in “Assisted intelligence”. The physicians shall make more informed & effective decisions based on circumstances.

2.The “Assisted intelligence” tool shall have advanced algorithms to train the artificial neural networks to give treatment options to physicians. IBM’s Watson for e.g. provides suggestions for treatment plans & every step is linked to supporting evidence. It is left to the physician to take an appropriate decision. For “Assisted intelligence” can make a significant impact, but for this it must be integrated into clinician’s workflow

3.AI as in “Automated intelligence”. Here the doctor is no longer in loop & the control is handed over to the machine (as in driverless cars). This situation is very risky as many of the situations in patient-care the solutions are not binary, the solution is often ambiguous requiring discretion & judgment from the clinicians.


Rough waters …..

Implementation of AI in healthcare was faced with several challenges which made its use on large scale difficult to begin with. There were issues of standardization, availability of good quality data sets to develop neural algorithms, validation & reading of unstructured data (like radiography images). However lot of work has been done in these areas & we can now infer that many a stable & reliable products have made appearance in this realm. The AI platforms of the day quite successfully address the quintessential problem of “too much data & too little time” faced by the clinicians.

The early movers…

IBM-Watson-Most spectacular success of AI in healthcare has been IBM-Watson named after IBM’s first CEO Thomas Watson. Its natural language processing skills, hypothesis generation, and evidence based learning capabilities allow it to function as clinical decision support system. Watson can read 40 million documents in 15 seconds. 80 % data in healthcare is invisible as it is unstructured. However Watson can see it.

At Memorial Sloan-kettering hospital IBM-watson is poised to help doctors make better treatment choices for oncology patients. There are 2 similar promising AI platforms Enlitic & Hind S (AI) t using deep learning diagnostic support.

Babylon health-Babylon health is an AI based app designed to improve doctors hit rate. It can constantly monitor information on kidneys, liver, bones, cholesterol levels, sleep patterns & heart rates from wearable devices. It can issue alerts to patients based on these inputs, it can also predict illness. Since the current regulation does not machines to diagnose illness, Babylon offers suggestions based upon collected evidence.

Sophia genetics-Sophia genetics develops clinical genomics solution by intercepting data from DNA extraction. Thanks to advances both in DNA sequencing & mathematics many types of cancers are becoming less mysterious. In fact Sophia genetics claims to be world’s most advanced collective AI platform.


We all wish to make sense of the world around us. Till recently the data which was inputted to us was fairly limited & our natural intelligence was good enough to figure things out. But the world of “Big data” is so huge that we will need “Artificial intelligence” to keep a track of it. Although the state of “real AI” has not been reached, it shall sneak into healthcare eco-system without much fanfare.


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AI in Healthcare? …it’s already there!