Concerns Linger Over AI In Health Care

 Concerns Linger Over AI In Health Care 



Of all the fields AI is expected to permeate over the coming decade, perhaps none is more consequential than health care. From early diagnostics to robot-assisted surgery, AI is expected to enhance our health in a variety of ways.

It also has the potential to do great harm. The human body is a swirling mass of biological, chemical, and electrical processes. This is all while having structures and physiologies so diverse that no two are exactly alike. Health care should tread carefully when it comes to implementing AI, making certain that negative outcomes be kept to a minimum.


There is a lot to look forward to when it comes to allowing bots to participate in our health decisions. For one thing, AI can provide practitioners with real-time information and analytics on medical issues, as well as streamline many of the time-consuming tasks that impede the health care process. It should be able to accomplish this while also reducing the need for an abundance of resources that currently clogs up the health care process and drives up costs.


At the moment,  AI is having the greatest impact on two key medical functions: diagnosis and clinical decision-making. Its chief benefit is the ability to minimize intra- and inter-observer variability to provide greater accuracy and speed. An X-ray, can be subject to wide interpretation to the human eye, but AI can pinpoint minute details that can confirm one diagnosis over another, or detect anomalies that might otherwise have gone unnoticed. In the future, we can expect AI to make significant contributions in the areas of drug discovery, pandemic tracking and prevention.


However, AI is not perfect, so mistakes are bound to happen. Not only is AI limited by the data it can access when trying to plot a course of action, it is also susceptible to security breaches and has been shown to exhibit the same social biases that humans possess.


Another potential problem is the fact that the decision-making processes employed by most AI algorithms are opaque at best. This lack of transparency is causing some embarrassing failures in AI-driven health solutions. The good news is that these issues are not intractable. As algorithms become more refined and both patients and practitioners learn what to expect from AI and what not to expect, the technology’s contribution to the health care system should be significant. When both costs and patient outcomes are less than what people desire, it’s good to know that there is something at the ready that should make substantial improvements to both.


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