Understanding Healthcare AI’s Crucial Flaw and It’s Upcoming Solution

Learn about AI's one crucial flaw and why, once it's been solved, providers will be able to transform they way they diagnose, prescribe, and care.

According to IDC FutureScape, in the next 5 years, 30% of business and clinical decisions made by health and life science organizations will be informed by AI insight and in 2023 AI moderated collaboration between humans and machines will transform the way 1 in 3 health systems operate.

Healthcare’s Infatuation with AI

Doctors and nurses deal with large amounts of complex information and variables. AI has done wonders in processing this information. Not only that, it’s even leveraged this information towards making predictive diagnoses for patients without doctor input. Moorfield’s Eye Hospital NHS Foundation Trust tested this capacity of AI’s with its DeepMind project.

The software’s AI was trained on over 15,000 different eye scans from 7,500 different patients. Using this data, the system was capable of recommending the same treatments as a panel of 8 doctors with a success rate of 94%.

Unfortunately, when dealing with a patient’s vision or life, even a 1% inaccuracy warrants double or triple checking results. And therein lies the main problem with healthcare AI. There’s no real “double checking” an AI’s answer because it isn’t transparent with how it reaches that answer.

The “Black Box” Conundrum

AI isn’t human, despite how adamantly we try to blur that line. It cannot provide rationale or explain its point of view. All it can do is provide that point of view.

This is because the algorithms an AI system goes through when coming up with an answer are often so complex that a through-line between the proposed answer and the steps taken to come up with that answer can hardly be drawn. This leads many to refer to AI as a “Black Box” operation, one in which the process behind the provided answer is hidden behind a curtain and all the human operator sees is the result. But what if AI could explain its answer?

Explainable AI: Removing the Curtain

Explainable AI (XAI) is a set of complex algorithms that CAN explain the reasoning behind its answer. They exist today, but they’re limited to simpler algorithms that are easier to trace back such as decision trees. Unfortunately, more powerful algorithms such as neural networks sacrifice explainability for accuracy.

It’s likely that the next breakthrough in AI will be “interpretable” AI systems, ones in which complex and powerful algorithms can also be observed for their reasoning. Many are attempting to reach XAI by implementing a “Reasoning Engine” – a built in operation within an AI designed to provide links between small bits of transparent information and the actual proposed solution. The applications of such a system would be awe-inspiring.

AI Assisted Precision Medicine

Precision medicine refers to treatment catered and personalized to a patient based on everything from their lifestyle and habits to their environment and even genetics. Of course, statistical data about all of these variables is grand and wide spanning, making them perfect candidates for being processed by a deep learning AI program.

Doctors will be able to trust the treatment plans delivered to them by the algorithms because the program will provide exact reasoning for the proposed treatment.

AI Assisted Diagnosis

A professional can use AI to double check or confirm diagnoses.

Let’s take the case of someone who might have come down with the flu. An XAI system may tell a doctor, “No, this patient does not have the flu,” going on to justify that statement by adding, “the patient exhibits sneezing and headaches but not fatigue, a very common symptom of the flu.”

From here, the practicing physician may agree. Contrarily, they could disagree, perhaps because they noticed something the system missed such as a different symptom or a facet of the patient’s health history that raises the likelihood of contracting the flu.

Moving Towards Transparent and Accurate AI

AI will continue to grow in versatility and application as we continue to fine tune it throughout the years. To learn more about these exciting applications and what they mean for the healthcare space, contact a professional from Cybernet today.