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Hope for the post-pandemic world (part III)

Hope for the post-pandemic world (part III)

“Success in creating AI would be the biggest event in human history. Unfortunately, it might also be the last, unless we learn how to avoid the risks.”

-Stephen Hawking, British theoretical physicist

We conclude this series on the future of artificial intelligence in healthcare with a discussion on the more distant future of artificial intelligence. In our struggle against this menacing pandemic, we can look at the future with optimism and idealism, and with artificial intelligence as one of our essential resources in our portfolio.

Significant needs and advances for AI in medicine in the coming decades will mandate us to understand the potential, limitations, and perhaps dangers of this resource.

First, means of decreasing the human burden of labeling medical images will be in the form of innovations in artificial intelligence such as few shots learning and generative adversarial networks that can enable more automated interpretation in the future.

Second, there need to be AI systems that can perform real-time AI. For this to occur, AI architectures will need to be even more robust and will need to include AI tools such as anytime algorithms, decision-theoretic meta-reasoning, and reflective architecture. These new AI tools will also need to incorporate the nuances of complexity and chaos theory as biomedical phenomena often have complex rather than complicated elements.

The entire learning portfolio will need to be explored and orchestrated for biomedical work: transfer learning, unsupervised and self-supervised learning, predictive learning, apprenticeship learning, reinforcement learning, and other types of learning to come in the future.

Digital twins at the individual and population levels and federated learning of all health systems at the international scale are now part of healthcare.

Cognitive elements of artificial intelligence such as 1) Joseph Voss’ cognitive architecture (declarative and procedure learning and memory, perception, action selection, etc), 2) Geoff Hinton’s “capsule networks”, or 3) Jeff Hawkins’ “reference frames” described in his book A Thousand Brains: A New Theory of Intelligence, will need to be increasingly a broad motif in artificial intelligence in medicine and healthcare that will incorporate the insights, intuition, and intelligence of our clinicians. The phrase “artificial intelligence in medicine or healthcare” will no longer be used as it was decades before.

The future of artificial intelligence in healthcare and many other topics will be discussed at our in-person AIMed Global Summit, currently being rescheduled to a later date this year to navigate around the pandemic. An exciting final session will be on the future of artificial intelligence in healthcare including its role in digital twins, extended reality, and federated learning. We will see each other in the near future. Find more information here.