A short guide for medical professionals in the era of artificial intelligence

Abstract

Artificial intelligence (A.I.) is expected to significantly influence the practice of medicine and the delivery of healthcare in the near future. While there are only a handful of practical examples for its medical use with enough evidence, hype and attention around the topic are significant. There are so many papers, conference talks, misleading news headlines and study interpretations that a short and visual guide any medical professional can refer back to in their professional life might be useful. For this, it is critical that physicians understand the basics of the technology so they can see beyond the hype, evaluate A.I.-based studies and clinical validation; as well as acknowledge the limitations and opportunities of A.I. This paper aims to serve as a short, visual and digestible repository of information and details every physician might need to know in the age of A.I. We describe the simple definition of A.I., its levels, its methods, the differences between the methods with medical examples, the potential benefits, dangers, challenges of A.I., as well as attempt to provide a futuristic vision about using it in an everyday medical practice.

Keywords: Communication; Information technology; Medical research.

Fig. 1. Number of medical A.I. studies by year from 2010 to 2020; and by medical specialties.

a The number of studies as found on Pubmed.com using the search term)“machine learning” OR “deep learning”) and choosing a year in advanced search. b The same search method was used followed by (AND specialty) without specifying a time frame. The number in the circles determine how many studies we found.

Fig. 2. Levels of A.I.

The three levels of A.I. as defined by Nick Bostrom in Superintelligence. The green dot indicates a theoretical threshold for what the ideal scenario would be.

Fig. 3. Visual guide to machine and deep learning subtypes.

a In supervised learning, the teacher (developer) knows what he wants to teach to the child (A.I.), defines the expected answer and the child learns to excel at the task. b In unsupervised learning, the teacher does not influence how the child learns to play but observes the conclusions the child can draw from solving the task. c In reinforcement learning, the teacher knows what he wants to teach to the child but does not define step-by-step how the child should learn it. Instead, the teacher only gives feedback after the task is completed and asks the child to find out his own strategy using those outcomes the teacher rewarded. d In deep learning, it is possible to analyze vastly more complex data sets from images and videos to a sort of human reasoning. It is multi-layered and could mimic how neural networks in the brain work.