Ready or not, health care is undergoing a massive transformation driven by artificial intelligence. But medical schools have barely started to teach about AI and machine learning — creating knowledge gaps that could compound the damage caused by flawed algorithms and biased decision support systems.
In a conversation, Erkin Ötleş, a machine learning researcher working toward his medical degree and Ph.D. at the University of Michigan, said: “We need to prioritize teaching basic concepts of AI and machine learning because we have a limited time. We need to figure out what the most important things are and use that as a foundation.” He further stated, “We’re going to be at a point where we’re not going to be able to catch up and be able to call out the technology defects or flaws. Without being armed with that set of foundational knowledge into how these things work, we’re going to be at a disadvantage.”
The problem isn’t our capability to create technological tools to augment clinical decision-making. A number of these exist and are being used by physicians around the country. The problem is that these tools aren’t a routine part of the medical school curriculum, and medical students aren’t taught how to integrate them into clinical practice.
For more articles in the series on Artificial Intelligence, go to:
- An AI (Artificial Intelligence) Primer
- AI’s Capabilities
- AI Achievements
- ChatGPT (or any AI bot) and Your Medical office
- Patient Trust in AI Chatbots & ChatGPT
- Competitors to ChatGPT
- AI Policies and Regulatory Challenges
- Ai Bias
- AI’s Limitations, Concerns and Threats
- What AI Can’t and Shouldn’t Do
- AI and Accountability
- The Dangers of AI
- The Future of Generative AI
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