JAMA Clinical Reviews
How Artificial Intelligence Has Evolved and the Implications for Health Care
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Quick takeaways
- AI 1.0 focused on rule-based decision logic flow but was limited in capabilities and faced challenges in real-world complexities.
- AI 2.0 excelled in predicting future outcomes and classifying unstructured data, but faced limitations in conducting tasks beyond prediction and classification and encountered biases inherent in the data and algorithmic design choices.
Deep dives
The Three Epochs of Artificial Intelligence in Healthcare
The podcast episode discusses three distinct epochs of artificial intelligence (AI) in healthcare. AI 1.0, which started in the 1950s, focused on symbolic AI and probabilistic models. It involved encapsulating human knowledge into computer code, such as clinical pathways in electronic health records. However, AI 1.0 was limited in its capability to handle complex scenarios and was susceptible to biased data. In the 2000s, AI 2.0 revolutionized healthcare with deep learning, enabling the prediction of future events and the classification of unstructured data. However, AI 2.0 was restricted to performing one task at a time and inherited biases from biased data. The recent emergence of AI 3.0 brings foundation models or large language models that can perform multiple tasks and generate new content. Though promising, AI 3.0 introduces the challenges of building an evidence base, addressing semantic bias, and ensuring fairness and equity.
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