The history of medical diagnosis has evolved from ancient observation to modern technological advancements. While diagnosis remains a human endeavor, artificial intelligence (AI) can help address two key challenges: human error and identifying patterns that do not fit traditional scripts. AI is less susceptible to common factors that lead to diagnostic errors and can identify intricate details in healthcare data. It can improve the accuracy and timeliness of diagnosing illnesses such as strokes, sepsis, pneumonia, and more.

Additionally, AI can recognize new patterns that may be too subtle for humans, allowing for earlier detection of conditions like hypertrophic cardiomyopathy. However, the high cost and lack of financial incentives for insurers pose challenges to the widespread adoption of AI in medicine. Federal support and collaboration between the private and public sectors can help overcome these obstacles. AI should complement human expertise rather than replace it, offering a promising future for medical diagnosis if adequately supported and implemented.