Artificial Intelligence is becoming an essential component of medical diagnosis
In recent years, a wealth of literature has emerged exploring how AI and machine learning (ML) can improve diagnostic precision in medicine. Combined with deep learning (a subset of ML), this research has the potential,
inter alia, to advance cancer detection, streamline treatment algorithms, and enhance our ability to predict the risk of disease development. In brief, ML is the process by which AI can be trained to mimic the way humans learn, thereby improving its own accuracy over time.
As with any professional paradigm shift, controversy and spirited debates on ethics abound. Topics have included physician concerns that expert clinical decision-making may be forfeited to a computer algorithm with limited interpretability, the problem of ML systems often "overfitting" data (when an algorithm starts to measure sheer randomness rather than observable characteristics),
1 and the integration of bias into any given ML program.
2 The discussion of how medical bias relates to racial disparities in medicine is of particular concern in the modern era. However, a recent study regarding diagnostic imaging offers a reminder that this topic remains fraught with taboos and confusion.
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