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The technical landscape of clinical machine learning is shifting in ways thatdestabilize pervasive assumptions about the nature and causes of algorithmicbias. On one hand, the dominant paradigm in clinical machine learning is narrowin the sense that models are trained on biomedical datasets for particularclinical tasks such as diagnosis and treatment recommendation. On the otherhand, the emerging paradigm is generalist in the sense that general-purposelanguage models such as Google's BERT and PaLM are increasingly being adaptedfor clinical use cases via prompting or fine-tuning on biomedical datasets.Many of these next-generation models provide substantial performance gains overprior clinical models, but at the same time introduce novel kinds ofalgorithmic bias and complicate the explanatory relationship betweenalgorithmic biases and biases in training data. This paper articulates how andin what respects biases in generalist models differ from biases in priorclinical models, and draws out practical recommendations for algorithmic biasmitigation.

Algorithmic Bias, Generalist Models,and Clinical Medicine