Biases of large language models in diagnosing Cushing’s syndrome

Core Tip

Core Tip: The diagnosis of Cushing’s syndrome remains challenging due to its rarity and its resemblance to common metabolic disorders. Large language models and other artificial intelligence-capable systems are potential diagnostic tools for early detection and differential diagnosis; nevertheless, they are likely to strengthen both human cognitive and training data biases. Large language models are susceptible to biases in the textual data they are trained on, reflecting human cognitive biases, while traditional machine learning models are susceptible to biases in structured data, leading to spectrum and measurement bias. Spectrum bias, exclusion of demographic variables, and heterogeneity of the data undermine diagnostic validity and justice. These heuristics are similar to clinicians’ mental shortcuts - anchoring, availability, and framing - and share the same diagnostic bias. Transparency, data diversity, and clinically relevant predictors are required to build unbiased, interpretable artificial intelligence solutions for endocrine diagnosis.