A Plea to the Labs: Let the Models Diagnose (tangent.bearblog.dev)

Anthropic's recent Fable 5 model highlights a significant dilemma in the integration of large language models (LLMs) into medical diagnostics. While models like GPT 5.5 and Opus 4.8 demonstrate strong performance in ECG case analysis—often surpassing human experts—the strict guardrails of Fable prevent it from providing explicit diagnoses, raising concerns about how LLMs are currently constrained. This cautious approach, driven by liability fears and media pressure, may hinder potential advancements in medical diagnostics, according to the author. The author argues that LLMs, given their proven capabilities in correctly identifying medical conditions, should be allowed to contribute to diagnoses rather than being overly restricted. Current literature undervalues the advancements in model accuracy, with conventional studies using outdated benchmarks. The call to action urges labs to reconsider the alignment processes that inhibit LLMs from making diagnostic attempts, as doing so could ultimately enhance patient care and reduce miscommunications that lead to medical errors. By accepting the flaws inherent in both LLMs and medical practice, the author posits that we can significantly improve the diagnostic landscape in healthcare.