Interpreting Some Surprising Results

Research suggests physicians are undervaluing how artificial intelligence can improve medical care.

By simulating realistic doctor–patient conversations, a framework can be applied to large language models to investigate shortcomings and bias in patient interactions, providing insight before actual ...

There have been extensive evaluations of artificial intelligence (AI) systems for narrow medical tasks, but more work is needed to systematically evaluate and deploy AI systems that can perform a broad range of medical tasks. The medical training process itself might offer a template for addressing this challenge. Clinicians undergo rigorous education and training, progressing through stages of increasing responsibility and autonomy. Similarly, generalist medical AI systems could be subjected to a phased certification model before they are granted greater autonomy in patient care.

Nature Medicine - By simulating realistic doctor–patient conversations, a framework can be applied to large language models to investigate shortcomings and bias in patient interactions,...

Open-source leaderboard for comparing and benchmarking AI models in radiology report generation.

Open-source leaderboard for comparing and benchmarking AI models in radiology report generation.

Open-source leaderboard for comparing and benchmarking AI models in radiology report generation.

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