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<p>Clinicians spend significant time reviewing medical images and transcribing findings. By integrating visual and textual data, foundation models have the potential to reduce workloads and boost efficiency, yet their practical clinical value remains uncertain. In this study, we find that OpenAI’s ChatGPT-4o and two medical vision-language models (VLMs) significantly underperform ophthalmologists in key tasks for age-related macular degeneration (AMD). To address this, we developed a dedicated training curriculum, designed by domain specialists, to optimize VLMs for tasks related to clinical decision making. The resulting model, RetinaVLM-Specialist, significantly outperforms foundation medical VLMs and ChatGPT-4o in AMD disease staging (F1: 0.63 vs. 0.33) and referral (0.67 vs. 0.50), achieving performance comparable to junior ophthalmologists. In a reader study, two senior ophthalmologists confirmed that RetinaVLM’s reports were substantially more accurate than those written by ChatGPT-4o (64.3% vs. 14.3%). Overall, our curriculum-based approach offers a blueprint for adapting foundation models to real-world medical applications.</p>

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Specialized curricula for training vision language models in retinal image analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月19日

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  • Robbie Holland,
  • Thomas R. P. Taylor,
  • Christopher Holmes,
  • Sophie Riedl,
  • Julia Mai,
  • Maria Patsiamanidi,
  • Dimitra Mitsopoulou,
  • Paul Hager,
  • Philip Müller,
  • Johannes C. Paetzold,
  • Hendrik P. N. Scholl,
  • Hrvoje Bogunović,
  • Ursula Schmidt-Erfurth,
  • Daniel Rueckert,
  • Sobha Sivaprasad,
  • Andrew J. Lotery,
  • Martin J. Menten,
  • Toby Prevost,
  • Lars Fritsche,
  • Kristina Pfau,
  • Maximilian Pfau

摘要

Clinicians spend significant time reviewing medical images and transcribing findings. By integrating visual and textual data, foundation models have the potential to reduce workloads and boost efficiency, yet their practical clinical value remains uncertain. In this study, we find that OpenAI’s ChatGPT-4o and two medical vision-language models (VLMs) significantly underperform ophthalmologists in key tasks for age-related macular degeneration (AMD). To address this, we developed a dedicated training curriculum, designed by domain specialists, to optimize VLMs for tasks related to clinical decision making. The resulting model, RetinaVLM-Specialist, significantly outperforms foundation medical VLMs and ChatGPT-4o in AMD disease staging (F1: 0.63 vs. 0.33) and referral (0.67 vs. 0.50), achieving performance comparable to junior ophthalmologists. In a reader study, two senior ophthalmologists confirmed that RetinaVLM’s reports were substantially more accurate than those written by ChatGPT-4o (64.3% vs. 14.3%). Overall, our curriculum-based approach offers a blueprint for adapting foundation models to real-world medical applications.