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Architecting Clinical Collaboration: Multi-Agent Reasoning Systems for Multimodal Medical VQA

Created by
  • Haebom

Author

Karishma Thakrar, Shreyas Basavatia, Akshay Daftardar

Outline

This paper highlights the challenges of dermatology consultations in remote settings, namely the need for diagnosis with limited information (images and brief descriptions). To address this, we propose a medical AI system that mimics clinical reasoning. We compared and analyzed seven vision-language models across six configurations: a baseline model, a fine-tuned model, a model with an additional inference layer, and a model with added medical literature search capabilities. While fine-tuning actually resulted in a decrease in performance, the architecture mimicking clinical reasoning achieved up to 70% accuracy and generated explainable, literature-based output, a crucial element for clinical application. This demonstrates that medical AI can be successful by reimagining collaborative and evidence-based practice in clinical diagnosis.

Takeaways, Limitations

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Takeaways:
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We propose that a medical AI architecture that mimics clinical reasoning processes is effective in improving the accuracy of dermatological care in telemedicine environments.
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This suggests that fine-tuning does not always guarantee improved performance, and may even result in decreased performance.
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We emphasize that explainable and medical literature-based output is essential for clinical application.
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This demonstrates the importance of mimicking clinical processes, rather than simply relying on data-driven approaches, in the development of medical AI.
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Limitations:
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Further research on generalizability using limited models and datasets is needed.
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Validation in a real clinical setting is needed.
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Further performance evaluation for various skin conditions is needed.
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Performance may be affected by the quality and quantity of medical literature used.
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