Frozen Models, Evolving Expertise: Model-Agnostic Learning from Deployment Experience for Multimodal Medical AI

Key points

• This is especially concerning in medicine, where new clinical evidence, updated guidelines, and new therapies can change established practice.1
• Fine-tuning can update the model, but it requires access to model weights and additional training.1
• Parameter-free methods avoid training, but they may overfit a fixed validation set, lack reliable domain knowledge, or lose visual details by saving experience only as text.1
• Across six benchmarks covering clinical diagnosis, clinical workflows, medical reasoning, and medical and non-medical visual reasoning, and with four open-weight and closed-source base models, our framework improves performance during online deployment by up to 34.2% over the base model on medical tasks, generalizes to unseen cases, transfers to other models without further optimization, and works in non-medical domains.1

Sources (2)

• [1]Frozen Models, Evolving Expertise: Model-Agnostic Learning from Deployment Experience for Multimodal Medical AI
  Hugging Face Daily Papers · Oct 6, 12:00 AM

  Large language models (LLMs) and vision-language models (VLMs) are usually frozen after deployment, so they do not learn from the cases they solve.

  This is especially concerning in medicine, where new clinical evidence, updated guidelines, and new therapies can change established practice.

• [2]Frozen Models, Evolving Expertise: Model-Agnostic Learning from Deployment Experience for Multimodal Medical AI
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 6, 09:41 PM · same content

Extractive summary: sentences quoted from the sources.