mlsanityData to 14 Oct 2026

A Perspective for Adapting Generalist AI to Specialized Medical AI Applications and Their Challenges

arXiv 2411.00024 ↗First posted 28 Oct 2024cs.CL cs.AIcited by 8

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Abstract (arXiv)

The integration of Large Language Models (LLMs) into medical applications has sparked widespread interest across the healthcare industry, from drug discovery and development to clinical decision support, assisting telemedicine, medical devices, and healthcare insurance applications. This perspective paper aims to discuss the inner workings of building LLM-powered medical AI applications and introduces a comprehensive framework for their development. We review existing literature and outline the unique challenges of applying LLMs in specialized medical contexts. Additionally, we introduce a three-step framework to organize medical LLM research activities: 1) Modeling: breaking down complex medical workflows into manageable steps for developing medical-specific models; 2) Optimization: optimizing the model performance with crafted prompts and integrating external knowledge and tools, and 3) System engineering: decomposing complex tasks into subtasks and leveraging human expertise for building medical AI applications. Furthermore, we offer a detailed use case playbook that describes various LLM-powered medical AI applications, such as optimizing clinical trial design, enhancing clinical decision support, and advancing medical imaging analysis. Finally, we discuss various challenges and considerations for building medical AI applications with LLMs, such as handling hallucination issues, data ownership and compliance, privacy, intellectual property considerations, compute cost, sustainability issues, and responsible AI requirements.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 71 of its 140 references that are in the map. First posted on arXiv 28 Oct 2024.

Builds on

71 of 140 references.

References · 71

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38 more
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50 most cited of 71 All 71 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 6; 2026: 1.

8 papers.

  1. 2025A foundation model for human-AI collaboration in medical literature miningcited by 11
  2. 2024MedMax: Mixed-Modal Instruction Tuning for Training Biomedical Assistantscited by 6
  3. 2025MTBBench: A Multimodal Sequential Clinical Decision-Making Benchmark in Oncologycited by 6
  4. 2025Can Generalist Vision Language Models (VLMs) Rival Specialist Medical VLMs? Benchmarking and Strategic Insightscited by 4
  5. 2025Structured Prompts Improve Evaluation of Language Modelscited by 3
  6. 2025Nirvana: A Specialized Generalist Model With Task-Aware Memory Mechanismcited by 2
  7. 2025Holistic Artificial Intelligence in Medicine; improved performance and explainabilitycited by 0
  8. 2026From Clinical Intent to Clinical Model: Autonomous Coding-Agents for Clinician-driven AI Developmentcited by 0