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Applying Language Models in Clinical Medicine: Recent Trends and Perspectives

arXiv 2609.34780 ↗First posted 28 Sep 2026cs.AIcited by 0

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Abstract (arXiv)

The use and applicability of artificial intelligence (AI) in medical research and clinical practice has received increasing attention in the literature over recent years. The emergence of large language models (LLMs) has expanded discussions in regards to applications of AI within healthcare. While traditional deep learning based AI applications in medicine have often focused on specific and defined tasks, LLMs offer broader capabilities and flexibility in working with available data,. At the same time of writing, the integration of LLMs into medical settings raises important questions regarding their reliability, accuracy, transparency, safety, and appropriate role in a medical setting. This text presents and discusses recent talks and articles concerning the application of LLMs in medicine, with particular emphasis on their potential utility in research and clinical practice. It considers both the opportunities offered by these technologies and the challenges associated with their implementation, aiming to provide a perspective on the current and emerging role of LLMs within the medical field.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 23 of its 30 references that are in the map. First posted on arXiv 28 Sep 2026.

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References · 23

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  2. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
  3. 2023MINT: Evaluating LLMs in Multi-turn Interaction with Tools and Language Feedbackcited by 297
  4. 2023MetaTool Benchmark for Large Language Models: Deciding Whether to Use Tools and Which to Usecited by 217
  5. 2023CRAFT: Customizing LLMs by Creating and Retrieving from Specialized Toolsetscited by 129
  6. 2023MOTOR: A Time-To-Event Foundation Model For Structured Medical Recordscited by 78
  7. 2024MedCalc-Bench: Evaluating Large Language Models for Medical Calculationscited by 70
  8. 2024Medformer: A Multi-Granularity Patching Transformer for Medical Time-Series Classificationcited by 53
  9. 2024MedSafetyBench: Evaluating and Improving the Medical Safety of Large Language Modelscited by 44
  10. 2024Tool-Planner: Task Planning with Clusters across Multiple Toolscited by 33
  11. 2025MedAgentBoard: Benchmarking Multi-Agent Collaboration with Conventional Methods for Diverse Medical Taskscited by 29
  12. 2024MediConfusion: Can you trust your AI radiologist? Probing the reliability of multimodal medical foundation modelscited by 23
11 more
  1. 2025BingoGuard: LLM Content Moderation Tools with Risk Levelscited by 21
  2. 2024Eye-gaze Guided Multi-modal Alignment for Medical Representation Learningcited by 16
  3. 2024PediatricsGPT: Large Language Models as Chinese Medical Assistants for Pediatric Applicationscited by 16
  4. 2024Learning Evolving Tools for Large Language Modelscited by 16
  5. 2024SM3-Text-to-Query: Synthetic Multi-Model Medical Text-to-Query Benchmarkcited by 14
  6. 2023Enhancing Small Medical Learners with Privacy-preserving Contextual Promptingcited by 11
  7. 2024The Limited Impact of Medical Adaptation of Large Language and Vision-Language Modelscited by 9
  8. 2024FEDMEKI: A Benchmark for Scaling Medical Foundation Models via Federated Knowledge Injectioncited by 8
  9. 2024Reliable and diverse evaluation of LLM medical knowledge masterycited by 6
  10. 2024Low-Rank Knowledge Decomposition for Medical Foundation Modelscited by 5
  11. 2024How Can We Diagnose and Treat Bias in Large Language Models for Clinical Decision-Making?cited by 4

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