Differentiating hype from practical applications of large language models in medicine - a primer for healthcare professionals

• URL: http://arxiv.org/abs/2507.19567v1
• Date: Fri, 25 Jul 2025 16:40:17 GMT
• Title: Differentiating hype from practical applications of large language models in medicine - a primer for healthcare professionals
• Authors: Elisha D. O. Roberson,
• Abstract summary: Machine learning and artificial intelligence techniques, including large language models (LLMs), have been promised to deliver on healthcare innovation.<n>LLMs have no understanding of objective truth that is based in reality.<n>They also represent real risks to the disclosure of protected information when used by clinicians and researchers.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The medical ecosystem consists of the training of new clinicians and researchers, the practice of clinical medicine, and areas of adjacent research. There are many aspects of these domains that could benefit from the application of task automation and programmatic assistance. Machine learning and artificial intelligence techniques, including large language models (LLMs), have been promised to deliver on healthcare innovation, improving care speed and accuracy, and reducing the burden on staff for manual interventions. However, LLMs have no understanding of objective truth that is based in reality. They also represent real risks to the disclosure of protected information when used by clinicians and researchers. The use of AI in medicine in general, and the deployment of LLMs in particular, therefore requires careful consideration and thoughtful application to reap the benefits of these technologies while avoiding the dangers in each context.

Related papers

• Medical Reasoning in the Era of LLMs: A Systematic Review of Enhancement Techniques and Applications [59.721265428780946]
  Large Language Models (LLMs) in medicine have enabled impressive capabilities, yet a critical gap remains in their ability to perform systematic, transparent, and verifiable reasoning.<n>This paper provides the first systematic review of this emerging field.<n>We propose a taxonomy of reasoning enhancement techniques, categorized into training-time strategies and test-time mechanisms.
  arXiv  Detail & Related papers  (2025-08-01T14:41:31Z)
• Generative AI in clinical practice: novel qualitative evidence of risk and responsible use of Google's NotebookLM [0.0]
  NotebookLM is a tool that generates AI-voiced podcasts to educate patients about treatment and rehabilitation.<n>We argue that NotebookLM presently poses clinical and technological risks that should be tested and considered prior to its implementation in clinical practice.
  arXiv  Detail & Related papers  (2025-05-04T01:25:33Z)
• Towards Safe AI Clinicians: A Comprehensive Study on Large Language Model Jailbreaking in Healthcare [15.438265972219869]
  Large language models (LLMs) are increasingly utilized in healthcare applications.<n>This study systematically assesses the vulnerabilities of seven LLMs to three advanced black-box jailbreaking techniques.
  arXiv  Detail & Related papers  (2025-01-27T22:07:52Z)
• Demystifying Large Language Models for Medicine: A Primer [50.83806796466396]
  Large language models (LLMs) represent a transformative class of AI tools capable of revolutionizing various aspects of healthcare. This tutorial aims to equip healthcare professionals with the tools necessary to effectively integrate LLMs into clinical practice.
  arXiv  Detail & Related papers  (2024-10-24T15:41:56Z)
• Safety challenges of AI in medicine in the era of large language models [23.817939398729955]
  Large language models (LLMs) offer new opportunities for medical practitioners, patients, and researchers.<n>As AI and LLMs become more powerful and especially achieve superhuman performance in some medical tasks, public concerns over their safety have intensified.<n>This review examines emerging risks in AI utilization during the LLM era.
  arXiv  Detail & Related papers  (2024-09-11T13:47:47Z)
• Towards Reliable Medical Question Answering: Techniques and Challenges in Mitigating Hallucinations in Language Models [1.03590082373586]
  This paper conducts a scoping study of existing techniques for mitigating hallucinations in knowledge-based task in general and especially for medical domains. Key methods covered in the paper include Retrieval-Augmented Generation (RAG)-based techniques, iterative feedback loops, supervised fine-tuning, and prompt engineering. These techniques, while promising in general contexts, require further adaptation and optimization for the medical domain due to its unique demands for up-to-date, specialized knowledge and strict adherence to medical guidelines.
  arXiv  Detail & Related papers  (2024-08-25T11:09:15Z)
• Dr-LLaVA: Visual Instruction Tuning with Symbolic Clinical Grounding [53.629132242389716]
  Vision-Language Models (VLM) can support clinicians by analyzing medical images and engaging in natural language interactions. VLMs often exhibit "hallucinogenic" behavior, generating textual outputs not grounded in contextual multimodal information. We propose a new alignment algorithm that uses symbolic representations of clinical reasoning to ground VLMs in medical knowledge.
  arXiv  Detail & Related papers  (2024-05-29T23:19:28Z)
• A Survey of Large Language Models in Medicine: Progress, Application, and Challenge [85.09998659355038]
  Large language models (LLMs) have received substantial attention due to their capabilities for understanding and generating human language. This review aims to provide a detailed overview of the development and deployment of LLMs in medicine.
  arXiv  Detail & Related papers  (2023-11-09T02:55:58Z)
• Large Language Models Illuminate a Progressive Pathway to Artificial Healthcare Assistant: A Review [16.008511195589925]
  Large language models (LLMs) have shown promising capabilities in mimicking human-level language comprehension and reasoning. This paper provides a comprehensive review on the applications and implications of LLMs in medicine.
  arXiv  Detail & Related papers  (2023-11-03T13:51:36Z)
• Redefining Digital Health Interfaces with Large Language Models [69.02059202720073]
  Large Language Models (LLMs) have emerged as general-purpose models with the ability to process complex information. We show how LLMs can provide a novel interface between clinicians and digital technologies. We develop a new prognostic tool using automated machine learning.
  arXiv  Detail & Related papers  (2023-10-05T14:18:40Z)
• Self-Verification Improves Few-Shot Clinical Information Extraction [73.6905567014859]
  Large language models (LLMs) have shown the potential to accelerate clinical curation via few-shot in-context learning. They still struggle with issues regarding accuracy and interpretability, especially in mission-critical domains such as health. Here, we explore a general mitigation framework using self-verification, which leverages the LLM to provide provenance for its own extraction and check its own outputs.
  arXiv  Detail & Related papers  (2023-05-30T22:05:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.