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Cited by 249Artificial Intelligence in Healthcare and EducationMachine Learning in HealthcareTopic Modeling

Large Language Models in Healthcare and Medical Applications: A Review

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Maity, Subhankar, Saikia, Manob Jyoti·2025·Bioengineering
DOI 10.3390/bioengineering12060631PMC PMC12189880
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Abstract

This paper provides a systematic and in-depth examination of large language models (LLMs) in the healthcare domain, addressing their significant potential to transform medical practice through advanced natural language processing capabilities. Current implementations demonstrate LLMs’ promising applications across clinical decision support, medical education, diagnostics, and patient care, while highlighting critical challenges in privacy, ethical deployment, and factual accuracy that require resolution for responsible integration into healthcare systems. This paper provides a comprehensive understanding of the background of healthcare LLMs, the evolution and architectural foundation, and the multimodal capabilities. Key methodological aspects—such as domain-specific data acquisition, large-scale pre-training, supervised fine-tuning, prompt engineering, and in-context learning—are explored in the context of healthcare use cases. The paper highlights the trends and categorizes prominent application areas in medicine. Additionally, it critically examines the prevailing technical and social challenges of healthcare LLMs, including issues of model bias, interpretability, ethics, governance, fairness, equity, data privacy, and regulatory compliance. The survey concludes with an outlook on emerging research directions and strategic recommendations for the development and deployment of healthcare LLMs.

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  • LLMs are increasingly used by healthcare professionals and patients for medical information synthesis.

    “Large Language Models (LLMs) are increasingly used by healthcare professionals and patients for medical information synthesis.”

    From Large Language Model Hallucinations in Spine Surgery: A Comparative Analysis of Clinician vs Patient-Level Prompts (2026) · cites this paper as [2]

    Section 5.4 says LLMs analyze and summarize medical literature for healthcare professionals. Section 5.3 describes patient-facing assistants that provide basic health information and explain medical terminology, but it does not establish that patients increasingly use LLMs for medical information synthesis specifically. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • Prompt framing and persona assignment can meaningfully shift LLM outputs and measured performance, including hallucination rate and factual accuracy.

    “In particular, prompt framing and persona assignment (e.g., instructing the model to respond as if to a clinician vs a patient) can meaningfully shift LLM outputs and measured performance, including hallucination rate and factual accuracy.”

    From Large Language Model Hallucinations in Spine Surgery: A Comparative Analysis of Clinician vs Patient-Level Prompts (2026) · cites this paper as [2]

    Section 4.3 (page unavailable) says that framing prompts can guide LLM outputs toward greater accuracy and improve task performance. It does not address clinician-versus-patient persona assignment or show that prompting changes hallucination rates; Section 7.2 discusses hallucinations as a general limitation, not as an outcome of prompt framing. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • How an LLM is prompted (prompt engineering) significantly affects the accuracy and ethical behavior of its output.

    “It is well-established that how an LLM is prompted (“prompt engineering”) significantly affects the accuracy and ethical behavior of its output.2,15,16”

    From Large Language Model Hallucinations in Spine Surgery: A Comparative Analysis of Clinician vs Patient-Level Prompts (2026) · cites this paper as [2]

    Section 4.3 says carefully crafted prompts guide LLMs toward accurate, clinically relevant outputs and can constrain responses to align with medical best practices. Section 7.3 discusses ethical concerns, but does not say that prompt engineering significantly affects the ethical behavior of outputs. No page numbers are available in our copy. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Maity, S., & Saikia, M. J.
Affiliations
University of Memphis (US)
Corresponding author
Saikia, Manob Jyoti
ORCID
Venue
Bioengineering
Publisher
MDPI AG
Published
2025-06-10
Volume
12
Issue
6
Pages
631
ISSN
2306-5354 (ISSN-L)
DOI
10.3390/bioengineering12060631
PMC
PMC12189880
OpenAlex
W4411184968
Cited by
249
References
10
Citations by year
2026: 213 · 2025: 35 · 2023: 1
FWCI
12.61
Type
journal article
Language
en
Open access
Licence
Locations
  • Bioengineering (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Biomedical Sensors & Systems Lab, University of Memphis, Memphis, TN 38152, USA
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
Keywords
large language models, medical applications, clinical decision support, medical education, medical diagnostics, patient care, multimodal models, prompt engineering, in-context learning, model bias, interpretability, data privacy, regulatory compliance, equity and fairness

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