Large Language Models in Healthcare: Opportunities and Ethical Challenges

Large Language Model: Future of Healthcare Research With Challenges

The integration of Large Language Models (LLMs) into healthcare is poised to revolutionize various aspects of medical practice, including clinical decision‐making, patient care, and medical research. This review explores the applications of LLMs such as ChatGPT‐3, ChatGPT‐4, and BERT in healthcare, focusing on their potential to enhance disease diagnosis, treatment planning, and personalized care. The paper presents a comprehensive bibliometric analysis of the growing body of research, highlighting key trends, influential authors, institutions, and geographical contributions. Despite their promise, significant challenges remain, including model accuracy, data privacy, ethical concerns, and the need for domain‐specific fine‐tuning. This review examines the moral and technical challenges associated with deploying LLMs in healthcare, including biases, a lack of transparency, and issues related to model interpretability. The paper further emphasizes the importance of robust frameworks for ensuring ethical usage. It proposes future research directions to address these challenges, including the development of specialized healthcare models, enhanced transparency, and improved integration into clinical workflows. Ultimately, this review aims to inform healthcare professionals, researchers, and policymakers about the transformative potential of LLMs in healthcare while underscoring the critical issues that must be overcome for their widespread adoption. This article is categorized under: Application Areas > Health Care Fundamental Concepts of Data and Knowledge > Big Data Mining Technologies > Artificial Intelligence

Challenges of Deep Learning in Natural Language Processing: A Healthcare-Oriented Perspective

In recent years, natural language processing has become an important tool in healthcare for extracting useful information from unstructured clinical text such as electronic health records, physician notes, and medical literature. Deep learning has significantly improved the performance of NLP systems, enabling stronger results in tasks such as disease prediction, clinical decision support, and patient risk assessment. However, healthcare NLP still faces major challenges in real-world deployment. Clinical text is often noisy, fragmented, and inconsistent, which can reduce model reliability. In addition, deep learning models lack transparency, which limits their adoption by clinicians who require explainable outputs for clinical decision-making. Privacy and security also remain major barriers because patient data is highly sensitive and subject to strict legal and ethical requirements. Bias in training data can further lead to uneven performance across patient populations. This paper combines a literature review with a healthcare-oriented case study to examine these issues in real-world settings. The findings show that although deep learning offers strong potential for healthcare analytics, progress depends on solving problems related to data quality, interpretability, privacy, and domain adaptation.

Large language models in clinical and healthcare scenarios: a global informatics analysis

Large language models (LLMs) are gradually evolving from text generators into cognitive interfaces and decision-making infrastructure that can be integrated into clinical systems. Although LLMs have demonstrated tremendous potential in retrieving evidence, structuring medical records, and facilitating clinician-patient communication, their clinical translation still faces systemic challenges in the highly heterogeneous, high-risk, and strictly regulated field of healthcare. This paper aims to utilize bibliometric methods to analyze the current global research landscape and trends regarding LLMs in clinical and healthcare scenarios. Furthermore, it proposes a systematic framework for future research directions, with the goal of providing more actionable guidance for subsequent research and clinical translation pathways in this field. Combining the results of global informatics analysis with relevant research advances, this paper conducts a comprehensive analysis of evaluation methods, deployment processes, and governance strategies for LLMs in the healthcare field, focusing on three key issues: model version drift, multilingual external validation, and prompt injection security governance. Future research on LLMs in the healthcare field should shift its focus from merely demonstrating model performance to building a closed-loop system of clinical evidence and governance that is reproducible, monitorable, auditable, and explainable. Through model drift governance, multilingual external validation, and system-level security governance, it is possible to enhance the models’ long-term reliability, generalization fairness, and the controllability and accountability of their deployment, respectively. On this basis, LLM applications may be cautiously expanded from low-risk, reviewable tasks to medical education, clinical decision support, and real-world healthcare workflows.

Large Language Models in Medicine: Opportunities, Limitations, and Future Directions – A Scoping Review

A scoping review of 24 PubMed-indexed studies published between 2023 and 2026 was conducted to assess current applications, benefits, limitations, and future directions of LLMs in healthcare.

Large Language Models in Medicine: Opportunities, Limitations, and Future Directions

Generative small language models in clinical NLP: applications, adaptation, and evaluation

An engineering-oriented, end-to-end roadmap that structures the full lifecycle of clinical language model systems—from model design and domain adaptation to optimization and real-world evaluation is introduced.