Large Language Models in Medicine: Clinical Applications, Technical Challenges, and Ethical Considerations

Area of Science:

• Artificial Intelligence in Medicine
• Clinical Informatics
• Biomedical Ethics

Background:

• Large language models (LLMs) are increasingly being explored for various applications within the medical field.
• Understanding the current capabilities, limitations, and ethical implications is vital for responsible adoption.

Purpose of the Study:

• To conduct a comprehensive review of the clinical applications of LLMs in medicine.
• To identify and analyze the technical challenges associated with LLM implementation.
• To examine the ethical considerations surrounding the use of LLMs in healthcare.

Main Methods:

• A systematic literature survey of peer-reviewed articles, technical reports, and expert commentary was performed.
• Key clinical applications, technical limitations (accuracy, validation, transparency), and ethical issues (bias, safety, privacy) were identified and analyzed.

Main Results:

• LLMs demonstrate potential in clinical documentation, decision support, patient communication, and workflow optimization.
• Technical challenges include model hallucination, validation deficits, integration complexities, and transparency issues.
• Ethical concerns encompass algorithmic bias, patient safety risks, accountability gaps, and data privacy.

Conclusions:

• LLMs offer transformative potential to augment clinician capabilities in medicine.
• Substantial technical and ethical hurdles require rigorous research, validation, clear guidelines, and human oversight.
• An assistive role for LLMs, prioritizing safety and equity, is supported by current evidence.