⚡ Quick Summary
This comprehensive review highlights the transformative potential of Large Language Models (LLMs) in healthcare, showcasing their ability to match or exceed human performance in medical examinations and diagnostics. The integration of LLMs can enhance patient education and streamline clinical workflows, but it requires careful consideration of ethical implications and user training.
🔍 Key Details
- 📊 Applications: Diagnostics in dermatology, radiology, and ophthalmology
- 🧩 Capabilities: Language understanding and generation
- ⚙️ Integration Needs: User interface design, clinician training, collaboration with AI systems
- 🔒 Ethical Considerations: Patient privacy, data security, bias mitigation, transparency
🔑 Key Takeaways
- 🤖 LLMs can significantly enhance patient education by providing accurate and empathetic responses.
- 📈 Clinical workflows can be streamlined through efficient information extraction from unstructured data.
- 👩⚕️ User training is essential for clinicians to effectively utilize LLMs in practice.
- ⚖️ Ethical integrity must be prioritized to ensure responsible deployment of AI in healthcare.
- 🌍 Future directions include interdisciplinary collaboration and the development of multimodal LLMs.
- 🧬 Addressing underrepresented specialties like rare diseases is crucial for comprehensive healthcare solutions.
- 🔗 Integration with robotic systems could enhance precision in medical procedures.
📚 Background
The advent of Large Language Models (LLMs) represents a significant leap in the capabilities of artificial intelligence, particularly in the healthcare sector. These models have demonstrated remarkable proficiency in understanding and generating human language, which opens up new avenues for improving patient care and clinical efficiency. However, the integration of such technologies into healthcare systems necessitates a thorough understanding of their implications and the ethical considerations involved.
🗒️ Study
The review conducted by Lin and Kuo provides a comprehensive analysis of the roles and potential of LLMs in healthcare. It emphasizes the need for effective user interface design and clinician training to facilitate the integration of these advanced AI systems into everyday clinical practice. The study also highlights the importance of collaboration between healthcare professionals and AI technologies to maximize the benefits of LLMs.
📈 Results
The findings indicate that LLMs have not only matched but often surpassed human performance in standardized medical examinations. They have shown promise in assisting with diagnostics across various specialties, including dermatology, radiology, and ophthalmology. Furthermore, LLMs can enhance patient education by delivering accurate and empathetic responses, thereby improving patient engagement and understanding.
🌍 Impact and Implications
The integration of LLMs into healthcare has the potential to revolutionize patient care by enhancing the accuracy of diagnostics and improving the efficiency of clinical workflows. By streamlining information extraction from unstructured data, LLMs can help healthcare professionals make more informed decisions. However, it is crucial to address ethical concerns, such as patient privacy and data security, to ensure that these technologies are deployed responsibly and effectively.
🔮 Conclusion
The review underscores the transformative potential of Large Language Models in healthcare. As we move forward, it is essential to emphasize patient safety, ethical integrity, and human-centered implementation to maximize the benefits of LLMs. By doing so, we can ensure that these AI tools enhance rather than replace the invaluable expertise and compassion of healthcare professionals.
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Roles and Potential of Large Language Models in Healthcare: A Comprehensive Review.
Abstract
Large Language Models (LLMs) are capable of transforming healthcare by demonstrating remarkable capabilities in language understanding and generation. They have matched or surpassed human performance in standardized medical examinations and assisted in diagnostics across specialties like dermatology, radiology, and ophthalmology. LLMs can enhance patient education by providing accurate, readable, and empathetic responses, and they can streamline clinical workflows through efficient information extraction from unstructured data such as clinical notes. Integrating LLM into clinical practice involves user interface design, clinician training, and effective collaboration between Artificial Intelligence (AI) systems and healthcare professionals. Users must possess a solid understanding of generative AI and domain knowledge to assess the generated content critically. Ethical considerations to ensure patient privacy, data security, mitigating biases, and maintaining transparency are critical for responsible deployment. Future directions for LLMs in healthcare include interdisciplinary collaboration, developing new benchmarks that incorporate safety and ethical measures, advancing multimodal LLMs that integrate text and imaging data, creating LLM-based medical agents capable of complex decision-making, addressing underrepresented specialties like rare diseases, and integrating LLMs with robotic systems to enhance precision in procedures. Emphasizing patient safety, ethical integrity, and human-centered implementation is essential for maximizing the benefits of LLMs, while mitigating potential risks, thereby helping to ensure that these AI tools enhance rather than replace human expertise and compassion in healthcare.
Author: [‘Lin C’, ‘Kuo CF’]
Journal: Biomed J
Citation: Lin C and Kuo CF. Roles and Potential of Large Language Models in Healthcare: A Comprehensive Review. Roles and Potential of Large Language Models in Healthcare: A Comprehensive Review. 2025; (unknown volume):100868. doi: 10.1016/j.bj.2025.100868