DEAR EDITOR
I am compelled to extend my profound respect and commendation for the article titled ‘Opportunities and challenges for ChatGPT and large language models (LLMs) in biomedicine and health’ by Tian et al. [1], featured in Briefings in Bioinformatics. This meticulous exploration into the intersection of LLMs, such as ChatGPT with biomedicine and healthcare, stands as a significant beacon for researchers, healthcare professionals and policymakers alike. It offers a balanced and critical examination of the transformative potential these technologies hold, alongside a candid discussion of the multifaceted challenges and ethical dilemmas they introduce [2, 3].
The authors have commendably navigated through the large landscape of LLM applications in biomedicine, illuminating areas, such as biomedical information retrieval, question answering, medical text summarization, information extraction and medical education. This breadth of exploration provides a comprehensive overview that not only underscores the innovative capabilities of LLMs to revolutionize healthcare practices but also conscientiously highlights the need for caution, especially in areas concerning data privacy, ethical considerations and the mitigation of biases.
We enlist some more popular LLMs in Table 1 in addition to what the authors did in their article.
Table 1.
A brief list of LLMs on health domain
| LLM | Size | Training data |
|---|---|---|
| Bio_ClinicalBERT | 436 MB (PT) | MIMIC III |
| BiomedCLIP-PubMedBERT_256-vit_base_patch16_224 | 784 MB (PT) | PMC-15M |
| BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext | 440 MB (PT) | PubMed and PubMecCentral |
| Biomedical-NER-All | 266 MB (PT) | Maccrobat to recognize the bio-medical entities (107 entities) from a given text corpus (case reports etc.) |
| ClinicalBERT | 542 MB (PT) | Large multicenter dataset with a large corpus of 1.2B words of diverse diseases we constructed and then utilized a large-scale corpus of EHRs from over 3 million patient records to fine tune the base language model |
| Medical_Summarization | 242 MB (PT) | Broad range of medical literature, enabling it to capture intricate medical terminology, extract crucial information, and produce meaningful summaries |
| Medical-NER | 736 MB (ST) | PubMed |
| Medical-mT5-large | 4.92GB (PT) | ClinicalTrials, EMEA, PubMed, Medical Crawler, SPACC, UFAL, WikiMed, Science Direct, Wikipedia—Médecine, EDP, Google Patents, Medical Commoncrawl—IT, Drug instructions, Wikipedia—Medicina, E3C Corpus—IT, Medicine descriptions, Medical theses, Medical websites, Supplement description, Medical notes, Pathologies, Medical test simulations, Clinical cases |
| BioMistral-7B | 14.5 GB (PT) | Textual data from PubMed Central Open Access (CC0, CC BY, CC BY-SA and CC BY-ND) |
PT, PyTorch; ST, SafeTensors.
The article pin points the limitations of LLMs as: hallucination, fairness and bias, privacy, legal and ethical concerns, lack of comprehensive evaluations, open-source versus closed-source LLMs and open-source versus closed-source LLMs. We add some key new challenges, such as those below along with their mitigation strategies, as shown in Table 2.
Table 2.
Further issues and mitigation strategies by using LLMs in health domain
| Issues to be tackled | Mitigation strategy |
|---|---|
| Adapting to evolving medical knowledge | Implement continuous learning mechanisms where LLMs periodically update their knowledge base from verified medical journals and databases. Establish partnerships with medical institutions to ensure timely updates on new research findings and treatment protocols |
| Personalization of healthcare information | Develop LLMs with adaptive learning capabilities to tailor responses based on patient history, preferences and specific medical conditions. Incorporate patient feedback loops to refine and personalize the information provided |
| Interpreting complex medical data | Integrate LLMs with specialized AI modules trained on specific types of medical data analysis. Foster interdisciplinary teams to create robust data interpretation frameworks combining AI insights with clinical expertise |
| Augmenting doctor-patient communication | Design LLMs to serve as augmentation tools that enhance, rather than replace, direct communication. Offer clear explanations for AI-generated advice and ensure that healthcare professionals review critical communications |
| Addressing dynamic healthcare policies | Establish a regulatory review board within the development team to monitor changes in healthcare laws and guidelines. Create agile LLM frameworks that can quickly adapt to new regulations |
The article suggests some key applications of LLMs in biomedicine and healthcare, including information retrieval, question answering, biomedical text summarization, information extraction and medical education. We include some new ways where LLMs can be beneficial in healthcare domain such as: (i) personalized treatment recommendations, (ii) predictive health analytics, (iii) automated clinical coding, (iv) virtual health assistants, (v) genomic data interpretation, (vi) patient journey mapping, (vii) healthcare workflow optimization, (viii) telemedicine support systems, (ix) ethical decision-making support, (x) clinical trial participant matching, (xi) drug discovery and repurposing, (xii) mental health monitoring and support, (xiii) nutritional advice with lifestyle coaching and (xiv) automated medical literature review.
As we stand on the precipice of this AI revolution in healthcare, it is imperative that we proceed with cautious optimism, guided by a commitment to ethical principles, inclusivity and the unwavering pursuit of advancements that serve the greater good [4]. The journey ahead is fraught with challenges, but with collective wisdom, collaboration and ethical observance, the integration of LLMs into healthcare promises to open new horizons for medical science and patient care [5].
Finally, Tian et al. have provided a timely and comprehensive review of the opportunities and challenges of ChatGPT and LLMs in biomedicine and health. While the article serves as a valuable resource for researchers, healthcare practitioners and policymakers, it could have benefited from exploring some futuristic challenges and applications of LLMs in the biomedical domain. Nonetheless, their work contributes significantly to the ongoing discourse on the role of AI in healthcare and paves the way for future research and development in this field.
Key Points
Addressing the new challenges posed by LLMs in healthcare.
Inclusion of new LLMs for healthcare.
Navigating new mitigation strategies.
Addition of novel LLM applications in medical domain.
ACKNOWLEDGEMENT
The author thanks Claude3 for initial discussion and drafting of this article.
Author Biographies
Partha Pratim Ray is working as an active academician in the field of next-generation technologies. He has published more than 140 research papers till now. He was listed as one of the top 2% scientists in the world by the Stanford University ranking in 2020, 2021, 2022 and 2023. He has a keen interest in conducting research in the key and cutting-edge technological domains. He is presently serving as an assistant professor in the Sikkim University, India. He has 7829 Google Scholar Citations, h-index of 33 and i10 index of 64. He is a senior member of IEEE and a fellow of IETE.
AUTHOR CONTRIBUTIONS
P.P.R. conceptualized, analysed, and drafted the manuscript.
References
- 1. Tian S, Jin Q, Yeganova L, et al. Opportunities and challenges for ChatGPT and large language models in biomedicine and health. Brief Bioinform 2024;25. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Subramanian CR, Yang DA, Khanna R. Enhancing health care communication with large language models—the role, challenges, and future directions. JAMA Netw Open 2024;7. [DOI] [PubMed] [Google Scholar]
- 3. Tripathi S, Sukumaran R, Cook TS. Efficient healthcare with large language models: optimizing clinical workflow and enhancing patient care. J Am Med Inform Assoc 2024;ocad258. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Guevara M, Chen S, Thomas S, et al. Large language models to identify social determinants of health in electronic health records. NPJ Digit Med 2024;7(1):6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Omiye JA, Gui H, Rezaei SJ, et al. Large language models in medicine: the potentials and pitfalls: a narrative review. Ann Intern Med 2024;177(2):210–20. [DOI] [PubMed] [Google Scholar]