Bioinformatics and biomedical informatics with ChatGPT: Year one review

Jinge Wang, Zien Cheng, Qiuming Yao, Li Liu, Dong Xu, Gangqing Hu

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Quant. Biol. ›› 2024, Vol. 12 ›› Issue (4) : 345-359. DOI: 10.1002/qub2.67
REVIEW ARTICLE

Bioinformatics and biomedical informatics with ChatGPT: Year one review

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Abstract

The year 2023 marked a significant surge in the exploration of applying large language model chatbots, notably Chat Generative Pre‐trained Transformer (ChatGPT), across various disciplines. We surveyed the application of ChatGPT in bioinformatics and biomedical informatics throughout the year, covering omics, genetics, biomedical text mining, drug discovery, biomedical image understanding, bioinformatics programming, and bioinformatics education. Our survey delineates the current strengths and limitations of this chatbot in bioinformatics and offers insights into potential avenues for future developments.

Keywords

ChatGPT / bioinformatics / biomedical informatics

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Jinge Wang, Zien Cheng, Qiuming Yao, Li Liu, Dong Xu, Gangqing Hu. Bioinformatics and biomedical informatics with ChatGPT: Year one review. Quant. Biol., 2024, 12(4): 345‒359 https://doi.org/10.1002/qub2.67

References

[1]
Wang H, Fu T, Du Y, Gao W, Huang K, Liu Z, et al. Scientific discovery in the age of artificial intelligence. Nature. 2023; 620 (7972): 47- 60.
CrossRef Google scholar
[2]
Xu Y, Liu X, Cao X, Huang C, Liu E, Qian S, et al. Artificial intelligence: a powerful paradigm for scientific research. Innovation. 2021; 2 (4): 100179.
CrossRef Google scholar
[3]
Van Noorden R, Perkel JM. AI and science: what 1,600 researchers think. Nature. 2023; 621 (7980): 672- 5.
CrossRef Google scholar
[4]
Milano S, McGrane JA, Leonelli S. Large language models challenge the future of higher education. Nat Mach Intell. 2023; 5 (4): 333- 4.
CrossRef Google scholar
[5]
van Dis EAM, Bollen J, Zuidema W, van Rooij R, Bockting CL. ChatGPT: five priorities for research. Nature. 2023; 614 (7947): 224- 6.
CrossRef Google scholar
[6]
Lee P, Bubeck S, Petro J. Benefits, limits, and risks of GPT-4 as an AI chatbot for medicine. N Engl J Med. 2023; 388 (13): 1233- 9.
CrossRef Google scholar
[7]
Tian S, Jin Q, Yeganova L, Lai PT, Zhu Q, Chen X, et al. Opportunities and challenges for ChatGPT and large language models in biomedicine and health. Briefings Bioinf. 2023; 25 (1): bbad493.
CrossRef Google scholar
[8]
Liu J, Yang M, Yu Y, Xu H, Li K, and Zhou X. Large language models in bioinformatics: applications and perspectives. 2024. Preprint at arXiv:2401.04155.
[9]
Xu D, Chen W, Peng W, Zhang C, Xu T, Zhao X, et al. Large language models for generative information extraction: a survey. 2023. Preprint at arXiv:2312.17617.
CrossRef Google scholar
[10]
Shue E Liu L, Li B, Feng Z, Li X, Hu G. Empowering beginners in bioinformatics with ChatGPT. Quantitative Biology. 2023; 11 (2): 105- 8.
CrossRef Google scholar
[11]
Piccolo SR, Denny P, Luxton-Reilly A, Payne SH, Ridge PG. Evaluating a large language model’s ability to solve programming exercises from an introductory bioinformatics course. PLoS Comput Biol. 2023; 19 (9): e1011511.
CrossRef Google scholar
[12]
Zhou J, Zhang B, Chen X, Li H, Xu X, Chen S, et al. An AI agent for fully automated multi‐omic analyses. 2024. Preprint at bioRxiv:2023.09.08.556814.
CrossRef Google scholar
[13]
Hu G, Liu L, Xu D. On the responsible use of chatbots in bioinformatics. Genom Proteom Bioinform. 2024; 22 (1): qzae002.
CrossRef Google scholar
[14]
Murdoch B. Privacy and artificial intelligence: challenges for protecting health information in a new era. BMC Med Ethics. 2021; 22 (1): 122.
CrossRef Google scholar
[15]
Karim MR, Islam T, Shajalal M, Beyan O, Lange C, Cochez M, et al. Explainable AI for bioinformatics: methods, tools and applications. Briefings Bioinf. 2023; 24 (5): bbad236.
CrossRef Google scholar
[16]
Hou W, Ji Z. Assessing GPT-4 for cell type annotation in single-cell rna-seq analysis. Nat Methods. 2024; 21 (8): 1462- 5.
CrossRef Google scholar
[17]
Hou W, Ji Z. Geneturing tests GPT models in genomics. 2023. Preprint at bioRxiv:2023.03.11.532238.
[18]
Jin Q, Yang Y, Chen Q, Lu Z. GeneGPT: augmenting large language models with domain tools for improved access to biomedical information. Bioinformatics. 2024; 40 (2).
CrossRef Google scholar
[19]
Ahimaz P, Bergner AL, Florido ME, Harkavy N, Bhattacharyya S. Genetic counselors’ utilization of ChatGPT in professional practice: a cross‐sectional study. Am J Med Genet. 2024; 194 (4).
CrossRef Google scholar
[20]
Duong D, Solomon BD. Analysis of large-language model versus human performance for genetics questions. Eur J Hum Genet. 2024; 32 (4): 466- 8.
CrossRef Google scholar
[21]
Alkuraya IF. Is artificial intelligence getting too much credit in medical genetics? Am J Med Genet C. 2023; 193 (3): e32062.
CrossRef Google scholar
[22]
Emmert-Streib F. Can ChatGPT understand genetics? Eur J Hum Genet. 2024; 32: 371- 2.
CrossRef Google scholar
[23]
Chen Q, Sun H, Liu H, Jiang Y, Ran T, Jin X, et al. An extensive benchmark study on biomedical text generation and mining with ChatGPT. Bioinformatics. 2023; 39 (9): btad557.
CrossRef Google scholar
[24]
Gu Y, Tinn R, Cheng H, Lucas M, Usuyama N, Liu X, et al. Domain‐specific language model pretraining for biomedical natural language processing. ACM Trans Comput Healthcare. 2021; 3 (Article 2): 1- 23.
CrossRef Google scholar
[25]
Chen Q, Du J, Hu Y, Kuttichi Keloth V, Peng X, Raja K, et al. Large language models in biomedical natural language processing: benchmarks, baselines, and recommendations. 2023. Preprint at arXiv:2305.16326.
[26]
Ateia S, Kruschwitz U. Is ChatGPT a biomedical expert? ‐‐exploring the zero‐shot performance of current GPT models in biomedical tasks. 2023. Preprint at arXiv:2306.16108.
[27]
Chen S, Li Y, Lu S, Van H, Aerts H, Savova GK, et al. Evaluating the ChatGPT family of models for biomedical reasoning and classification. J Am Med Inf Assoc. 2024; 31 (4): 940- 8.
CrossRef Google scholar
[28]
Jahan I, Laskar MTR, Peng C, Huang JX. A comprehensive evaluation of large language models on benchmark biomedical text processing tasks. Comput Biol Med. 2024; 171: 108189.
CrossRef Google scholar
[29]
Hou Y, Yeung J, Xu H, Su C, Wang F, Zhang R. From answers to insights: unveiling the strengths and limitations of ChatGPT and biomedical knowledge graphs. Preprint at Res Sq. 2023: rs.3.rs- 3185632.
CrossRef Google scholar
[30]
Rizvi RF, Vasilakes J, Adam TJ, Melton GB, Bishop JR, Bian J, et al. iDISK: the integrated dietary supplements knowledge base. J Am Med Inf Assoc. 2020; 27 (4): 539- 48.
CrossRef Google scholar
[31]
Zhao Q, Zhou X, Wu J, Cai J, Bao X, Tang L, et al. Biotreasury: a community-based repository enabling indexing and rating of bioinformatics tools. Sci China Life Sci. 2024; 67 (2): 221- 9.
CrossRef Google scholar
[32]
Wu X, Zeng Y, Das A, Jo S, Zhang T, Patel P, et al. ReguloGPT: harnessing GPT for knowledge graph construction of molecular regulatory pathways. 2024. Preprint at bioRxiv:2024.01.27.577521.
CrossRef Google scholar
[33]
Azam M, Chen Y, Arowolo M, Liu H, Popescu M, Xu D. A comprehensive evaluation of large language models in mining gene interactions and pathway knowledge. Quantitative Biology. 2024. In press.
CrossRef Google scholar
[34]
Rehana H, Bengisu Çam N, Basmaci M, Zheng J, Jemiyo C, He, Y, et al. Evaluation of GPT and BERT‐based models on identifying protein‐protein interactions in biomedical text. 2023. Preprint at arXiv:2303.17728.
CrossRef Google scholar
[35]
Tiwari K, Matthews L, May B, Shamovsky V, Orlic‐Milacic M, Rothfels K, et al. ChatGPT usage in the Reactome curation process. 2023. Preprint at bioRxiv:2023.11.08.566195.
CrossRef Google scholar
[36]
Zhou B, Ji B, Shen C, Zhang X, Yu X, Huang P, et al. Evlncrnas 3.0: an updated comprehensive database for manually curated functional long non‐coding RNAs validated by low‐throughput experiments. Nucleic Acids Res. 2024; 52 (D1): D98- 106.
CrossRef Google scholar
[37]
Chen X, Li C, Wang Z, Zhou Y, Chu M. Computational screening of biomarkers and potential drugs for arthrofibrosis based on combination of sequencing and large nature language model. Journal of Orthopaedic Translation. 2024; 44: 102- 13.
CrossRef Google scholar
[38]
Fo K, Chuah YS, Foo H, Davey EE, Fullwood M, Thibault G, et al. Plantconnectome: knowledge networks encompassing >100,000 plant article abstracts. 2023. Preprint at bioRxiv:2023.07.11.548541.
[39]
Rawte V, Sheth A, Das A. A survey of hallucination in large foundation models. 2023. Preprint at arXiv:2309.05922.
[40]
Zhang Y, Li Y, Cui L, Cai D, Liu L, Fu T, et al. Siren’s song in the AI ocean: a survey on hallucination in large language models. 2023. Preprint at arXiv:2309.01219.
[41]
Chen Y, Gao J, Petruc M, Hammer RD, Popescu M, Xu D. Iterative prompt refinement for mining gene relationships from ChatGPT. International Journal of Artificial Intelligence and Robotics Research. 2024. In press.
CrossRef Google scholar
[42]
Yao S, Yu D, Zhao J, Shafran I, Griffiths TL, Cao Y, et al. Tree of thoughts: deliberate problem solving with large language models. In: Neural information processing systems 36 (NeurIPS 2023) 35. Curran Associates, Inc; 2023, p. 11809- 22.
[43]
Savage N. Drug discovery companies are customizing ChatGPT: here’s how. Nat Biotechnol. 2023; 41 (5): 585- 6.
CrossRef Google scholar
[44]
Chakraborty C, Bhattacharya M, Lee SS. Artificial intelligence enabled ChatGPT and large language models in drug target discovery, drug discovery, and development. Mol Ther Nucleic Acids. 2023; 33: 866- 8.
CrossRef Google scholar
[45]
Zhao A, Wu Y. Future implications of ChatGPT in pharmaceutical industry: drug discovery and development. Front Pharmacol. 2023; 14: 1194216.
CrossRef Google scholar
[46]
Hu G, Xie Z. The artificial intelligence pharma era after “chat generative pre-trained transformer”. Medical Review. 2023; 3: 198- 9.
CrossRef Google scholar
[47]
Gao Z, Li L, Ma S, Wang Q, Hemphill L, Xu R. Examining the potential of ChatGPT on biomedical information retrieval: fact-checking drug-disease associations. Ann Biomed Eng. 2023.
CrossRef Google scholar
[48]
Guo T, Guo K, Nan B, Liang Z, Guo Z, Chawla NV, et al. What can large language models do in chemistry? A comprehensive benchmark on eight tasks. In: Advances in neural information processing systems, 36. Curran Associates, Inc; 2023. p. 59662- 88.
[49]
Juhi A, Pipil N, Santra S, Mondal S, Behera JK, Mondal H. The capability of ChatGPT in predicting and explaining common drug-drug interactions. Cureus. 2023; 15: e36272.
CrossRef Google scholar
[50]
Al-Ashwal FY, Zawiah M, Gharaibeh L, Abu-Farha R, Bitar AN. Evaluating the sensitivity, specificity, and accuracy of ChatGPT-3.5, ChatGPT-4, Bing AI, and bard against conventional drug-drug interactions clinical tools. Drug Healthc Patient Saf. 2023; 15: 137- 47.
CrossRef Google scholar
[51]
Herrero-Zazo M, Segura-Bedmar I, Martinez P, Declerck T. The DDI corpus: an annotated corpus with pharmacological substances and drug-drug interactions. J Biomed Inf. 2013; 46 (5): 914- 20.
CrossRef Google scholar
[52]
Wang YM, Shen HW, Chen TJ. Performance of ChatGPT on the pharmacist licensing examination in Taiwan. J Chin Med Assoc. 2023; 86 (7): 653- 8.
CrossRef Google scholar
[53]
Kunitsu Y. The potential of GPT-4 as a support tool for pharmacists: analytical study using the Japanese national examination for pharmacists. JMIR Medical Education. 2023; 9: e48452.
CrossRef Google scholar
[54]
Zong H, Li J, Wu E, Wu R, Lu J, Shen B. Performance of ChatGPT on Chinese national medical licensing examinations: a five-year examination evaluation study for physicians, pharmacists and nurses. BMC Med Educ. 2024; 24 (1): 143.
CrossRef Google scholar
[55]
Huang X, Estau D, Liu X, Yu Y, Qin J, Li Z. Evaluating the performance of ChatGPT in clinical pharmacy: a comparative study of ChatGPT and clinical pharmacists. Br J Clin Pharmacol. 2024; 90 (1): 232- 8.
CrossRef Google scholar
[56]
Wang R, Feng H, Wei GW. ChatGPT in drug discovery: a case study on anticocaine addiction drug development with chatbots. J Chem Inf Model. 2023; 63 (22): 7189- 209.
CrossRef Google scholar
[57]
Liu S, Wang J, Yang Y, Wang C, Liu L, Guo H, et al. Conversational drug editing using retrieval and domain feedback. In: The twelfth international conference on learning representations; 2024.
[58]
Liang Y, Zhang R, Zhang L, and Xie P. Drugchat: towards enabling ChatGPT‐like capabilities on drug molecule graphs. 2023. Preprint at arXiv:2309.03907.
CrossRef Google scholar
[59]
Ye G, Cai X, Lai H, Wang X, Huang J, Wang L, et al. Drugassist: a large language model for molecule optimization. 2023. Preprint at arXiv:2401.10334.
CrossRef Google scholar
[60]
Dong Q, Li L, Dai D, Zheng C, Wu Z, Chang B, et al. A survey on in‐context learning. 2022. Preprint at arXiv:2301.00234.
[61]
Li J, Liu Y, Fan W, Wei X-Y, Liu H, Tang J, et al. Empowering molecule discovery for molecule-caption translation with large language models: a ChatGPT perspective. IEEE transactions on knowledge and data engineering. 2023: 1- 13. In press.
[62]
Jablonka KM, Schwaller P, Ortega-Guerrero A, Smit B. Leveraging large language models for predictive chemistry. Nat Mach Intell. 2024; 6 (2): 161- 9.
CrossRef Google scholar
[63]
Cai X, Lai H, Wang X, Wang L, Liu W, Wang Y, et al. Comprehensive evaluation of molecule property prediction with ChatGPT. Methods. 2024; 222: 133- 41.
CrossRef Google scholar
[64]
Caldas Ramos M, Michtavy SS, Porosoff MD, White AD. Bayesian optimization of catalysts with in‐context learning. 2023. Preprint at arXiv:2304.05341.
[65]
Zeng Z, Yin B, Wang S, Liu J, Yang C, Yao H, et al. Interactive molecular discovery with natural language. 2023. Preprint at arXiv:2306.11976.
CrossRef Google scholar
[66]
Raffel C, Shazeer N, Roberts A, Lee K, Narang S, Matena M, et al. Exploring the limits of transfer learning with a unified text‐to‐text transformer. J Mach Learn Res. 2020; 21.
[67]
Hu H, Yang AJ, Deng S, Wang D, Song M, Shen S. A generative drug-drug interaction triplets extraction framework based on large language models. Proceedings of the Association for Information Science and Technology. 2023; 60 (1): 980- 2.
CrossRef Google scholar
[68]
Wei J, Bosma M, Zhao VY, Guu K, Yu AW, Lester B, et al. Fine‐tuned language models are zero‐shot learners. 2021. Preprint at arXiv:2109.01652.
[69]
Fang Y, Liang X, Zhang N, Liu K, Huang R, Chen Z, et al. Mol‐instructions: a large‐scale biomolecular instruction dataset for large language models. 2023. Preprint at arXiv:2306.08018.
[70]
Zhao Z, Ma D, Chen L, Sun L, Li Z, Xu H, et al. Chemdfm: dialogue foundation model for chemistry. 2024. Preprint at arXiv:2401.14818.
[71]
Cao H, Liu Z, Lu X, Yao Y, Li Y. Instructmol: multi‐modal integration for building a versatile and reliable molecular assistant in drug discovery. 2023. Preprint at arXiv:2311.16208.
[72]
Zhang W, Wang Q, Kong X, Xiong J, Ni S, Cao D, et al. Fine‐tuning large language models for chemical text mining. Chem Sci. 2024. In press.
CrossRef Google scholar
[73]
Acosta JN, Falcone GJ, Rajpurkar P, Topol EJ. Multimodal biomedical AI. Nat Med. 2022; 28 (9): 1773- 84.
CrossRef Google scholar
[74]
Truhn D, Weber CD, Braun BJ, Bressem K, Kather JN, Kuhl C, et al. A pilot study on the efficacy of GPT-4 in providing orthopedic treatment recommendations from MRI reports. Sci Rep-Uk. 2023; 13 (1): 20159.
CrossRef Google scholar
[75]
Liu Z, Jiang H, Zhong T, Wu Z, Ma C, Li Y, et al. Holistic evaluation of GPT‐4v for biomedical imaging. 2023. Preprint at arXiv:2312.05256.
[76]
Wu C, Lei J, Zheng Q, Zhao W, Lin W, Zhang X, et al. Can GPT‐4v(ision) serve medical applications? Case studies on GPT‐4v for multimodal medical diagnosis. 2023. Preprint at arXiv:2310.09909.
[77]
Yan Z, Zhang K, Zhou R, He L, Li X, Sun L. Multimodal ChatGPT for medical applications: an experimental study of GPT‐4v. 2023. Preprint at arXiv:2310.19061.
[78]
Buckley T, Diao JA, Rodman A, Manrai AK. Accuracy of a vision‐language model on challenging medical cases. 2023. Preprint at arXiv:2311.05591.
[79]
Yang Z, Yao Z, Tasmin M, Vashisht P, Jang WS, Ouyang F, et al. Performance of multimodal GPT‐4v on usmle with image: potential for imaging diagnostic support with explanations. 2023. Preprint at medRxiv:2023.10.26.23297629.
CrossRef Google scholar
[80]
Li Y, Liu Y, Wang Z, Liang X, Liu L, Wang L, et al. A comprehensive study of GPT‐4V’s multimodal capabilities in medical imaging. 2023. Preprint at medRxiv:2023.11.03.23298067.
CrossRef Google scholar
[81]
Hou W, Ji Z. GPT‐4V exhibits human‐like performance in biomedical image classification. 2024. Preprint at bioRxiv:2023.12.31.573796.
[82]
Wang J, Ye Q, Liu L, Guo NL, Hu G. Scientific figures interpreted by ChatGPT: strengths in plot recognition and limits in color perception. NPJ Precis Oncol. 2024; 8 (1): 84.
CrossRef Google scholar
[83]
OpenAI. GPT‐4V(ision) system card. (2023) 1- 18.
[84]
Nickerson RS. Confirmation bias: a ubiquitous phenomenon in many guises. Rev Gen Psychol. 1998; 2: 175- 220.
CrossRef Google scholar
[85]
Jin Q, Chen F, Zhou Y, Xu Z, Cheung JM, Chen R, et al. Hidden flaws behind expert‐level accuracy of GPT‐4 vision in medicine. 2024. Preprint at arXiv:2401.08396.
CrossRef Google scholar
[86]
Wang J, Liu Z, Zhao L, Wu Z, Ma C, Yu S, et al. Review of large vision models and visual prompt engineering. Meta-Radiology. 2023; 1 (3): 100047.
CrossRef Google scholar
[87]
Yang Z, Li L, Lin K, Wang J, Lin C, Liu Z, et al. The dawn of lmms: preliminary explorations with GPT‐4v(ision). 2023. Preprint at arXiv:2309.17421.
[88]
Li Z, Wang C, Liu C, Ma P, Wu D, Wang S, et al. Vrptest: evaluating visual referring prompting in large multimodal models. 2023. Preprint at arXiv:2312.04087.
[89]
Li J, Chen H, Wang Y, Chen MM, Liang H. Next-generation analytics for omics data. Cancer Cell. 2021; 39 (1): 3- 6.
CrossRef Google scholar
[90]
Xie M, Yang L, Chen G, Wang Y, Xie Z, Wang H. Ribochat: a chat‐style web interface for analysis and annotation of ribosome profiling data. Briefings Bioinf. 2022; 23 (2).
CrossRef Google scholar
[91]
Merow C, Serra-Diaz JM, Enquist BJ, Wilson AM. AI chatbots can boost scientific coding. Nat Ecol Evol. 2023; 7: 960- 2.
CrossRef Google scholar
[92]
Jansen JA, Manukyan A, Khoury NA, Akalin A. Leveraging large language models for data analysis automation. 2023. Preprint at bioRxiv:2023.12.11.571140.
CrossRef Google scholar
[93]
Dong Z, Zhong V, Lu YY. Biomania: simplifying bioinformatics data analysis through conversation. 2023. Preprint at bioRxiv:2023.10.29.564479.
CrossRef Google scholar
[94]
Liu A, Hu X, Wen L, Yu PS. A comprehensive evaluation of ChatGPT’s zero‐shot text‐to‐SQL capability. 2023. Preprint at arXiv:2303.13547.
[95]
Sima A‐C, de Farias TM. On the potential of artificial intelligence chatbots for data exploration of federated bioinformatics knowledge graphs. In: SeWebMeDa’23: 6th workshop on semantic web solutions for large‐scale biomedical data analytics, 3466. CEUR‐WS.org; 2023.
[96]
Rangel JC, de Farias TM, Sima AC, Kobayashi N. Sparql generation: an analysis on fine‐tuning openllama for question answering over a life science knowledge graph. In: WAT4HCLS 2024: 15th international semantic web applications and tools for health care and life Sciences conference; 2024. In press.
[97]
Chen C, Stadler T. Genspectrum chat: data exploration in public health using large language models. 2023. Preprint at arXiv: 2305.13821.
[98]
Wang L, Ge XJ, Liu L, Hu GQ. Code interpreter for bioinformatics: are we there yet? Ann Biomed Eng. 2024; 52 (4): 754- 6.
CrossRef Google scholar
[99]
Tang X, Qian B, Gao R, Chen J, Chen X, Gerstein M. Biocoder: a benchmark for bioinformatics code generation with large language models. Bioinformatics. 2023. In press.
CrossRef Google scholar
[100]
Sarwal V, Munteanu V, Suhodolschi T, Ciorba D, Eskin E, Wang W, et al. Biollmbench: a comprehensive benchmarking of large language models in bioinformatics. 2023. Preprint at bioRxiv:2023.12.19.572483.
CrossRef Google scholar
[101]
Lubiana T, Lopes R, Medeiros P, Silva JC, Goncalves ANA, Maracaja-Coutinho V, et al. Ten quick tips for harnessing the power of ChatGPT in computational biology. PLoS Comput Biol. 2023; 19 (8): e1011319.
CrossRef Google scholar
[102]
Chen M, Tworek J, Jun H, Yuan Q, Ponde de Oliveira Pinto H, Kaplan J, et al. Evaluating large language models trained on code. 2021. Preprint at arXiv:2107.03374.
[103]
Lehtinen T, Haaranen L, Leinonen J. Automated questionnaires about students’ Javascript programs: towards gauging novice programming processes. In: Ace’ 23: proceedings of the 25th australasian computing education conference. Association for Computing Machiner; 2023. p. 49- 58.
CrossRef Google scholar
[104]
Lin ZC. How to write effective prompts for large language models. Nat Human Behav. 2024; 8 (4): 611- 5.
CrossRef Google scholar
[105]
Denny P, Leinonen J, Prather J, Luxton‐Reilly A, Amarouche T, Becker BA, et al. Promptly: using prompt problems to teach learners how to effectively utilize Ai code generators. 2023. Preprint at arXiv:2307.16364.
[106]
Soman K, Rose PW, Morris JH, Akbas RE, Smith B, Peetoom B, et al. Biomedical knowledge graph‐enhanced prompt generation for large language models. 2023. Preprint at arXiv:2311.17330.
CrossRef Google scholar
[107]
Chen L, Zaharia M, Zou J. How is ChatGPT’s behavior changing over time?. 2023. Preprint at arXiv:2307.09009.
CrossRef Google scholar
[108]
Wang G, Yang G, Du Z, Fan L, Li X. ClinicalGPT: large language models fine‐tuned with diverse medical data and comprehensive evaluation. 2023. Preprint at arXiv:2306.09968.
[109]
Peng C, Yang X, Chen A, Smith KE, PourNejatian N, Costa AB, et al. A study of generative large language model for medical research and healthcare. NPJ Digit Med. 2023; 6 (1): 210.
CrossRef Google scholar
[110]
Lai T, Shi Y, Du Z, Wu, J, Fu, K, Dou Y, et al. Psy‐llm: scaling up global mental health psychological services with AI‐based large language models. 2023. Preprint at arXiv:2307.11991.
[111]
Liu JM, Li D, Cao H, Ren T, Liao Z, Wu J. Chatcounselor: a large language models for mental health support. 2023. Preprint at arXiv:2309.15461.
[112]
Han T, Adams LC, Papaioannou J‐M, Grundmann P, Oberhauser T, Löser A, et al. Medalpaca ‐‐ an open‐source collection of medical conversational AI models and training data. 2023. Preprint at arXiv:2304.08247.
[113]
Zhang S, Fan R, Liu Y, Chen S, Liu Q, Zeng W. Applications of transformer-based language models in bioinformatics: a survey. Bioinform Adv. 2023; 3 (1): vbad001.
CrossRef Google scholar
[114]
Qiu JN, Li L, Sun JK, Peng JC, Shi PL, Zhang RY, et al. Large AI models in health informatics: applications, challenges, and the future. IEEE J Biomed Health. 2023; 27 (12): 6074- 87.
CrossRef Google scholar

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