Letter to the Editor Open Access
Copyright ©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Clin Cases. Nov 16, 2023; 11(32): 7935-7939
Published online Nov 16, 2023. doi: 10.12998/wjcc.v11.i32.7935
Potential and limitations of ChatGPT and generative artificial intelligence in medical safety education
Xin Wang, Xin-Qiao Liu, School of Education, Tianjin University, Tianjin 300350, China
ORCID number: Xin Wang (0009-0001-1098-0589); Xin-Qiao Liu (0000-0001-6620-4119).
Author contributions: Liu XQ designed the study; Liu XQ and Wang X wrote the manuscript; all authors contributed equally to this work and have approved the final manuscript.
Conflict-of-interest statement: The authors declare no conflict of interests.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Xin-Qiao Liu, PhD, Associate Professor, School of Education, Tianjin University, No. 135 Yaguan Road, Jinnan District, Tianjin 300350, China. xinqiaoliu@pku.edu.cn
Received: August 7, 2023
Peer-review started: August 7, 2023
First decision: September 19, 2023
Revised: September 21, 2023
Accepted: November 2, 2023
Article in press: November 2, 2023
Published online: November 16, 2023

Abstract

The primary objectives of medical safety education are to provide the public with essential knowledge about medications and to foster a scientific approach to drug usage. The era of using artificial intelligence to revolutionize medical safety education has already dawned, and ChatGPT and other generative artificial intelligence models have immense potential in this domain. Notably, they offer a wealth of knowledge, anonymity, continuous availability, and personalized services. However, the practical implementation of generative artificial intelligence models such as ChatGPT in medical safety education still faces several challenges, including concerns about the accuracy of information, legal responsibilities, and ethical obligations. Moving forward, it is crucial to intelligently upgrade ChatGPT by leveraging the strengths of existing medical practices. This task involves further integrating the model with real-life scenarios and proactively addressing ethical and security issues with the ultimate goal of providing the public with comprehensive, convenient, efficient, and personalized medical services.

Key Words: Medical safety education, ChatGPT, Generative artificial intelligence, Potential, Limitation

Core Tip: Generative artificial intelligence, represented by ChatGPT, has been experiencing rapid development. We believe that the era of leveraging artificial intelligence for medical safety education has arrived. To make the most of ChatGPT and generative artificial intelligence, it is essential to acknowledge both their strengths and limitations. By remaining vigilant and capitalizing on their advantages while addressing their shortcomings, we can strive to optimize and enhance the performance of ChatGPT and generative artificial intelligence. This ongoing exploration of the seamless integration of medical safety education with artificial intelligence is crucial in providing better medical services to the public.



TO THE EDITOR

We have read the article by Liu et al[1] on medication safety. The findings of that research indicate that age and working status have positive and significant impacts on knowledge scores related to medication risk. Additionally, there is a significant positive correlation between working status and medication behavior scores. Moreover, the scores for knowledge, cultural beliefs, and medication behavior are significantly influenced by individuals' education levels, with higher levels of education leading to higher scores in these areas. This discoveries reported by this study contribute to the literature and provide valuable evidence related to medication safety among Chinese residents. Simultaneously, in combination with other related research, this study highlights the increasingly significant role of education in improving medication safety. Considering patient characteristics, a comprehensive approach that combines online and offline approaches to medical safety education will be a necessary path in the future to reduce medication risk among residents.

The main purpose of medical safety education is to help the public understand fundamental knowledge concerning medications, develop a scientific approach to drug usage, and enhance awareness and acceptance of diseases and medication treatments. These goals, in turn, help individuals avoid the dangerous consequences of such misconceptions in their daily lives. Effective health guidance plays a vital role in changing patients’ lifestyles, improving their self-efficacy, and enhancing their overall physical and mental well-being[2]. Traditional methods of providing medication education to residents include one-on-one face-to-face explanations, group lectures, telephone guidance, electronic campaigns, books, magazines, and personalized online consultations[3-5]. Due to the swift development of information technology and the rapid rise of artificial intelligence, AI-based large-scale screening and digital intervention methods have gradually emerged and been applied in practice[6-10]. The global coronavirus disease 2019 pandemic has further accelerated the rapid adoption and widespread use of telehealth based on electronic information and telecommunication technologies[11]. The internet has become an essential source of health information and a medium for empowering patients[12]. Moreover, since the end of 2022, significant breakthroughs have been achieved by large language models, exemplified by ChatGPT[13]. Therefore, in the digital era, exploring generative artificial intelligence technologies such as ChatGPT offers significant opportunities in the field of medical safety education.

The potential of ChatGPT and generative artificial intelligence

ChatGPT, as a typical representative of generative artificial intelligence technologies, is a chatbot developed by OpenAI that utilizes a pretrained transformer language model known as GPT to comprehend and respond to natural language inputs[14]. Its purpose is to provide answers to various questions across different domains[15]. As a technological advancement in the 5.0 era[16], ChatGPT has been applied in numerous fields[17-20], and the health care sector is no exception[21-25].

The advantages of ChatGPT or generative AI (hereinafter referred to as ChatGPT) in the context of medical safety education can be summarized in terms of the following four aspects. First, ChatGPT possesses basic health care knowledge and the potential to conduct medical safety education. Research has shown that without any specialized training or reinforcement, ChatGPT achieved an accuracy rate of approximately 60% in all three subjects of the United States Medical Licensing Examination[26]. In the field of liver transplantation, ChatGPT can provide high-quality answers to relevant questions, making it a valuable resource for patient education[27].

Second, the anonymity offered by ChatGPT allows for better access to authentic patient information[28]. Due to the fear of stigmatization[29], patients may be reluctant to honestly disclose sensitive personal information related to their conditions[30], even resisting participation in medical safety education. By using ChatGPT as a medium for medical safety education, users' concerns with stigmatization can be minimized, encouraging them to honestly disclose crucial information related to their illnesses and thereby enhancing the effectiveness of medical safety education.

Third, ChatGPT can overcome the limitations of time, space, and language, thereby providing the public with more convenient and efficient pharmaceutical and health care services while maximizing resource utilization. ChatGPT can operate efficiently 24/7[31], significantly saving manpower, resources, and time. Users can easily access ChatGPT with just a few clicks, allowing them to receive medical consultations and answers without leaving their homes.

Finally, ChatGPT has great potential with regard to personalized medication education. It can analyze specific patient data to generate tailored treatment recommendations[32] and offer more personalized medical and health care services and more effective problem-solving approaches. Compared to general health education for the entire public, the use of ChatGPT as a medium for medical safety education based on individuals' medical history, genetic information, and existing knowledge levels is more targeted and can assist users in improving their medical safety knowledge and practical skills more effectively.

The limitations of ChatGPT and generative artificial intelligence

It is worth noting that while generative AI, as represented by ChatGPT, has tremendous potential with regard to conducting medical safety education, its practical application still faces several limitations. First, medical and health care education, as a crucial aspect of the nation's well-being, must possess a high degree of scientific rigor, authority, and accuracy to effectively improve the public's medical knowledge and ensure residents' safety with respect to medication. While ChatGPT has the potential to serve as an information source and can respond actively to users' inquiries, the accuracy and reliability of these responses remain questionable[15,33]. False or erroneous information could have severe negative impacts on public health, even posing threats to people’s lives and safety.

Second, ethical considerations pertaining to the use of ChatGPT must be taken seriously[34,35]. In the medical field, health care professionals have legal responsibilities and are bound by professional ethics to ensure the physical and mental well-being of patients. However, with regard to virtual robots such as ChatGPT, such legal responsibilities and moral obligations have yet to be clearly defined. How can the humanistic care of medicine be adequately reflected in their interactions? How can patients' personal information be properly protected? If accidents occur, how should responsibility for safety be assigned? These questions are all important and require thoughtful consideration.

Finally, the inappropriate use of ChatGPT may pose health risks. As ChatGPT operates through the internet and is accessed via electronic devices such as smartphones and computers, improper usage may not only fail to achieve the goals of medical and health care education but may even backfire. Prolonged screen time or internet addiction may impede individuals' normal physical activities and sleep, causing harm to their psychological and cognitive development[36].

Directions for future research

Given the potential issues pertaining to accuracy, ethical considerations, and health implications in medical safety education, it is essential to implement more robust measures to intelligently upgrade ChatGPT in the future. On the one hand, ChatGPT must be further integrated with real-life scenarios, making full use of electronic aids such as sensors and cameras to engage in real-time interactions with users. By accurately recognizing users' environments and usage patterns, it can provide a more immersive and authentic educational experience, thereby enhancing the accuracy and relevance of medical safety education.

In addition, all stakeholders should work together to establish relevant usage guidelines, industry standards, and regulatory frameworks to further regulate the application of ChatGPT in the medical field. Additionally, a clear delineation of safety and responsibility risks should be provided. This collective effort can contribute substantially to effectively addressing ethical and safety concerns.

The wave of generative artificial intelligence technologies represented by ChatGPT is approaching with great force and seems to be unstoppable. We firmly believe that only by recognizing the strengths and limitations of ChatGPT in medical safety education and by remaining vigilant and striving to optimize its performance can we fully explore the organic integration of education and artificial intelligence. In so doing, we can harness the potential of ChatGPT to empower medical safety education through technology and provide the public with more comprehensive, convenient, efficient, and personalized medical services.

Footnotes

Provenance and peer review: Invited article; Externally peer reviewed.

Peer-review model: Single blind

Specialty type: Medicine, research and experimental

Country/Territory of origin: China

Peer-review report’s scientific quality classification

Grade A (Excellent): 0

Grade B (Very good): 0

Grade C (Good): C

Grade D (Fair): 0

Grade E (Poor): 0

P-Reviewer: Colak C, Turkey S-Editor: Qu XL L-Editor: A P-Editor: Qu XL

References
1.  Liu XT, Wang N, Zhu LQ, Wu YB. Assessment of knowledge, cultural beliefs, and behavior regarding medication safety among residents in Harbin, China. World J Clin Cases. 2023;11:2956-2965.  [PubMed]  [DOI]  [Cited in This Article: ]  [Reference Citation Analysis (0)]
2.  Kivelä K, Elo S, Kyngäs H, Kääriäinen M. The effects of health coaching on adult patients with chronic diseases: a systematic review. Patient Educ Couns. 2014;97:147-157.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 229]  [Cited by in F6Publishing: 217]  [Article Influence: 21.7]  [Reference Citation Analysis (0)]
3.  Cao XJ, Zhang QY, Liu XQ. Cross-Lagged Relationship between Physical Activity Time, Openness and Depression Symptoms among Adolescents: Evidence from China. International Journal of Mental Health Promotion. 2023;.  [PubMed]  [DOI]  [Cited in This Article: ]
4.  Zheng SQ, Yang L, Zhou PX, Li HB, Liu F, Zhao RS. Recommendations and guidance for providing pharmaceutical care services during COVID-19 pandemic: A China perspective. Res Social Adm Pharm. 2021;17:1819-1824.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 106]  [Cited by in F6Publishing: 142]  [Article Influence: 35.5]  [Reference Citation Analysis (0)]
5.  Sacco WP, Malone JI, Morrison AD, Friedman A, Wells K. Effect of a brief, regular telephone intervention by paraprofessionals for type 2 diabetes. J Behav Med. 2009;32:349-359.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 80]  [Cited by in F6Publishing: 87]  [Article Influence: 5.8]  [Reference Citation Analysis (0)]
6.  Vogl L, Teesson M, Andrews G, Bird K, Steadman B, Dillon P. A computerized harm minimization prevention program for alcohol misuse and related harms: randomized controlled trial. Addiction. 2009;104:564-575.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 84]  [Cited by in F6Publishing: 83]  [Article Influence: 5.5]  [Reference Citation Analysis (1)]
7.  Liu XQ, Guo YX, Wang X. Delivering substance use prevention interventions for adolescents in educational settings: A scoping review. World J Psychiatry. 2023;13:409-422.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in F6Publishing: 10]  [Reference Citation Analysis (4)]
8.  Malmberg M, Kleinjan M, Overbeek G, Vermulst A, Monshouwer K, Lammers J, Vollebergh WA, Engels RC. Effectiveness of the 'Healthy School and Drugs' prevention programme on adolescents' substance use: a randomized clustered trial. Addiction. 2014;109:1031-1040.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 27]  [Cited by in F6Publishing: 26]  [Article Influence: 2.6]  [Reference Citation Analysis (0)]
9.  Harrer M, Adam SH, Baumeister H, Cuijpers P, Karyotaki E, Auerbach RP, Kessler RC, Bruffaerts R, Berking M, Ebert DD. Internet interventions for mental health in university students: A systematic review and meta-analysis. Int J Methods Psychiatr Res. 2019;28:e1759.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 169]  [Cited by in F6Publishing: 177]  [Article Influence: 35.4]  [Reference Citation Analysis (0)]
10.  Cao X, Liu X. Time Use and Cognitive Achievement among Adolescents in China: Depression Symptoms as Mediators. J Intell. 2023;11.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in F6Publishing: 10]  [Reference Citation Analysis (0)]
11.  Wijesooriya NR, Mishra V, Brand PLP, Rubin BK. COVID-19 and telehealth, education, and research adaptations. Paediatr Respir Rev. 2020;35:38-42.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 91]  [Cited by in F6Publishing: 121]  [Article Influence: 30.3]  [Reference Citation Analysis (0)]
12.  Medlock S, Eslami S, Askari M, Arts DL, Sent D, de Rooij SE, Abu-Hanna A. Health information-seeking behavior of seniors who use the Internet: a survey. J Med Internet Res. 2015;17:e10.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 134]  [Cited by in F6Publishing: 120]  [Article Influence: 13.3]  [Reference Citation Analysis (0)]
13.  Hassani H, Silva ES. The Role of ChatGPT in Data Science: How AI-Assisted Conversational Interfaces Are Revolutionizing the Field. Big Data and Cognitive Computing. 2023;7.  [PubMed]  [DOI]  [Cited in This Article: ]
14.  Salvagno M, Taccone FS, Gerli AG. Can artificial intelligence help for scientific writing? Crit Care. 2023;27:75.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 165]  [Cited by in F6Publishing: 124]  [Article Influence: 124.0]  [Reference Citation Analysis (0)]
15.  Lahat A, Shachar E, Avidan B, Glicksberg B, Klang E. Evaluating the Utility of a Large Language Model in Answering Common Patients' Gastrointestinal Health-Related Questions: Are We There Yet? Diagnostics (Basel). 2023;13.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in F6Publishing: 17]  [Reference Citation Analysis (0)]
16.  Putra FW, Rangka IB, Aminah S, Aditama MHR. ChatGPT in the higher education environment: perspectives from the theory of high order thinking skills. J Public Health (Oxf). 2023;.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 6]  [Reference Citation Analysis (0)]
17.  Zhu JJ, Jiang J, Yang M, Ren ZJ. ChatGPT and Environmental Research. Environ Sci Technol. 2023;.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 32]  [Cited by in F6Publishing: 12]  [Article Influence: 12.0]  [Reference Citation Analysis (0)]
18.  Du HP, Teng SY, Chen H, Ma JQ, Wang X, Gou C, Li B, Ma SJ, Miao QH, Na XX, Ye PJ, Zhang H, Luo GY, Wang FY. Chat With ChatGPT on Intelligent Vehicles: An IEEE TIV Perspective. IEEE Transactions on Intelligent Vehicles. 2023;8:2020-2026.  [PubMed]  [DOI]  [Cited in This Article: ]
19.  Agathokleous E, Saitanis CJ, Fang C, Yu Z. Use of ChatGPT: What does it mean for biology and environmental science? Sci Total Environ. 2023;888:164154.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in F6Publishing: 5]  [Reference Citation Analysis (0)]
20.  Paul J, Ueno A, Dennis C. ChatGPT and consumers: Benefits, Pitfalls and Future Research Agenda. International Journal of Consumer Studies. 2023;47:1213-1225.  [PubMed]  [DOI]  [Cited in This Article: ]
21.  Duey AH, Nietsch KS, Zaidat B, Ren R, Ndjonko LCM, Shrestha N, Rajjoub R, Ahmed W, Hoang T, Saturno MP, Tang JE, Gallate ZS, Kim JS, Cho SK. Thromboembolic prophylaxis in spine surgery: an analysis of ChatGPT recommendations. Spine J. 2023;23:1684-1691.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in F6Publishing: 4]  [Reference Citation Analysis (0)]
22.  Park I, Joshi AS, Javan R. Potential role of ChatGPT in clinical otolaryngology explained by ChatGPT. Am J Otolaryngol. 2023;44:103873.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 8]  [Cited by in F6Publishing: 11]  [Article Influence: 11.0]  [Reference Citation Analysis (0)]
23.  Gebrael G, Sahu KK, Chigarira B, Tripathi N, Mathew Thomas V, Sayegh N, Maughan BL, Agarwal N, Swami U, Li H. Enhancing Triage Efficiency and Accuracy in Emergency Rooms for Patients with Metastatic Prostate Cancer: A Retrospective Analysis of Artificial Intelligence-Assisted Triage Using ChatGPT 4.0. Cancers (Basel). 2023;15.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in F6Publishing: 4]  [Reference Citation Analysis (0)]
24.  Jin JQ, Dobry AS. ChatGPT for healthcare providers and patients: Practical implications within dermatology. J Am Acad Dermatol. 2023;89:870-871.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 2]  [Reference Citation Analysis (0)]
25.  Moazzam Z, Lima HA, Endo Y, Noria S, Needleman B, Pawlik TM. A Paradigm Shift: Online Artificial Intelligence Platforms as an Informational Resource in Bariatric Surgery. Obes Surg. 2023;33:2611-2614.  [PubMed]  [DOI]  [Cited in This Article: ]  [Reference Citation Analysis (0)]
26.  Kung TH, Cheatham M, Medenilla A, Sillos C, De Leon L, Elepaño C, Madriaga M, Aggabao R, Diaz-Candido G, Maningo J, Tseng V. Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models. PLOS Digit Health. 2023;2:e0000198.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 4]  [Cited by in F6Publishing: 638]  [Article Influence: 638.0]  [Reference Citation Analysis (0)]
27.  Endo Y, Sasaki K, Moazzam Z, Lima HA, Schenk A, Limkemann A, Washburn K, Pawlik TM. Quality of ChatGPT Responses to Questions Related To Liver Transplantation. J Gastrointest Surg. 2023;27:1716-1719.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in F6Publishing: 2]  [Reference Citation Analysis (0)]
28.  Lucas G, Gratch J, King A, Morency L. It’s only a computer: virtual humans increase willingness to disclose. Comp Hum Behav. 2014;37:94-100.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 337]  [Cited by in F6Publishing: 352]  [Article Influence: 35.2]  [Reference Citation Analysis (0)]
29.  Bhanot D, Singh T, Verma SK, Sharad S. Stigma and Discrimination During COVID-19 Pandemic. Front Public Health. 2020;8:577018.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 203]  [Cited by in F6Publishing: 151]  [Article Influence: 50.3]  [Reference Citation Analysis (0)]
30.  Khan R. Garman EC, Sorsdahl K. Perspectives on Self-Disclosure of HIV Status among HIV-Infected Adolescents in Harare, Zimbabwe: A Qualitative Study. J Child Fam Stud. 2023;.  [PubMed]  [DOI]  [Cited in This Article: ]
31.  Palanica A, Flaschner P, Thommandram A, Li M, Fossat Y. Physicians' Perceptions of Chatbots in Health Care: Cross-Sectional Web-Based Survey. J Med Internet Res. 2019;21:e12887.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 108]  [Cited by in F6Publishing: 111]  [Article Influence: 22.2]  [Reference Citation Analysis (0)]
32.  Liu J, Wang C, Liu S. Utility of ChatGPT in Clinical Practice. J Med Internet Res. 2023;25:e48568.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 24]  [Cited by in F6Publishing: 41]  [Article Influence: 41.0]  [Reference Citation Analysis (0)]
33.  Wu TY, He SZ, Liu JP, Sun SQ, Liu K, Han QL, Tang y. A Brief Overview of ChatGPT: The History, Status Quo and Potential Future Development. IEEE/CAA Journal of Automatica Sinica. 2023;10:1122-1136.  [PubMed]  [DOI]  [Cited in This Article: ]
34.  Praveen SV, Lohia R. What do psychiatry researchers feel about ChatGPT? A study based on Natural Language Processing techniques. Asian J Psychiatr. 2023;85:103626.  [PubMed]  [DOI]  [Cited in This Article: ]  [Reference Citation Analysis (0)]
35.  Thornton J, D'Souza R, Tandon R. Artificial intelligence and psychiatry research and practice. Asian J Psychiatr. 2023;81:103509.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in F6Publishing: 23]  [Reference Citation Analysis (0)]
36.  Cao X. Sleep Time and Depression Symptoms as Predictors of Cognitive Development Among Adolescents: A Cross-Lagged Study From China. Psychol Rep. 2023;332941231175833.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in F6Publishing: 5]  [Reference Citation Analysis (0)]