Abstract
Artificial intelligence (AI) has emerged as a transformative force in various sectors, including medicine and healthcare. Large language models like ChatGPT showcase AI’s potential by generating human-like text through prompts. ChatGPT’s adaptability holds promise for reshaping medical practices, improving patient care, and enhancing interactions among healthcare professionals, patients, and data. In pandemic management, ChatGPT rapidly disseminates vital information. It serves as a virtual assistant in surgical consultations, aids dental practices, simplifies medical education, and aids in disease diagnosis. A total of 82 papers were categorised into eight major areas, which are G1: treatment and medicine, G2: buildings and equipment, G3: parts of the human body and areas of the disease, G4: patients, G5: citizens, G6: cellular imaging, radiology, pulse and medical images, G7: doctors and nurses, and G8: tools, devices and administration. Balancing AI’s role with human judgment remains a challenge. A systematic literature review using the PRISMA approach explored AI’s transformative potential in healthcare, highlighting ChatGPT’s versatile applications, limitations, motivation, and challenges. In conclusion, ChatGPT’s diverse medical applications demonstrate its potential for innovation, serving as a valuable resource for students, academics, and researchers in healthcare. Additionally, this study serves as a guide, assisting students, academics, and researchers in the field of medicine and healthcare alike.
Keywords:
ChatGPT; cellular imaging; medicine; healthcare; image; dental; disease; radiology and sonar; pharmaceutical 1. Introduction
Artificial intelligence (AI) has emerged as a powerful tool with transformative potential across various sectors, and the field of medicine and healthcare is no exception. One remarkable application of AI in this realm is the development of large language models, such as ChatGPT, which have gained significant attention for their ability to generate human-like text based on prompts. ChatGPT’s versatile capabilities hold promise for reshaping medical practices, enhancing patient care, and revolutionising the way healthcare professionals interact with both patients and data [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32].
OpenAI launched in last November 2022 the Chat Generative Pre-trained Transformer (ChatGPT) and revolutionised the approach in artificial intelligence to human–model interaction used for fields [4]. It was used in all areas: healthcare and management [5,6,7,8,9], aiding cosmetic orthognathic surgery consultation [10], revolutionising dental practices [8,11,12], medical scientific articles and education fields [6,9,13,14,15,16,17,18,19,20,21,22,23], support in disease diagnosis [24], revolutionising radiology, sonar imaging and writing assessment [13,25,26,27,28,29,30,31,32,33], pharmaceutical research and treatment [11,24,34,35,36,37,38], and navigating limitations and ethical considerations [18,19,31,35,38,39,40,41]. Addressing the open issues in AI applications, such as ChatGPT, particularly for medical use, involves the tackling of model interpretability. This is carried out to make the AI’s decision making transparent, especially in complex medical scenarios. Equally critical is the combatting of data bias in order to prevent healthcare disparities. There is also a pressing need for mechanisms that allow for continuous learning, which enables ChatGPT to stay abreast of the latest medical research and guidelines. Moreover, the management of integrating ChatGPT with existing healthcare IT ecosystems is crucial to guarantee seamless operation. Lastly, it is essential to strictly adhere to ethical and legal standards, with a focus on maintaining trust and compliance in healthcare applications through patient confidentiality and informed consent.
The innovative aspects of ChatGPT’s deployment in medicine include a variety of unique applications, such as assisting in the identification of rare diseases and providing support for men’s health, demonstrating the model’s versatility. Advanced methodologies are at the heart of its training and effectiveness evaluation, which are tailored specifically for healthcare contexts to ensure relevance and dependability. The work is fundamentally interdisciplinary, as it merges AI with expert medical insights in order to effectively tackle significant healthcare challenges. The aim of this synergy is to translate into tangible benefits, which include enhanced patient outcomes and streamlined healthcare processes. This, in turn, marks a substantial real-world impact on the medical field. The areas of ChatGPT’s application in medical fields and healthcare are as follows.
1.1. Aiding Cosmetic Orthognathic Surgery Consultations
Within the realm of surgical procedures, such as cosmetic orthognathic surgery, ChatGPT can function as a virtual assistant, offering patients crucial preoperative information. Potential candidates who are considering cosmetic orthognathic surgery may be seeking information regarding the procedure, recovery, risks, and benefits. ChatGPT can offer standardised and accurate responses, which prepare patients for their consultations and help them make informed decisions [5,6,7,8,9,10,42,43].
1.2. Enhancing Medical Education
Medical education can benefit significantly from AI-driven tools like ChatGPT. Medical students and professionals can engage with ChatGPT to access quick references, clarify doubts, and explore complex medical concepts. Its ability to explain intricate medical terminology in a comprehensible manner aids in knowledge acquisition, fostering continuous learning and improving medical literacy [6,9,13,14,15,16,17,18,19,20,21,22,23].
1.3. Support in Disease Diagnosis
The potential of ChatGPT as a diagnostic tool holds promise in the early detection of diseases. ChatGPT can generate potential differential diagnoses by analysing patient-reported symptoms and medical history, thereby aiding healthcare providers in narrowing down diagnostic possibilities. However, caution must be exercised as diagnosis requires domain-specific expertise [24].
1.4. Cellular Imaging, Revolutionizing Radiology, and Sonar Imaging
Cellular imaging pertains to the utilisation of diverse techniques and technologies for visualising and studying cells at the microscopic level. Scientists and researchers are allowed to examine the structure, function, and behaviour of individual cells or cell populations. Cellular imaging techniques comprise light microscopy, fluorescence microscopy, confocal microscopy, electron microscopy, and various other advanced imaging methods. These techniques are widely used in various fields, such as biology, medicine, and biotechnology, for the purpose of better understanding cellular processes, cell interactions, and disease mechanisms.
The interpretation of medical images, such as radiology and sonar scans, requires precision and accuracy. The potential of ChatGPT lies in its ability to analyse and generate descriptions for medical images, which can enhance the workflow of radiologists. It has the ability to provide initial observations, which can highlight regions of interest and assist radiologists in their analyses [13,25,26,27,28,29,30,31,32,33].
1.5. Pharmaceutical Research and Treatment
In the realm of pharmaceutical research, ChatGPT can contribute by sifting through vast volumes of scientific literature, identifying potential drug candidates, and suggesting innovative research directions. It can assist in drug discovery, research proposal writing, and summarising complex medical research, expediting the research process [11,24,34,35,36,37,38].
1.6. Navigating Limitations and Ethical Considerations
While ChatGPT offers numerous opportunities, it also poses certain limitations. The responses are based on the data on which it was trained, potentially resulting in biased or inaccurate information. Ethical concerns arise when content generated by AI is mistaken for the expertise of a human. The challenge of balancing the role of AI with the need for human judgement and expertise remains [18,19,27,31,35,38,39,40,41], as depicted in Figure 1.
Figure 1.
Categories and groups covered by all studies of the review. G1: treatment and medicine, G2: buildings and equipment, G3: parts of the human body and areas of the disease, G4: patients, G5: citizens, G6: cellular imaging, radiology, pulse, and medical images, G7: doctors and nurses, and G8: tools, devices and administration.
Johnson, D et al. [5] utilised the accuracy and completeness of ChatGPT in answering medical queries by academic physician specialists. The results generally show accurate information with limitations, which suggests a need for further research and model development. Mohammad H et al. [13] conducted a hybrid panel discussion that focused on the integration of ChatGPT, a large language model, in the fields of education, research, and healthcare. The event gathered responses from attendees, both in-person and online, by utilising an audience interaction platform. According to the study, approximately 40% of the participants had utilised ChatGPT, with a higher number of trainees compared to faculty members having experimented with it. Those individuals who had utilised ChatGPT demonstrated a heightened level of interest in its potential application across a multitude of contexts. Uncertainty was observed regarding its use in education, with pros and cons being discussed for its integration in education, research, and healthcare. The perspectives varied according to role (trainee, faculty, and staff), highlighting the need for further discussion and exploration of the implications and optimal uses of ChatGPT in these sectors. The study emphasises the significance of taking a deliberate and measured approach to adoption in order to mitigate potential risks and challenges. Sallam et al. [44] assessed the advantages and disadvantages of ChatGPT in healthcare education. While it is beneficial for learning, it lacks emotional interaction and poses plagiarism risks.
In conclusion, the integration of ChatGPT into medicine and healthcare holds significant potential to transform various aspects of patient care, education, diagnosis, and research. As technology continues its advancement, it becomes crucial to responsibly harness the capabilities of ChatGPT, while also addressing its limitations and ethical considerations. The exploration of ChatGPT’s applications across diverse medical domains emphasises its role as a catalyst for innovation and improvement in the medical landscape.
The main objectives of the study can be succinctly summarised as follows:
- To investigate the various applications of ChatGPT in healthcare, such as pandemic management, surgical consultations, dental practices, medical education, disease diagnosis, cellular imaging, sonar imaging, radiology, and pharmaceutical research.
- To investigate the potential benefits and risks of integrating ChatGPT in healthcare, assessing its impact on patient care, medical processes, and ethical considerations.
- To investigate the ethical issues and the need to balance AI’s role in healthcare settings with human judgement.
- To offer perspectives and recommendations for the responsible adoption and use of ChatGPT in medicine in general, and cellular imaging in particular, while addressing limitations and ethical concerns.
2. Materials and Methods
The field of this study is covered by very important query words (keywords), namely, ChatGPT with “Medicine”, “Healthcare”, “Tackles”, “Pandemics/Infectious”, “A Cosmetic orthognathic surgery Consultation”, “dental Medical/Education”, “Disease, Radiology”, “sonar”, “pharmaceutical”, “Treatment”, “Diagnosis” and “patient care”, “Medical school”, “Ophthalmology”, “Digital Health”, and “operating room”. Our study is closed to English-language studies only. The following digital databases and publishers were selected to search for target papers, and the numbers of articles included the following form: Taylor and Francis (31) article, Google Scholar (179) article, Scopus (412) article, Web of Science (239) article, Elsevier (127) article, Springer (218) article, MDPI (41) article, IEEE Xplore digital (18) article, and Wiley (7) article, as shown in Figure 2.
Figure 2.
No. of databases/publishers.
The studies were chosen by conducting literature searches, which were then followed by three rounds of screening and filtering. In the initial iteration, using Mendeley software (1.19.4-win32), only publications that were published in the last eight months were gathered after eliminating duplicate articles.
As part of this investigation, a systematic literature review was conducted using the PRISMA methodology (Supplementary Materials) [40,45,46,47,48,49,50]. We included all types of articles: original research articles, review articles, meta-analysis and systematic reviews, case studies, editorials and opinion articles, letters to the editor, perspective or commentary articles, short communications or brief reports, technical notes, research protocols, book reviews, clinical trials and observational studies, and short reports; the numbers included the following form (39, 12, 5, 2, 1, 6, 1, 4, 1, 1, 3, 2, and 5), respectively, as shown in Figure 3.
Figure 3.
Distribution by type of articles to number of articles.
2.1. Inclusion Criteria
- The article should be original or reviewed, written in English, and published in English journals, conferences, case studies, editorials, opinion articles, and letters to the editor.
- The study specifies that it contains the specified keywords.
- Any relevant articles are published or in press between November 2022 and August 2023.
- The main point is on Chat GPT with specific words in the field of healthcare and health.
- The study includes the study’s objectives, design, applications, benefits, risks, concerns, limitations, study field/area, conclusions, and recommended actions.
The focus is on the pros and cons, challenges, as well as all the inclusion criteria shown in Figure 4.
Figure 4.
Flowchart for selecting studies with specific query and eligibility criteria.
2.2. Data Gathering Procedure
All included articles were reviewed, examined, and summarised in accordance with their basic classifications, then saved as Microsoft Word and Excel files to simplify the filtering process. For every piece, the authors read the complete text. We were able to develop the proposed taxonomy using a variety of highlights and comments on the surveyed works, as well as a running classification of all the articles. The remarks were recorded in either paper copy or electronic form, depending on the writing style of each contributor. After this, another procedure was conducted to characterise, describe, tabulate, and draw conclusions about the key findings.
2.3. Research Questions
The selection of research questions is a critical step in shaping the purpose of the study and the anticipated results. Consequently, we have formulated the following research questions to align with the primary objective of our systematic literature review:
- RQ1: How does ChatGPT contribute to pandemic management and what specific advantages does it offer in disseminating critical information during health crises?
- RQ2: How is ChatGPT utilised in the field of dental practices and how does it enhance the overall patient experience in this context?
- RQ3: What challenges and ethical considerations are associated with the integration of ChatGPT into medical practices and healthcare settings?
- RQ4: What are the key components associated with work related to ChatGPT in medicine and healthcare and their contributions in ChatGPT applications in the field?
3. Results
The results of the initial query search, which yielded 1273 articles, are as follows: there are 31 articles from Taylor and Francis, 179 articles from Google Scholar, 412 articles from Scopus, 239 articles from Web of Science, 127 articles from Elsevier, 218 articles from Springer, 41 articles from MDPI, 18 articles from IEEE Xplore digital, and 7 articles from Wiley from between November 2022 and August 2023.
The papers were filtered according to the sequence that was adopted in this research and were divided into two categories: 1273 articles were published in the last eight months (November 2022 to August 2023) and 392 papers appeared in all nine databases or publishers, resulting in a total of 1547 papers. Following a comprehensive scan of the titles and abstracts of the papers, an additional 1273 papers were excluded. After the final full-text reading, 92 papers were excluded. The final set consisted of 82 papers, which were divided into eight major categories/groups as follows: G1: treatment and medicine are essential; G2: buildings and equipment play a crucial role; G3: parts of the human body and areas of disease; G4: patients; G5: citizens; G6: the focus is on radiology, pulse, and medical images; G7: revolves around doctors and nurses; and G8: encompasses tools, devices, and administration.
The first category (G1), which comprised 27 articles (32.93%), was focused on treatment and medicine. The second category (G2) consisted of two articles (2.44%) that were focused on buildings and equipment. The third category, known as G3, comprised 14 articles, accounting for 17.07% of the total. These articles focused on parts of the human body and areas affected by disease. The fourth category (G4), which comprised eight articles (9.76%), focused on patients. The fifth category (G5), which comprised three articles (3.66%), was citizens. The sixth category (G6) comprised four articles (4.88%) on cellular imaging, radiology, pulse, and medical images. The seventh category, known as G7, consisted of 17 articles, accounting for 20.73% of the total. This category focused on doctors and nurses. The eighth category, known as G8, consisted of seven articles, accounting for 8.54% of the total. This category focused on tools, devices, and administration.
3.1. RQ1: How Does ChatGPT Contribute to Pandemic Management and What Specific Advantages Does It Offer in Disseminating Critical Information during Health Crises?
ChatGPT plays a crucial role in pandemic management by aiding in the swift and accurate dissemination of critical information. Its natural language processing capabilities enable it to generate coherent responses, making it a valuable tool for healthcare organisations. Specific advantages include its ability to provide rapid updates, preventive measures, and medical guidelines to the public. ChatGPT assists in addressing queries and concerns, ensuring accurate information flow and timely interventions during health crises [5,6,7,8,9,42,43].
3.2. RQ2: How Is ChatGPT Utilised in the Field of Dental Practices, and How Does It Enhance the Overall Patient Experience in This Context?
ChatGPT is integrated into dental practices in order to empower dental assistants and improve the overall patient experience. It assists in the handling of patient inquiries, the provision of oral health tips, and the guidance of patients through postoperative care instructions. ChatGPT ensures standardised and accurate responses, thereby assisting patients in making informed decisions regarding their dental care. This AI integration revolutionises patient communication, appointment scheduling, and postoperative support in dental practices [8,11,12].
3.3. RQ3: What Challenges and Ethical Considerations Are Associated with the Integration of ChatGPT into Medical Practices and Healthcare Settings?
The integration of ChatGPT into medical practices and healthcare settings presents challenges and ethical considerations. One challenge is that the responses of ChatGPT are based on the data on which it was trained, which may result in biased or inaccurate information. Ethical concerns arise when content generated by AI is mistaken for the expertise of a human. The challenge lies in striking a balance between the capabilities of AI and the necessity for human judgement and expertise. Ensuring that AI does not replace human healthcare professionals entirely while leveraging its advantages is a significant ethical consideration [18,19,27,31,35,38,39,40,41]. Careful thought and responsible adoption are essential to mitigate potential risks and challenges.
3.4. RQ4: What Are the Key Components Associated with Work Related to ChatGPT in Medicine and Healthcare and Their Contributions in ChatGPT Applications in the Field?
The findings of our studies are summarised in Table 1, which includes information about the purpose of the study (in the “aim of study” column), the design and application (in the “design, application(s)” column), the benefits and risks (in the “benefit(s), risks” column), the summary of results for concerns and limitations (in the “concern(s), limitation(s)” column), the main outcomes, the type of study (in the “study field/area” column), and the main outcomes reported by each article in the conclusion section (in the “Suggested Action” column). Table 2 summarises the pros and cons or challenges of some of the papers that were exposed.
Table 1.
The summary of related works includes the study’s goal, design, application(s), benefit(s), risk(s), concern(s), limitation(s), study field/area, conclusion(s), and suggested action for ChatGPT with medicine and healthcare.
Table 2.
Summary of associated works includes all fields of study, study aim, pros, and cons or challenges.
4. Challenges
In the field of healthcare and the medical field, refer to the difficult or complex issues, obstacles, or problems that healthcare professionals, researchers, organisations, and technologies face while providing medical care, conducting research, and addressing public health concerns. These challenges can arise due to various factors such as scientific advancements, technological limitations, ethical considerations, regulatory frameworks, economic constraints, patient expectations, and more.
In the context of healthcare and the medical field, challenges can encompass a wide range of issues, including but not limited to medical advancements, patient care, resource allocation, healthcare access, disease prevention and control, chronic disease management, healthcare costs, medical ethics, data privacy and security, protecting patient data and ensuring compliance with privacy regulations, interdisciplinary collaboration, patient education, and public health initiatives. In Figure 5, there were numerous challenges that Chatbot models such as GPT-3 could face in the field of healthcare and medicine.
Figure 5.
Challenges of healthcare and the medical fields.
4.1. Language Understanding and Medical Terms
Medical professionals frequently use specialised terminology and jargon that may be difficult for a general-purpose chatbot to understand. It is difficult to ensure that the chatbot understands medical terminology correctly.
Importance: this paper can illustrate the sophistication needed in natural language processing for medical contexts, underscoring the potential of advanced AI models to bridge communication gaps in healthcare.
Benefits: readers will discover the critical role of contextually aware AI in comprehending patient interactions, resulting in better patient support and care.
4.2. Accuracy and Reliability
Of utmost importance in healthcare is ensuring that the information provided by the chatbot is accurate and reliable. Medical information, which can be complex and critical, must be accurate to avoid misinformation and potential harm to patients.
Importance: this paper’s contribution to the ongoing conversation about the reliability of AI in clinical settings is highlighted, presenting the chatbot as a tool to augment, rather than replace, human judgement.
Benefits: readers can derive advantages from comprehending the significance of error-checking mechanisms and the ongoing updating of medical databases that AI systems must integrate.
4.3. Privacy and Security
Healthcare data are highly sensitive and subject to strict privacy regulations (like HIPAA in the United States). Chatbots need to adhere to these regulations and ensure that patient data are handled securely and confidentially.
Importance: the stress is on the advanced security protocols and compliance standards that AI systems must adhere to, as they are a cornerstone of healthcare technology.
Benefits: this paper educates readers on the stringent data protection measures required for AI integration in healthcare, with an emphasis on the technology’s potential to maintain confidentiality.
4.4. Accountability and Responsibility
Decisions made by chatbots in healthcare settings can indeed have real-life consequences. Determining who is accountable for incorrect advice or recommendations provided by a chatbot can be a complex matter.
Importance: enriching the discourse on the role of AI in healthcare decision making by examining the legal and ethical frameworks that regulate AI.
Benefits: the survey can educate readers on the complex interaction between AI recommendations and human decision making, as well as the legal implications.
4.5. Human Oversight and Intervention
Although chatbots can assist in various healthcare tasks, there is a requirement for human oversight and intervention, particularly in critical situations. The challenge lies in balancing automation with the human touch.
Importance: the need for human experts to oversee AI systems is underlined, reinforcing the notion of AI as a supportive tool rather than a replacement.
Benefits: healthcare professionals can learn about the importance of their experience in supervising AI, ensuring patient safety and providing high-quality care.
4.6. Medical Training and Regulation
Chatbots that provide medical advice may unintentionally bypass the regulatory frameworks established for traditional healthcare providers. It is crucial to ensure that chatbots are developed and used within established medical guidelines.
Importance: address the incorporation of AI within existing medical regulations, emphasising how the paper advances the conversation about regulatory adaptations for AI tools.
Benefits: this paper can help medical professionals understand the importance of regulatory compliance for AI, as well as encourage proactive engagement with technology.
4.7. Patient Empowerment
While chatbots can provide information, they should also encourage patients to consult qualified healthcare professionals for personalised advice. It is critical to strike a balance between empowerment and not substituting professional medical care.
Importance: it highlights the chatbot’s contribution to promoting well-informed patient decision making, underscoring the paper’s adherence to patient-centred care models.
Benefits: readers will appreciate how AI can provide information to patients while also understanding AI’s limitations in providing personalised medical advice.
5. Limitations and the Motivation
The limitations of these studies include a tendency to focus on theoretical potential rather than practical implementation, which leaves some real-world challenges unaddressed. Furthermore, significant concerns persist regarding the ethical and legal issues surrounding the roles, accuracy, and originality of AI. There exists a necessity for more comprehensive evaluations of content generated by AI and its impact on tasks performed by humans. Additionally, there is a potential risk of overreliance, which could result in a decrease in human critical thinking and involvement. Current studies frequently lack a thorough exploration of the broader implications, which necessitates adopting a more holistic approach to comprehending the complete scope and consequences of integrating AI, such as ChatGPT, into diverse fields.
The integration of ChatGPT in medicine and healthcare, though promising, does come with certain limitations. One key concern is the potential for inaccuracies in medical information provided by ChatGPT, as it lacks the ability to fully comprehend complex medical contexts. Serious medical errors could occur if patient queries are misinterpreted or incorrect diagnoses are generated. Furthermore, the reliance of ChatGPT on training data may introduce biases, which could potentially impact the quality and fairness of the provided information. Ethical challenges arise regarding patient data privacy and consent, as well as accountability for AI-generated medical content. The sharing of outdated or obsolete medical information could be a potential consequence of the lack of real-time updates and dynamic learning in ChatGPT. Lastly, there is a risk of overdependence on AI, which may diminish the role of healthcare professionals, thereby reducing critical thinking and human interaction in medical care.
The motivation for integrating ChatGPT with medicine and healthcare stems from the potential to improve patient care, medical research, and clinical decision-making processes. ChatGPT’s natural language processing capabilities provide a user-friendly interface for patients to seek medical information, resulting in increased patient engagement and empowerment. Its ability to analyse large amounts of medical literature assists healthcare professionals in staying up to date on the latest research and treatment options. ChatGPT can assist in the generation of accurate and concise medical documentation, thereby streamlining administrative tasks for healthcare providers. In addition, it holds promise in facilitating medical education, supporting remote consultations, and optimising clinical workflows. In general, the integration of ChatGPT with medicine and healthcare is in line with the objective of utilising AI technology to progress medical practices and improve patient outcomes.
6. Conclusions
As part of this investigation, a PRISMA-based systematic literature review was conducted. AI has the potential to transform many industries, including medicine and healthcare. Large language models, such as ChatGPT, have attracted attention for their ability to generate human-like text, revolutionising the healthcare landscape. This paper investigated ChatGPT’s applications in medicine, including pandemic management, surgical consultations such as cosmetic orthognathic surgery, dental practices, medical education, disease diagnosis, radiology and sonar imaging, pharmaceutical research, and treatment. The use of ChatGPT in disseminating critical information during pandemics and infectious diseases has showcased its ability to efficiently communicate essential updates and guidelines to the public. Furthermore, ChatGPT demonstrated its role as a virtual assistant in the context of cosmetic orthognathic surgery consultations. It provided standardised information and facilitated informed patient decisions. The integration of AI into dental practices has enhanced patient experiences by efficiently addressing inquiries and providing guidance.
The ability of ChatGPT to simplify complex medical concepts has enhanced medical education, leading to improved knowledge acquisition. It also displayed potential in disease diagnosis by offering differential diagnoses based on patient-reported symptoms and medical history. In the fields of cellular imaging, radiology, and sonar imaging, the descriptive capabilities of ChatGPT could be helpful for radiologists in the analysis of medical images and cellular imaging. Pharmaceutical research, benefiting from ChatGPT, was another realm that expedited research processes. It accomplished this by sifting through scientific literature and suggesting research directions. However, ChatGPT offers immense potential; however, it also has limitations that stem from its training data and ethical concerns that are related to its output. In conclusion, the integration of ChatGPT into medicine and healthcare holds vast transformative potential. It is crucial to responsibly utilise the capabilities of ChatGPT as technology advances, while also addressing its limitations and ethical considerations. The role of ChatGPT as a catalyst for innovation, advancement, and improved patient care within the medical landscape is underscored by this exploration.
Future work for ChatGPT in AI for healthcare includes the enhancement of diagnostic precision, the personalisation of care, the improvement of EHR integration, the assurance of regulatory compliance, the expansion of telemedicine capabilities, the advancement of medical education tools, and addressing ethical considerations in patient AI interactions.
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/diagnostics14010109/s1, Figure S1: PRISMA 2020 flow diagram for updated systematic reviews which included searches of databases and registers only; File S1: PRISMA 2020 checklist.
Author Contributions
Conceptualization, H.A.Y. (Hussain A. Younis), T.A.E.E. and I.M.H.; methodology, M.N., H.A.Y. (Hussain A. Younis) and T.M.S.; software, H.A.Y. (Hameed AbdulKareem Younis) and O.M.A.; writing—original draft, A.A.N., H.A.Y. (Hussain A. Younis), I.M.H. and H.A.Y. (Hameed AbdulKareem Younis); writing—review and editing, I.M.H., T.M.S., I.M.H., M.N., T.A.E.E. and S.S.; supervision, H.A.Y. (Hussain A. Younis) and T.A.E.E.; project administration, A.A.N., M.N., O.M.A. and T.M.S.; funding acquisition, S.S. and M.N. All authors have read and agreed to the published version of the manuscript.
Funding
The Deanship of Scientific Research at King Khalid University funded this work through a large-group research project under grant number (RGP2/52/44).
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
Not applicable.
Acknowledgments
The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through a large group Research Project under grant number (RGP2/52/44).
Conflicts of Interest
The authors declare no conflict of interest.
References
- Agathokleous, E.; Saitanis, C.J.; Fang, C.; Yu, Z. Use of ChatGPT: What Does It Mean for Biology and Environmental Science? Sci. Total Environ. 2023, 888, 164154. [Google Scholar] [CrossRef]
- McGowan, A.; Gui, Y.; Dobbs, M.; Shuster, S.; Cotter, M.; Selloni, A.; Goodman, M.; Srivastava, A.; Cecchi, G.A.; Corcoran, C.M. ChatGPT and Bard Exhibit Spontaneous Citation Fabrication during Psychiatry Literature Search. Psychiatry Res. 2023, 326, 115334. [Google Scholar] [CrossRef]
- Choudhary, O.P. Priyanka ChatGPT in Travel Medicine: A Friend or Foe? Travel Med. Infect. Dis. 2023, 54, 102615. [Google Scholar] [CrossRef] [PubMed]
- Kocoń, J.; Cichecki, I.; Kaszyca, O.; Kochanek, M.; Szydło, D.; Baran, J.; Bielaniewicz, J.; Gruza, M.; Janz, A.; Kanclerz, K.; et al. ChatGPT: Jack of All Trades, Master of None. Inf. Fusion 2023, 99, 101861. [Google Scholar] [CrossRef]
- Mueen Sahib, T.; Younis, H.A.; Mohammed, A.O.; Ali, A.H.; Salisu, S.; Noore, A.A.; Hayder, I.M.; Shahid, M. ChatGPT in Waste Management: Is it a Profitable. Mesopotamian J. Big Data 2023, 2023, 107–109. [Google Scholar] [CrossRef]
- Sallam, M. ChatGPT Utility in Healthcare Education, Research, and Practice: Systematic Review on the Promising Perspectives and Valid Concerns. Healthcare 2023, 11, 887. [Google Scholar] [CrossRef] [PubMed]
- Jeblick, K.; Schachtner, B.; Dexl, J.; Mittermeier, A.; Stüber, A.T.; Topalis, J.; Weber, T.; Wesp, P.; Sabel, B.; Ricke, J.; et al. ChatGPT Makes Medicine Easy to Swallow: An Exploratory Case Study on Simplified Radiology Reports. Eur. Radiol. 2022; ahead of print. [Google Scholar] [CrossRef]
- Fatani, B. ChatGPT for Future Medical and Dental Research. Cureus 2023, 15, e37285. [Google Scholar] [CrossRef] [PubMed]
- Temsah, O.; Khan, S.A.; Chaiah, Y.; Senjab, A.; Alhasan, K.; Jamal, A.; Aljamaan, F.; Malki, K.H.; Halwani, R.; Al-Tawfiq, J.A.; et al. Overview of Early ChatGPT’s Presence in Medical Literature: Insights from a Hybrid Literature Review by ChatGPT and Human Experts. Cureus 2023, 15, e37281. [Google Scholar] [CrossRef]
- Xie, Y.; Seth, I.; Hunter-smith, D.J.; Rozen, W.M.; Ross, R.; Lee, M. Aesthetic Surgery Advice and Counseling from Artificial Intelligence: A Rhinoplasty Consultation with ChatGPT. Aesthetic Plast. Surg. 2023, 47, 1985–1993. [Google Scholar] [CrossRef]
- Surovkov, J.; Strunga, M.; Lifkov, M.; Thurzo, A. The New Role of the Dental Assistant and Nurse in the Age of Advanced Artificial Intelligence in Telehealth Orthodontic Care with Dental Monitoring: Preliminary Report. Appl. Sci. 2023, 13, 5212. [Google Scholar] [CrossRef]
- Mijwil, M.; Aljanabi, M.; Ali, A.H. ChatGPT: Exploring the Role of Cybersecurity in the Protection of Medical Information. Mesopotamian J. Cyber Secur. 2023, 2023, 18–21. [Google Scholar] [CrossRef]
- Hosseini, M.; Gao, C.A.; Liebovitz, D.; Carvalho, A.; Ahmad, F.S.; Luo, Y.; MacDonald, N.; Holmes, A.K. An Exploratory Survey about Using ChatGPT in Education, Healthcare, and Research. PLoS ONE 2023, 18, e0292216. [Google Scholar] [CrossRef]
- Mohammed, A.O.; Salisu, S.A.; Younis, H.; Salman, A.M.; Sahib, T.M.; Akhtom, D.; Hayder, I.M. ChatGPT Revisited: Using ChatGPT-4 for Finding References and Editing Language in Medical Scientific Articles. 2023. Available online: https://ssrn.com/abstract=4621581 (accessed on 18 November 2023). [CrossRef]
- Khairatun, H.U.; Miftahul, A.M. ChatGPT and Medical Education: A Double-Edged Sword. J. Pedagog. Educ. Sci. 2023, 2, 71–89. [Google Scholar] [CrossRef]
- Abouammoh, N.; Alhasan, K.A.; Raina, R.; Children, A.; Aljamaan, F. Exploring Perceptions and Experiences of ChatGPT in Medical Education: A Qualitative Study Among Medical College Faculty and Students in Saudi Arabia Original Research: Exploring Perceptions and Experiences of ChatGPT in Medical Education: A Qualitativ. Cold Spring Harb. Lab. 2023; preprint. [Google Scholar] [CrossRef]
- Gilson, A.; Safranek, C.W.; Huang, T.; Socrates, V.; Chi, L.; Taylor, R.A.; Chartash, D. How Does ChatGPT Perform on the United States Medical Licensing Examination? The Implications of Large Language Models for Medical Education and Knowledge Assessment. JMIR Med. Educ. 2023, 9, e45312. [Google Scholar] [CrossRef]
- Busch, F.; Adams, L.C.; Bressem, K.K. Biomedical Ethical Aspects Towards the Implementation of Artificial Intelligence in Medical Education in Medical Education. Med. Sci. Educ. 2023, 33, 1007–1012. [Google Scholar] [CrossRef] [PubMed]
- Friederichs, H.; Friederichs, W.J.; März, M.; Friederichs, H.; Friederichs, W.J.; Chatgpt, M.M.; Friederichs, H.; Friederichs, W.J. ChatGPT in Medical School: How Successful Is AI in Progress Testing? ChatGPT in Medical School: How Successful Is AI in Progress Testing? Med. Educ. Online 2023, 28, 2220920. [Google Scholar] [CrossRef] [PubMed]
- Grabb, D. ChatGPT in Medical Education: A Paradigm Shift or a Dangerous Tool? Acad. Psychiatry 2023, 47, 439–440. [Google Scholar] [CrossRef]
- Sedaghat, S. Early Applications of ChatGPT in Medical Practice, Education and Research. Clin. Med. 2023, 23, 278–279. [Google Scholar] [CrossRef]
- Giannos, P. Evaluating the Limits of AI in Medical Specialisation: ChatGPT’s Performance on the UK Neurology Specialty Certificate Examination. BMJ Neurol. Open 2023, 5, e000451. [Google Scholar] [CrossRef]
- Guo, A.A.; Li, J. Harnessing the Power of ChatGPT in Medical Education. Med. Teach. 2023, 45, 1063. [Google Scholar] [CrossRef]
- Huh, S. Can We Trust AI Chatbots’ Answers about Disease Diagnosis and Patient Care? J. Korean Med. Assoc. 2023, 66, 218–222. [Google Scholar] [CrossRef]
- Currie, G.; Singh, C.; Nelson, T.; Nabasenja, C.; Al-Hayek, Y.; Spuur, K. ChatGPT in Medical Imaging Higher Education. Radiography 2023, 29, 792–799. [Google Scholar] [CrossRef]
- Dahmen, J.; Kayaalp, M.E.; Ollivier, M.; Pareek, A.; Hirschmann, M.T.; Karlsson, J.; Winkler, P.W. Artificial Intelligence Bot ChatGPT in Medical Research: The Potential Game Changer as a Double-Edged Sword. Knee Surg. Sports Traumatol. Arthrosc. 2023, 31, 1187–1189. [Google Scholar] [CrossRef]
- Mohammed, O.; Thaeer, M.S.; Israa, M.H.; Sani, S.; Misbah, S. ChatGPT Evaluation: Can It Replace Grammarly and Quillbot Tools? Br. J. Appl. Linguistics 2023, 3, 34–46. [Google Scholar] [CrossRef]
- Yang, J.; Li, H.B.; Wei, D. The Impact of ChatGPT and LLMs on Medical Imaging Stakeholders: Perspectives and Use Cases. arXiv 2023, arXiv:2306.06767. [Google Scholar] [CrossRef]
- Zhu, Z.; Ying, Y.; Zhu, J.; Wu, H. ChatGPT’s Potential Role in Non-English-Speaking Outpatient Clinic Settings. Digit. Health 2023, 9, 1–3. [Google Scholar] [CrossRef]
- Verhoeven, F.; Wendling, D.; Prati, C. ChatGPT: When Artificial Intelligence Replaces the Rheumatologist in Medical Writing. Ann. Rheum. Dis. 2023, 82, 1015–1017. [Google Scholar] [CrossRef]
- Corsello, A.; Santangelo, A. May Artificial Intelligence Influence Future Pediatric Research?—The Case of ChatGPT. Children 2023, 10, 757. [Google Scholar] [CrossRef] [PubMed]
- Pozzessere, C. Optimizing Communication of Radiation Exposure in Medical Imaging, the Radiologist Challenge. Tomography 2023, 9, 717–720. [Google Scholar] [CrossRef]
- Ning, G.; Liang, H.; Jiang, Z.; Zhang, H.; Liao, H. The Potential of “Segment Anything” (SAM) for Universal Intelligent Ultrasound Image Guidance. Biosci. Trends 2023, 17, 230–233. [Google Scholar] [CrossRef]
- Strunga, M.; Thurzo, A.; Surovkov, J.; Lifkov, M.; Tom, J. AI-Assisted CBCT Data Management in Modern Dental Practice: Benefits, Limitations and Innovations. Electronics 2023, 12, 1710. [Google Scholar]
- Pratim, P.; Poulami, R. AI Tackles Pandemics: ChatGPT’s Game–Changing Impact on Infectious Disease Control. Ann. Biomed. Eng. 2023, 51, 2097–2099. [Google Scholar] [CrossRef]
- Temsah, M.; Aljamaan, F.; Malki, K.H.; Alhasan, K. ChatGPT and the Future of Digital Health: A Study on Healthcare Workers’ Perceptions and Expectations. Healthcare 2023, 11, 1812. [Google Scholar] [CrossRef] [PubMed]
- Lukac, S.; Dayan, D.; Fink, V.; Leinert, E.; Hartkopf, A.; Veselinovic, K.; Janni, W.; Rack, B.; Pfister, K.; Heitmeir, B.; et al. Evaluating ChatGPT as an Adjunct for the Multidisciplinary Tumor Board Decision–Making in Primary Breast Cancer Cases. Arch. Gynecol. Obstet. 2023, 308, 1831–1844. [Google Scholar] [CrossRef] [PubMed]
- Kavian, J.A.; Wilkey, H.L.; Parth, A.; Boyd, C.J. Harvesting the Power of Arti Fi Cial Intelligence for Surgery: Uses, Implications, and Ethical Considerations. Am. Surg. 2023, 2–4. [Google Scholar] [CrossRef]
- Dave, T.; Athaluri, S.A.; Singh, S. ChatGPT in Medicine: An Overview of Its Applications, Advantages, Limitations, Future Prospects, and Ethical Considerations. Front. Artif. Intell. 2023, 6, 1169595. [Google Scholar] [CrossRef]
- Ruksakulpiwat, S.; Kumar, A.; Ajibade, A. Using ChatGPT in Medical Research: Current Status and Future Directions. J. Multidiscip. Healthc. 2023, 16, 1513–1520. [Google Scholar] [CrossRef] [PubMed]
- Tustumi, F.; Andreollo, N.A.; Aguilar-Nascimento, J.E.; No, E.; Em, S.; Prazo, L. Future of the Language Models in Healthcare: The Role of Chatgpt. ABCD. Arq. Bras. Cir. Dig. 2023, 34, e1727. [Google Scholar] [CrossRef] [PubMed]
- Kaarre, J.; Feldt, R.; Keeling, L.E.; Dadoo, S.; Zsidai, B.; Hughes, J.D.; Samuelsson, K.; Musahl, V. Exploring the Potential of ChatGPT as a Supplementary Tool for Providing Orthopaedic Information. Knee Surg. Sports Traumatol. Arthrosc. 2023, 31, 5190–5198. [Google Scholar] [CrossRef] [PubMed]
- Ollivier, M.; Pareek, A.; Dahmen, J.; Kayaalp, M.E.; Winkler, P.W.; Hirschmann, M.T.; Karlsson, J. A Deeper Dive into ChatGPT: History, Use and Future Perspectives for Orthopaedic Research. Knee Surg. Sports Traumatol. Arthrosc. 2023, 31, 1190–1192. [Google Scholar] [CrossRef]
- Sallam, M.; Salim, N.A.; Barakat, M.; Al-Tammemi, A.B. ChatGPT Applications in Medical, Dental, Pharmacy, and Public Health Education: A Descriptive Study Highlighting the Advantages and Limitations. Narra J 2023, 3, e103. [Google Scholar] [CrossRef]
- Liberati, A.; Altman, D.G.; Tetzlaff, J.; Mulrow, C.; Gøtzsche, P.C.; Ioannidis, J.P.A.; Clarke, M.; Devereaux, P.J.; Kleijnen, J.; Moher, D. The PRISMA Statement for Reporting Systematic Reviews and Meta-Analyses of Studies That Evaluate Health Care Interventions: Explanation and Elaboration. PLoS Med. 2009, 6, e1000100. [Google Scholar] [CrossRef]
- Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.; Brennan, S.E.; et al. The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews. BMJ 2021, 372, 71. [Google Scholar] [CrossRef] [PubMed]
- Younis, H.A.; Ruhaiyem, N.I.R.; Badr, A.A.; Abdul-Hassan, A.K.; Alfadli, I.M.; Binjumah, W.M.; Altuwaijri, E.A.; Nasser, M. Multimodal Age and Gender Estimation for Adaptive Human-Robot Interaction: A Systematic Literature Review. Processes 2023, 11, 1488. [Google Scholar] [CrossRef]
- Salisu, S.; Ruhaiyem, N.I.R.; Eisa, T.A.E.; Nasser, M.; Saeed, F.; Younis, H.A. Motion Capture Technologies for Ergonomics: A Systematic Literature Review. Diagnostics 2023, 13, 2593. [Google Scholar] [CrossRef]
- Younis, H.A.; Ruhaiyem, N.I.R.; Ghaban, W.; Gazem, N.A.; Nasser, M. A Systematic Literature Review on the Applications of Robots and Natural Language Processing in Education. Electronics 2023, 12, 2864. [Google Scholar] [CrossRef]
- Götz, S. Supporting Systematic Literature Reviews in Computer Science: The Systematic Literature Review Toolkit. In Proceedings of the 21th ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS 2018, Copenhagen, Denmark, 14–19 October 2018; pp. 22–26. [Google Scholar] [CrossRef]
- Muftić, F.; Kadunić, M.; Mušinbegović, A.; Almisreb, A.A. Exploring Medical Breakthroughs: A Systematic Review of ChatGPT Applications in Healthcare. Southeast Eur. J. Soft Comput. 2023, 12, 13–41. [Google Scholar]
- Liao, W.; Liu, Z.; Dai, H.; Xu, S.; Wu, Z.; Zhang, Y.; Huang, X.; Zhu, D.; Cai, H.; Liu, T.; et al. Differentiate ChatGPT-Generated and Human-Written Medical Texts. arXiv 2023, arXiv:2304.11567. [Google Scholar]
- Asch, D.A. An Interview with ChatGPT About Health Care. NEJM Catal. 2023, 1–8. [Google Scholar]
- Li, J.; Dada, A.; Kleesiek, J.; Egger, J. ChatGPT in Healthcare: A Taxonomy and Systematic Review. medRxiv 2023. [Google Scholar] [CrossRef]
- Vaishya, R.; Misra, A.; Vaish, A. ChatGPT: Is This Version Good for Healthcare and Research? Diabetes Metab. Syndr. Clin. Res. Rev. 2023, 17, 102744. [Google Scholar] [CrossRef]
- Homolak, J. Opportunities and Risks of ChatGPT in Medicine, Science, and Academic Publishing: A Modern Promethean Dilemma. Croat. Med. J. 2023, 64, 1–3. [Google Scholar] [CrossRef]
- Chow, J.C.L.; Sanders, L.; Li, K. Impact of ChatGPT on Medical Chatbots as a Disruptive Technology. Front. Artif. Intell. 2023, 6, 1166014. [Google Scholar] [CrossRef]
- Aydın, Ö.; Karaarslan, E. OpenAI ChatGPT Generated Literature Review: Digital Twin in Healthcare. SSRN Electron. J. 2022, 2, 22–31. [Google Scholar] [CrossRef]
- Javaid, M.; Haleem, A.; Singh, R.P. ChatGPT for Healthcare Services: An Emerging Stage for an Innovative Perspective. BenchCouncil Trans. Benchmarks Stand. Eval. 2023, 3, 100105. [Google Scholar] [CrossRef]
- Liebrenz, M.; Schleifer, R.; Buadze, A.; Bhugra, D.; Smith, A. Generating Scholarly Content with ChatGPT: Ethical Challenges for Medical Publishing. Lancet Digit. Health 2023, 5, e105–e106. [Google Scholar] [CrossRef]
- Eysenbach, G. The Role of ChatGPT, Generative Language Models, and Artificial Intelligence in Medical Education: A Conversation with ChatGPT and a Call for Papers. JMIR Med. Educ. 2023, 9, e46885. [Google Scholar] [CrossRef] [PubMed]
- Alberts, I.L.; Mercolli, L.; Pyka, T.; Prenosil, G.; Shi, K.; Rominger, A.; Afshar-Oromieh, A. Large Language Models (LLM) and ChatGPT: What Will the Impact on Nuclear Medicine Be? Eur. J. Nucl. Med. Mol. Imag. 2023, 50, 1549–1552. [Google Scholar] [CrossRef]
- Cascella, M.; Montomoli, J.; Bellini, V.; Bignami, E. Evaluating the Feasibility of ChatGPT in Healthcare: An Analysis of Multiple Clinical and Research Scenarios. J. Med. Syst. 2023, 47, 33. [Google Scholar] [CrossRef]
- Sohail, S.S.; Farhat, F.; Himeur, Y.; Nadeem, M.; Madsen, D.Ø.; Singh, Y.; Atalla, S.; Mansoor, W. The Future of GPT: A Taxonomy of Existing ChatGPT Research, Current Challenges, and Possible Future Directions. SSRN Electron. J. 2023. [Google Scholar] [CrossRef]
- Wen, J.; Wang, W. The Future of ChatGPT in Academic Research and Publishing: A Commentary for Clinical and Translational Medicine. Clin. Transl. Med. 2023, 13, 2–4. [Google Scholar] [CrossRef]
- Mohammad, B.; Supti, T.; Alzubaidi, M.; Shah, H.; Alam, T.; Shah, Z.; Househ, M. The Pros and Cons of Using ChatGPT in Medical Education: A Scoping Review. Stud. Health Technol. Inform. 2023, 305, 644–647. [Google Scholar] [CrossRef]
- Cox, A.; Seth, I.; Xie, Y.; Hunter-Smith, D.J.; Rozen, W.M. Utilizing ChatGPT-4 for Providing Medical Information on Blepharoplasties to Patients. Aesthetic Surg. J. 2023, 43, NP658–NP662. [Google Scholar] [CrossRef]
- Digiorgio, A.M.; Ehrenfeld, J.M. Artificial Intelligence in Medicine & ChatGPT: De-Tether the Physician. J. Med. Syst. 2023, 47, 32. [Google Scholar] [PubMed]
- Ellaway, R.H. Artificial Scholarship: LLMs in Health Professions Education Research. Adv. Health Sci. Educ. 2023, 28, 659–664. [Google Scholar] [CrossRef] [PubMed]
- Samaan, J.S.; Hui, Y.; Nithya, Y.; Lauren, R.; Stuart, H.; Wee, A.; Ng, H.; Srinivasan, N.; Park, J.; Burch, M.; et al. Assessing the Accuracy of Responses by the Language Model ChatGPT to Questions Regarding Bariatric Surgery. Obes. Surg. 2023, 33, 1790–1796. [Google Scholar] [CrossRef] [PubMed]
- Gilson, A.; Safranek, C.W.; Huang, T.; Socrates, V.; Chi, L.; Street, C. Authors’ Reply to: Variability in Large Language Models’ Responses to Medical Licensing and Certification Examinations. JMIR Med. Educ. 2023, 9, e50336. [Google Scholar] [CrossRef] [PubMed]
- Santandreu-Calonge, D.; Medina-Aguerrebere, P.; Hultberg, P.; Shah, M.-A. Can ChatGPT Improve Communication in Hospitals? Anu. Thinkepi 2023, 32, 1–16. [Google Scholar] [CrossRef]
- Singh, S.; Djalilian, A.; Ali, M.J. ChatGPT and Ophthalmology: Exploring Its Potential with Discharge Summaries and Operative Notes. Semin. Ophthalmol. 2023, 38, 503–507. [Google Scholar] [CrossRef] [PubMed]
- Oh, N.; Choi, G.; Lee, W.Y. ChatGPT Goes to the Operating Room: Evaluating GPT-4 Performance and Its Potential in Surgical Education and Training in the Era of Large Language Models. Ann. Surg. Treat. Res. 2023, 104, 269–273. [Google Scholar] [CrossRef]
- Communication, S.; Khan, R.A.; Jawaid, M.; Khan, A.R.; Sajjad, M. ChatGPT–Reshaping Medical Education and Clinical Management. Pak. J. Med. Sci. 2023, 39, 605–607. [Google Scholar]
- Liu, X.; Wu, C.; Lai, R.; Lin, H.; Xu, Y.; Lin, Y.; Zhang, W. ChatGPT: When the Artificial Intelligence Meets Standardized Patients in Clinical Training. J. Transl. Med. 2023, 21, 447. [Google Scholar] [CrossRef] [PubMed]
- Gao, C.A.; Howard, F.M.; Pearson, A.T.; Dyer, E.C. Comparing Scientific Abstracts Generated by ChatGPT to Real Abstracts with Detectors and Blinded Human Reviewers. NPJ Digit. Med. 2023, 6, 75. [Google Scholar] [CrossRef] [PubMed]
- Rahimzadeh, V.; Kostick-quenet, K.; Barby, J.B.; Mcguire, A.L.; Rahimzadeh, V.; Kostick-quenet, K.; Barby, J.B.; Rahimzadeh, V.; Kostick-Quenet, K.; Barby, J.B.; et al. Ethics Education for Healthcare Professionals in the Era of ChatGPT and Other Large Language Models: Do We Still Need It? Ethics Education for Healthcare Professionals in the Era of ChatGPT And. Am. J. Bioeth. 2023, 23, 17–27. [Google Scholar] [CrossRef] [PubMed]
- 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 2023, 13, 1950. [Google Scholar] [CrossRef] [PubMed]
- Wang, H.; Jenny, Y.; Yuan, D. Future of ChatGPT in Pharmacovigilance. Drug Saf. 2023, 46, 711–713. [Google Scholar] [CrossRef] [PubMed]
- Rodigin, A. Is Medicine Ready for ChatGPT–Why Not Just Ask ChatGPT? Eur. J. Transl. Clin. Med. 2023, 6, 5–8. [Google Scholar] [CrossRef]
- Wang, H.; Wu, W.; Dou, Z.; He, L.; Yang, L. Performance and Exploration of ChatGPT in Medical Examination, Records and Education in Chinese: Pave the Way for Medical AI. Int. J. Med. Inform. 2023, 177, 105173. [Google Scholar] [CrossRef]
- Cheng, K.; Li, Z.; He, Y.; Guo, Q.; Lu, Y.; Gu, S.; Wu, H. Potential Use of Artificial Intelligence in Infectious Disease: Take ChatGPT as an Example. Ann. Biomed. Eng. 2023, 51, 1130–1135. [Google Scholar] [CrossRef]
- Nov, O.; Singh, N.; Mann, D. Putting ChatGPT’s Medical Advice to the (Turing) Test: Survey Study. JMIR Med. Educ. 2023, 9, e46939. [Google Scholar] [CrossRef]
- Janamala, V.; Sai, I.; Suresh, R.; Daram, B. Realization of Green 5G Cellular Network Role in Medical Applications: Use of ChatGPT–AI. Ann. Biomed. Eng. 2023, 51, 2337–2339. [Google Scholar] [CrossRef]
- Janamla, V.; Babu, S.; Patil, D.; Nagaraja, R.C.H. Response of ChatGPT for Humanoid Robots Role in Improving Healthcare and Patient Outcomes. Ann. Biomed. Eng. 2023, 51, 2359–2361. [Google Scholar] [CrossRef]
- Polonsky, M.J.; Rotman, J.D. Should Artificial Intelligent Agents Be Your Co-Author? Arguments in Favour, Informed by ChatGPT. Australas. Mark. J. 2023, 31, 91–96. [Google Scholar] [CrossRef]
- Sedaghat, S. Success Through Simplicity: What Other Artificial Intelligence Applications in Medicine Should Learn from History and ChatGPT. Ann. Biomed. Eng. 2023, 51, 2657–2658. [Google Scholar] [CrossRef] [PubMed]
- Bin Arif, T.; Munaf, U.; Ul-Haque, I. The Future of Medical Education and Research: Is ChatGPT a Blessing or Blight in Disguise? Med. Educ. Online 2023, 28, 2181052. [Google Scholar] [CrossRef]
- Wornow, M.; Xu, Y.; Thapa, R.; Patel, B.; Steinberg, E.; Fleming, S. The Shaky Foundations of Large Language Models and Foundation Models for Electronic Health Records. NPJ Digit. Med. 2023, 6, 135. [Google Scholar] [CrossRef]
- Hügle, T. The Wide Range of Opportunities for Large Language Models Such as ChatGPT in Rheumatology. RMD Open 2023, 9, e003105. [Google Scholar] [CrossRef] [PubMed]
- Liu, J.; Wang, C.; Liu, S. Utility of ChatGPT in Clinical Practice. J. Med. Internet Res. 2023, 25, e48568. [Google Scholar] [CrossRef]
- Cohen, I.G.; Cohen, I.G. What Should ChatGPT Mean for Bioethics? What Should ChatGPT Mean for Bioethics? Am. J. Bioeth. 2023, 23, 8–16. [Google Scholar] [CrossRef]
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