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Artificial Intelligence is no more the talk of the fiction read in novels or seen in movies. It has been making inroads slowly and gradually in medical education and clinical management of patients apart from all other walks of life. Recently, chatbots particularly ChatGPT, were developed and trained, using a huge amount of textual data from the internet. This has made a significant impact on our approach in medical science. Though there are benefits of this new technology, a lot of caution is required for its use. doi: https://doi.org/10.12669/pjms.39.2.7653 How to cite this: Khan RA, Jawaid M, Khan AR, Sajjad M. ChatGPT - Reshaping medical education and clinical management. Pak J Med Sci. 2023;39(2):---. doi: https://doi.org/10.12669/pjms.39.2.7653 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Introduction: The emergence of artificial intelligence (AI) has presented several opportunities to ease human work. AI applications are available for almost every domain of life. A new technology, Chat Generative Pre-Trained Transformer (ChatGPT), was introduced by OpenAI in November 2022, and has become a topic of discussion across the world. ChatGPT-3 has brought many opportunities, as well as ethical and privacy considerations. ChatGPT is a large language model (LLM) which has been trained on the events that happened until 2021. The use of AI and its assisted technologies in scientific writing is against research and publication ethics. Therefore, policies and guidelines need to be developed over the use of such tools in scientific writing. The main objective of the present study was to highlight the use of AI and AI assisted technologies such as the ChatGPT and other chatbots in the scientific writing and in the research domain resulting in bias, spread of inaccurate information and plagiarism. Methodology: Experiments were designed to test the accuracy of ChatGPT when used in research and academic writing. Results: The information provided by ChatGPT was inaccurate and may have far-reaching implications in the field of medical science and engineering. Critical thinking should be encouraged among researchers to raise awareness about the associated privacy and ethical risks.  Conclusions: Regulations for ethical and privacy concerns related to the use of ChatGPT in academics and research need to be developed.

In this paper, OpenAI's ChatGPT (Generative Pre-trained Transformer), also known as GPT-3, a machine-learning model that has the ability to generate human-like text, was employed as an interviewee instead of a human subject. The scope of the interview was the impacts of OpenAI's GPT on higher education and academic publishing. Particularly, several questions about the impacts of OpenAI's ChatGPT and other AI-based machine learning models on the hospitality and tourism industry and education were asked. The originality of this paper derives from having the ChatGPT as an interviewee. ChatGPT stated that its use helps instructors delegate monotonous tasks such as grading and focus on more intellectual tasks, and students may utilize ChatGPT to brainstorm ideas. ChatGPT confesses the risk of diminishing critical thinking for students in the case of over-reliance on ChatGPT as well as educational inequalities. For academic work, ChatGPT addressed it cannot be a substitute for human creativity and intellectuality because originality and novelty lack in outputs generated by ChatGPT. The tourism and hospitality industry can benefit from ChatGPT for certain things such as personalized services, content creation, and many more.

Background The integration of artificial intelligence (AI) in radiology has opened new possibilities for diagnostic accuracy, with large language models (LLMs) showing potential for supporting clinical decision‐making. While proprietary models like ChatGPT have gained attention, open‐source alternatives such as Meta LLaMa 3.1 remain underexplored. This study aims to evaluate the diagnostic accuracy of LLaMa 3.1 in thoracic imaging and to discuss broader implications of open‐source versus proprietary AI models in healthcare. Methods Meta LLaMa 3.1 (8B parameter version) was tested on 126 multiple‐choice thoracic imaging questions selected from Thoracic Imaging: A Core Review by Hobbs et al. These questions required no image interpretation. The model’s answers were validated by two board‐certified diagnostic radiologists. Accuracy was assessed overall and across subgroups, including intensive care, pathology, and anatomy. Additionally, a narrative review introduces three widely used AI platforms in thoracic imaging: DeepLesion, ChexNet, and 3D Slicer. Results LLaMa 3.1 achieved an overall accuracy of 61.1%. It performed well in intensive care (90.0%) and terms and signs (83.3%) but showed variability across subgroups, with lower accuracy in normal anatomy and basic imaging (40.0%). Subgroup analysis revealed strengths in infectious pneumonia and pleural disease, but notable weaknesses in lung cancer and vascular pathology. Conclusion LLaMa 3.1 demonstrates promise as an open‐source NLP tool in thoracic diagnostics, though its performance variability highlights the need for refinement and domain‐specific training. Open‐source models offer transparency and accessibility, while proprietary models deliver consistency. Both hold value, depending on clinical context and resource availability. This study evaluates the diagnostic accuracy of the Meta LLaMa 3.1 model in thoracic imaging, highlighting its strengths and areas for improvement. It also compares open‐source and proprietary natural language processing models in healthcare, focusing on factors such as transparency and performance consistency. The paper further explores the role of open‐source AI platforms in advancing thoracic imaging and diagnostics.

The integration of artificial intelligence (AI) and brain–computer interfaces (BCIs) represents a significant advancement in neurotechnology, with broad potential applications in healthcare, communication, and human augmentation. This study examines the synergy between DeepSeek, a leader in efficient, open‐source AI models, and next‐generation BCI technologies. We analyze DeepSeek's contributions to model training efficiency, adaptive reasoning, and open‐source accessibility, and propose a framework for BCI development that incorporates these innovations. Additionally, we explore how AI‐driven neural signal processing, hardware optimization, and ethical AI–BCI systems can address the critical limitations of current BCI technologies, including signal fidelity, scalability, and real‐world applicability. Finally, we offer recommendations for interdisciplinary collaboration, regulatory improvements, and equitable technology dissemination to foster the sustainable development of AI–BCI technology. This perspective examines the synergy between DeepSeek, a leader in efficient, open‐source artificial intelligence models, and next‐generation brain–computer interface (BCI) technologies. Based on the advanced technology of DeepSeek, this study explores the fusion of large language models (LLMs) in the fields of BCI devices, electroencephalogram signal processing, and individual service, and deeply discusses the data safety and ethical issues that are inevitable with LLMs, providing unique insights into the path to democratizing neurotechnology.

The rapid trajectory of artificial intelligence (AI) development and advancement is quickly outpacing society's ability to determine its future role. As AI continues to transform various aspects of our lives, one critical question arises for medical education: what will be the nature of education, teaching, and learning in a future world where the acquisition, retention, and application of knowledge in the traditional sense are fundamentally altered by AI? The purpose of this perspective is to plan for the intersection of health care and medical education in the future. We used GPT-4 and scenario-based strategic planning techniques to craft 4 hypothetical future worlds influenced by AI's integration into health care and medical education. This method, used by organizations such as Shell and the Accreditation Council for Graduate Medical Education, assesses readiness for alternative futures and effectively manages uncertainty, risk, and opportunity. The detailed scenarios provide insights into potential environments the medical profession may face and lay the foundation for hypothesis generation and idea-building regarding responsible AI implementation. The following 4 worlds were created using OpenAI's GPT model: AI Harmony, AI conflict, The world of Ecological Balance, and Existential Risk. Risks include disinformation and misinformation, loss of privacy, widening inequity, erosion of human autonomy, and ethical dilemmas. Benefits involve improved efficiency, personalized interventions, enhanced collaboration, early detection, and accelerated research. To ensure responsible AI use, the authors suggest focusing on 3 key areas: developing a robust ethical framework, fostering interdisciplinary collaboration, and investing in education and training. A strong ethical framework emphasizes patient safety, privacy, and autonomy while promoting equity and inclusivity. Interdisciplinary collaboration encourages cooperation among various experts in developing and implementing AI technologies, ensuring that they address the complex needs and challenges in health care and medical education. Investing in education and training prepares professionals and trainees with necessary skills and knowledge to effectively use and critically evaluate AI technologies. The integration of AI in health care and medical education presents a critical juncture between transformative advancements and significant risks. By working together to address both immediate and long-term risks and consequences, we can ensure that AI integration leads to a more equitable, sustainable, and prosperous future for both health care and medical education. As we engage with AI technologies, our collective actions will ultimately determine the state of the future of health care and medical education to harness AI's power while ensuring the safety and well-being of humanity.

Background ChatGPT is a free artificial intelligence (AI) language model developed and released by OpenAI in late 2022. This study aimed to evaluate the performance of ChatGPT to accurately answer clinical questions (CQs) on the Guideline for the Management of Blepharoptosis published by the American Society of Plastic Surgeons (ASPS) in 2022. Methods CQs in the guideline were used as question sources in both English and Japanese. For each question, ChatGPT provided answers for CQs, evidence quality, recommendation strength, reference match, and answered word counts. We compared the performance of ChatGPT in each component between English and Japanese queries. Results A total of 11 questions were included in the final analysis, and ChatGPT answered 61.3% of these correctly. ChatGPT demonstrated a higher accuracy rate in English answers for CQs compared to Japanese answers for CQs (76.4% versus 46.4%; p = 0.004) and word counts (123 words versus 35.9 words; p = 0.004). No statistical differences were noted for evidence quality, recommendation strength, and reference match. A total of 697 references were proposed, but only 216 of them (31.0%) existed. Conclusions ChatGPT demonstrates potential as an adjunctive tool in the management of blepharoptosis. However, it is crucial to recognize that the existing AI model has distinct limitations, and its primary role should be to complement the expertise of medical professionals. Level of Evidence V Observational study under respected authorities. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Background Chat generative pre-trained transformer (ChatGPT) is a publicly available extensive artificial intelligence (AI) language model that leverages deep learning to generate text that mimics human conversations. In this study, the performance of ChatGPT was assessed by offering insightful and precise answers to a series of fictional questions and emulating a preliminary consultation on blepharoplasty. Methods ChatGPT was posed with questions derived from a blepharoplasty checklist provided by the American Society of Plastic Surgeons. Board-certified plastic surgeons and non-medical staff members evaluated the responses for accuracy, informativeness, and accessibility. Results Nine questions were used in this study. Regarding informativeness, the average score given by board-certified plastic surgeons was significantly lower than that given by non-medical staff members (2.89 ±  0.72 vs 4.41 ± 0.71; p  = 0.042). No statistically significant differences were observed in accuracy ( p  = 0.56) or accessibility ( p  = 0.11). Conclusions Our results emphasize the effectiveness of ChatGPT in simulating doctor–patient conversations during blepharoplasty. Non-medical individuals found its responses more informative compared with the surgeons. Although limited in terms of specialized guidance, ChatGPT offers foundational surgical information. Further exploration is warranted to elucidate the broader role of AI in esthetic surgical consultations. Level of Evidence V Observational study under respected authorities. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

The launch of the open AI chatbot, ChatGPT, in November 2022 has generated widespread excitement around Generative Artificial Intelligence (AI). While researchers have explored ChatGPT's ability to produce content and respond to input, our study takes a different approach and examines its use cases in the financial industry. We aim to understand what ChatGPT offers the financial industry and how it differs from existing banking and financial chatbots. Financial institutions can use ChatGPT for a variety of purposes, including customer engagement, personalization, up-selling and cross-selling, stock forecasting, product development, and financial education. By focusing on the potential of ChatGPT in finance, we hope to spark discussions about its applications in other domains and explore the possibilities of a larger revolution in the future. Finally, this study identifies the challenges associated with the use of Generative Open AI and LLMs-based chatbots in the financial industry and provides recommendations for addressing these challenges.

The present paper reports on the development of Bosnian/Croatian/Serbian Novice and Intermediate Chatbots at Arizona State University. The principal AI platform used for this course is MyAI Builder, an open AI development tool. In the initial phase of this project, from the summer of 2000 to the Fall of 2022, materials to be used as the input for the platform were gathered. Various lexical lists and other materials were gathered along with developing an interactive platform. Next, numerous materials were selected by student researchers supported by ASU Melikian Center Research Grants. This is of particular importance given that it is likely to increase the interest of their fellow students in them. The two chatbots were created in the fall of 2024 and their testing has started. The paper reports on the first, overwhelmingly positive, round of feedback from students, which, at this point, is just the first indication of the chatbots’ performance. There are several takeaways from this project. First, it is important to emphasize the benefits of using open AI platforms that enable control of the knowledge base and thus limit the number of hallucinations. Second, it is important to involve students in developing this educational platform so that the content is appealing to prospective users. Third, it is important to integrate existing pedagogical and reference materials into this platform. Finally, finding the most suitable large language model is of the utmost importance.