Artificial Intelligence and the Future of Computer-Assisted Medical Research and Writing

Dutton, Jonathan J.

doi:10.1097/iop.0000000000002420

Cited by 3 publications

(4 citation statements)

References 23 publications

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“…Although ChatGPT can create designs and texts suitable for an academic article, it has many shortcomings when looking at the article examples it has written. At this point, there is also a concern that it may lead to a lack of critical thinking, originality and analysis and provide biased information (Bishop, 2023;Dutton, 2023;İskender, 2023;Nguyen, 2023). In addition, there are concerns that ChatGPT may not represent the moral, social and cultural ideals that academics value (OpenAI, 2022).…”

Section: Discussionmentioning

confidence: 99%

Toward non-human-centered design: designing an academic article with ChatGPT

Livberber

2023

EPI

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Non-human-centered design tools, such as ChatGPT, have shown potential as effective aids in academic article design. This study conducts a comparative evaluation of ChatGPT-3.5 and ChatGPT-4, examining their capabilities and limitations in supporting the academic article design process. The study aims to demonstrate the utility of ChatGPT as a writing tool and investigate its applicability and efficacy in the context of academic paper design. The author interacted with both versions of ChatGPT, providing prompts and analyzing the generated responses. In addition, a different expert academic was consulted to assess the appropriateness of the ChatGPT responses. The findings suggest that ChatGPT, despite its limitations, could serve as a useful tool for academic writing, particularly in the design of academic articles. Despite the limitations of both GPT-3.5 and GPT-4, GPT-3.5 offers a broader perspective, whereas GPT-4 provides a more in-depth and detailed approach to the design of articles. ChatGPT exhibits capabilities in aiding the design process, generating ideas aligned with the overall purpose and focus of the paper, producing consistent and contextually relevant responses to various natural language inputs, partially assisting in literature reviews, supporting paper design in terms of both content and format, and providing reasonable editing and proofreading for articles. However, limitations were identified, including reduced critical thinking, potential for plagiarism, risk of misinformation, lack of originality and innovation, and limited access to literature.

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Section: Discussionmentioning

confidence: 99%

Toward non-human-centered design: designing an academic article with ChatGPT

Livberber

2023

EPI

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“…These findings underscore the importance of uniformly promoting transparency across all surgical disciplines. Additionally, the disclosure of LLM use is critical for upholding the integrity of academic work and research [64,71]. For surgical practice and research, transparency is essential.…”

Section: Transparencymentioning

confidence: 99%

“…They can produce content that could be mistaken for human-generated medical literature, raising significant issues about authorship [8,15,19,32,56,65,74,75]. However, LLMs like ChatGPT are ineligible for authorship due to their inability to accept responsibility [41,47,52,53,55,58,71,76]. Additionally, there is a risk that LLMs might inadvertently plagiarize content, leading to unintentional plagiarism by users [47,71,76].…”

Section: Authorship and Plagiarismmentioning

confidence: 99%

“…However, LLMs like ChatGPT are ineligible for authorship due to their inability to accept responsibility [41,47,52,53,55,58,71,76]. Additionally, there is a risk that LLMs might inadvertently plagiarize content, leading to unintentional plagiarism by users [47,71,76]. Such scenarios can blur the lines of true authorship and violate intellectual property rights and academic integrity [47,76].…”

Section: Authorship and Plagiarismmentioning

confidence: 99%

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AI and Ethics: A Systematic Review of the Ethical Considerations of Large Language Model Use in Surgery Research

Pressman,

Borna,

Gomez-Cabello

et al. 2024

Healthcare

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Introduction: As large language models receive greater attention in medical research, the investigation of ethical considerations is warranted. This review aims to explore surgery literature to identify ethical concerns surrounding these artificial intelligence models and evaluate how autonomy, beneficence, nonmaleficence, and justice are represented within these ethical discussions to provide insights in order to guide further research and practice. Methods: A systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Five electronic databases were searched in October 2023. Eligible studies included surgery-related articles that focused on large language models and contained adequate ethical discussion. Study details, including specialty and ethical concerns, were collected. Results: The literature search yielded 1179 articles, with 53 meeting the inclusion criteria. Plastic surgery, orthopedic surgery, and neurosurgery were the most represented surgical specialties. Autonomy was the most explicitly cited ethical principle. The most frequently discussed ethical concern was accuracy (n = 45, 84.9%), followed by bias, patient confidentiality, and responsibility. Conclusion: The ethical implications of using large language models in surgery are complex and evolving. The integration of these models into surgery necessitates continuous ethical discourse to ensure responsible and ethical use, balancing technological advancement with human dignity and safety.

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Evaluating the Accuracy of ChatGPT and Google BARD in Fielding Oculoplastic Patient Queries: A Comparative Study on Artificial versus Human Intelligence

Al-Sharif,

Penteado,

Dib El Jalbout

et al. 2024

Ophthalmic Plastic &Amp; Reconstructive Surgery

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Purpose: This study evaluates and compares the accuracy of responses from 2 artificial intelligence platforms to patients’ oculoplastics-related questions. Methods: Questions directed toward oculoplastic surgeons were collected, rephrased, and input independently into ChatGPT-3.5 and BARD chatbots, using the prompt: “As an oculoplastic surgeon, how can I respond to my patient’s question?.” Responses were independently evaluated by 4 experienced oculoplastic specialists as comprehensive, correct but inadequate, mixed correct and incorrect/outdated data, and completely incorrect. Additionally, the empathy level, length, and automated readability index of the responses were assessed. Results: A total of 112 patient questions underwent evaluation. The rates of comprehensive, correct but inadequate, mixed, and completely incorrect answers for ChatGPT were 71.4%, 12.9%, 10.5%, and 5.1%, respectively, compared with 53.1%, 18.3%, 18.1%, and 10.5%, respectively, for BARD. ChatGPT showed more empathy (48.9%) than BARD (13.2%). All graders found that ChatGPT outperformed BARD in question categories of postoperative healing, medical eye conditions, and medications. Categorizing questions by anatomy, ChatGPT excelled in answering lacrimal questions (83.8%), while BARD performed best in the eyelid group (60.4%). ChatGPT’s answers were longer and potentially more challenging to comprehend than BARD’s. Conclusion: This study emphasizes the promising role of artificial intelligence-powered chatbots in oculoplastic patient education and support. With continued development, these chatbots may potentially assist physicians and offer patients accurate information, ultimately contributing to improved patient care while alleviating surgeon burnout. However, it is crucial to highlight that artificial intelligence may be good at answering questions, but physician oversight remains essential to ensure the highest standard of care and address complex medical cases.

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Artificial Intelligence and the Future of Computer-Assisted Medical Research and Writing

Cited by 3 publications

References 23 publications

Toward non-human-centered design: designing an academic article with ChatGPT

Toward non-human-centered design: designing an academic article with ChatGPT

AI and Ethics: A Systematic Review of the Ethical Considerations of Large Language Model Use in Surgery Research

Evaluating the Accuracy of ChatGPT and Google BARD in Fielding Oculoplastic Patient Queries: A Comparative Study on Artificial versus Human Intelligence

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