The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index; none of the 25 sources its claims cite is checked for support yet.
Opportunities and risks of ChatGPT in medicine, science, and academic publishing: a modern Promethean dilemma
Open accessAbstract
Aim To evaluate the predictive properties of several com - mon prognostic scores regarding survival outcomes in hospitalized COVID-19 patients. Methods We retrospectively reviewed the medical records of 4014 consecutive COVID-19 patients hospitalized in our tertiary level institution from March 2020 to March 2021. Prognostic properties of the WHO COVID-19 severity clas - sification, COVID-GRAM, Veterans Health Administration COVID-19 (VACO) Index, 4C Mortality Score, and CURB65 score regarding 30-day mortality, in-hospital mortality, presence of severe or critical disease on admission, need for an intensive care unit treatment, and mechanical venti - lation during hospitalization were evaluated. Results All of the investigated prognostic scores signifi - cantly distinguished between groups of patients with differ - ent 30-day mortality. The CURB-65 and 4C Mortality Score had the best prognostic properties for prediction of 30-day mortality (area under the curve [AUC] 0.761 for both) and in-hospital mortality (AUC 0.757 and 0.762, respectively). The 4C Mortality Score and COVID-GRAM best predicted the presence of severe or critical disease (AUC 0.785 and 0.717, respectively). In the multivariate analysis evaluating 30-day mortality, all scores mutually independently provid - ed additional prognostic information, except the VACO In - dex, whose prognostic properties were redundant. Conclusion Complex prognostic scores based on many parameters and comorbid conditions did not have better prognostic properties regarding survival outcomes than a simple CURB-65 prognostic score. CURB-65 also provides the largest number of prognostic categories (five), allowing more precise risk stratification than other prognostic scores.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This commentary argues that ChatGPT and similar conversational AI will not replace physicians but can substantially augment their work, while posing serious risks to scientific integrity and academic publishing. It calls for mindful, debated adoption rather than hasty implementation or outright bans.
Key findings
- ChatGPT is a 175-billion-parameter model that allegedly gained more than 1 million users in its first few days and 100 million within 2 months, making it the fastest-growing consumer application in history.
- On knowledge-based medical tests, ChatGPT scored ~66% on Basic Life Support and ~72% on Advanced Cardiovascular Life Support, and performed at or near the passing threshold on the United States Medical Licensing Exam; however, the author notes algorithms remain "notoriously bad at context and nuance."
- Citing Frey and Osborne, the probability of automating physicians' and surgeons' jobs is 0.42%, versus 91% for health information technicians — supporting the claim that AI's value lies in redistributing workload, not replacing clinicians.
- In one cited experiment, 50 ChatGPT-generated research abstracts passed a plagiarism checker, an AI-output detector, and human reviewers; the author warns the chatbot also fabricates plausible-sounding references, and StackOverflow banned its generated text because the average rate of correct answers is too low.
Methods
- Design: a narrative commentary/opinion piece (no primary data collection or systematic methodology), structured around three questions — whether ChatGPT can replace physicians, whether it can act as a "chatbot-scientist," and what should happen next.
- Evidence base: synthesis of cited literature, news reports, publisher policies, and social media posts, including studies of ChatGPT's exam performance, abstract generation, and review-article writing.
- Main analysis: qualitative argumentation about opportunities (reduced paperwork, faster literature search, shifting research focus from presentation to content) and risks (bias amplification, fabricated sources, accountability and plagiarism gaps, lack of legal frameworks for AI-generated work).
Limitations
- Not stated in the paper: the text does not include a limitations section, as is typical of a commentary.
- Evident from the text: it is an opinion piece without primary data or systematic review, so its claims rest on secondary sources of varying rigor — including Twitter posts (refs 24, 25), a blog post, and a forum policy announcement.
- Evident from the text: several reference access dates are internally inconsistent (e.g., "Accessed: February 24, 2022" for sources published in 2023), and the author's assertions about future AI capability ("can only be improved," "might soon be able to") are speculative rather than evidenced.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
17 Not checked yet · 5 Own interpretation
None of the sources cited for this claim have been checked yet.
In the paper
“Algorithms perform relatively well on knowledge-based tests despite the lack of domain-specific training”
§ Opportunities and risks of ChatGPT in medicine, science, and academic publishing: a modern Promethean dilemma > Can ChatGPT replace physicians?
What the sources say
- Not checked yetcites [5] Fijačko N Gosak L Štiglic G Picard CT John Douma M Can ChatGPT pass the life support exams without entering the American Heart Association Course? Resuscitation 2023 169 97 8 10.1016/j.resuscitation.2023.109732 36775020
- Not checked yetcites [6] Gilson A Safranek C Huang T Socrates V Chi L 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 10.2196/45312 36753318 PMC9947764
- Not checked yetcites [7] Kung TH Cheatham M Medenilla A Sillos C Leon LD Elepaño C Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models. PLoS Digit Health. 2023 2 e0000198 10.1371/journal.pdig.0000198 36812645 PMC9931230
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“the tremendous potential of AI in healthcare does not lie in the possibility of replacing physicians, but rather in the capacity to increase physicians’ efficacy by redistributing workload and optimizing performance”
§ Opportunities and risks of ChatGPT in medicine, science, and academic publishing: a modern Promethean dilemma > Can ChatGPT replace physicians?
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: Context and nuance are critical for safe and effective patient care.
None of the sources cited for this claim have been checked yet.
In the paper
“performed at or near the passing threshold on the United States Medical Licensing Exam (6,7)”
§ Opportunities and risks of ChatGPT in medicine, science, and academic publishing: a modern Promethean dilemma > Can ChatGPT replace physicians?
What the sources say
- Not checked yetcites [6] Gilson A Safranek C Huang T Socrates V Chi L 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 10.2196/45312 36753318 PMC9947764
- Not checked yetcites [7] Kung TH Cheatham M Medenilla A Sillos C Leon LD Elepaño C Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models. PLoS Digit Health. 2023 2 e0000198 10.1371/journal.pdig.0000198 36812645 PMC9931230
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A failure to acknowledge the limitations of conversational AI might pose an additional strain on the publishing system already flooded with meaningless data and low-quality manuscripts”
§ Opportunities and risks of ChatGPT in medicine, science, and academic publishing: a modern Promethean dilemma > A chatbot-scientist
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: unclear — It is unclear whether it could be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“they are notoriously bad at context and nuance (8)”
§ Opportunities and risks of ChatGPT in medicine, science, and academic publishing: a modern Promethean dilemma > Can ChatGPT replace physicians?
What the sources say
- Not checked yetcites [8] Mbakwe AB Lourentzou I Celi LA Mechanic OJ Dagan A ChatGPT passing USMLE shines a spotlight on the flaws of medical education. PLoS Digit Health. 2023 2 e0000205 10.1371/journal.pdig.0000205 36812618 PMC9931307
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“the chatbot seems to have an alarming tendency to make up references with the goal of sounding convincing (18,24,25)”
§ Opportunities and risks of ChatGPT in medicine, science, and academic publishing: a modern Promethean dilemma > A chatbot-scientist
What the sources say
- Not checked yetcites [18] van Dis EAM Bollen J Zuidema W van Rooij R Bockting CL ChatGPT: five priorities for research. Nature 2023 614 224 6 10.1038/d41586-023-00288-7 36737653
- Not checked yetcites [24] Teresa Kubacka. Twitter. Available from: https://twitter.com/paniterka_ch/status/1599893718214901760. Accessed: February 25, 2023.
- Not checked yetcites [25] David Smerdon. Twitter. Available from: https://twitter.com/dsmerdon/status/1618816703923912704. Accessed: February 25, 2023.
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“ChatGPT seems to be equally unreliable when it comes to writing research articles”
§ Opportunities and risks of ChatGPT in medicine, science, and academic publishing: a modern Promethean dilemma > A chatbot-scientist
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“ChatGPT-generated manuscripts might be misleading, based on non-credible or completely made-up sources (18)”
§ Opportunities and risks of ChatGPT in medicine, science, and academic publishing: a modern Promethean dilemma > A chatbot-scientist
What the sources say
- Not checked yetcites [18] van Dis EAM Bollen J Zuidema W van Rooij R Bockting CL ChatGPT: five priorities for research. Nature 2023 614 224 6 10.1038/d41586-023-00288-7 36737653
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“researchers used existing publications to generate 50 research abstracts that were able to pass the plagiarism check performed by a plagiarism checker, an AI-output detector, and human reviewers (21)”
§ Opportunities and risks of ChatGPT in medicine, science, and academic publishing: a modern Promethean dilemma > A chatbot-scientist
What the sources say
- Not checked yetcites [21] Else H Abstracts written by ChatGPT fool scientists. Nature 2023 613 423 423 10.1038/d41586-023-00056-7 36635510
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- It may state two separate claims as one. 12
- Probably not a substantial claim. 2
In the paper
“ChatGPT achieved ~ 66% and ~ 72% on Basic Life Support and Advanced Cardiovascular Life Support tests, respectively (5)”
§ Opportunities and risks of ChatGPT in medicine, science, and academic publishing: a modern Promethean dilemma > Can ChatGPT replace physicians?
What the sources say
- Not checked yetcites [5] Fijačko N Gosak L Štiglic G Picard CT John Douma M Can ChatGPT pass the life support exams without entering the American Heart Association Course? Resuscitation 2023 169 97 8 10.1016/j.resuscitation.2023.109732 36775020
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“AI has a tremendous potential to revolutionize health care and make it more efficient by improving diagnostics, detecting medical errors, and reducing the burden of paperwork (3,4)”
§ Opportunities and risks of ChatGPT in medicine, science, and academic publishing: a modern Promethean dilemma > Can ChatGPT replace physicians?
What the sources say
- Not checked yetcites [3] Ahn JS Ebrahimian S McDermott S Lee L Naccarato JF Di Capua FY Association of artificial intelligence-aided chest radiograph interpretation with reader performance and efficiency. JAMA Netw Open ••• 5 e2229289 10.1001/jamanetworkopen.2022.29289 36044215 PMC9434361
- Not checked yetcites [4] Patel SB Lam K ChatGPT: the future of discharge summaries? Lancet Digit Health 2023 5 e145 6 36754724
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Frey and Osborne estimate that, while the probability of administrative health care jobs automation is relatively high (eg, 91% for health information technicians), the probability of automating the jobs of physicians and surgeons is 0.42% (9)”
§ Opportunities and risks of ChatGPT in medicine, science, and academic publishing: a modern Promethean dilemma > Can ChatGPT replace physicians?
What the sources say
- Not checked yetcites [9] Frey CB Osborne MA The future of employment: How susceptible are jobs to computerisation? Technol Forecast Soc Change 2017 114 254 80 10.1016/j.techfore.2016.08.019
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“the ability of ChatGPT to write a text of surprising quality might deceive reviewers and readers, with the final result being an accumulation of dangerous misinformation”
§ Opportunities and risks of ChatGPT in medicine, science, and academic publishing: a modern Promethean dilemma > A chatbot-scientist
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“the quality of the chatbot-generated content can only be improved with additional training and optimization”
What the sources say
- Not checked yetcites [2] ChatGPT: Optimizing Language Models for Dialogue. Available from: https://openai.com/blog/chatgpt/. Accessed: February 24, 2022.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“StackOverflow, a popular forum for computer programming-related discussions, banned the use of ChatGPT-generated text “because the average rate of getting correct answers from ChatGPT is too low, the posting of answers created by ChatGPT is substantially harmful to the site and to users who are asking and looking for correct answers“ (22)”
§ Opportunities and risks of ChatGPT in medicine, science, and academic publishing: a modern Promethean dilemma > A chatbot-scientist
What the sources say
- Not checked yetcites [22] StackOverflow. 2022. Temporary policy: ChatGPT is banned. Available from: https://meta.stackoverflow.com/questions/421831/temporary-policy-chatgpt-is-banned. Accessed: February 25, 2023.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“ChatGPT can provide biased outputs and perpetuate sexist stereotypes (13)”
§ Opportunities and risks of ChatGPT in medicine, science, and academic publishing: a modern Promethean dilemma > Can ChatGPT replace physicians?
What the sources say
- Not checked yetcites [13] Chat GPT Friend or foe? Lancet Digit Health 2023 5 e1 36754723
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“the creators of ChatGPT openly disclosed that the fact that “ChatGPT sometimes writes plausible-sounding but incorrect or nonsensical answers” a “challenging issue to fix“ (2)”
§ Opportunities and risks of ChatGPT in medicine, science, and academic publishing: a modern Promethean dilemma > A chatbot-scientist
What the sources say
- Not checked yetcites [2] ChatGPT: Optimizing Language Models for Dialogue. Available from: https://openai.com/blog/chatgpt/. Accessed: February 24, 2022.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“In medicine, AI might dramatically improve efficacy just by alleviating a fragment of the suffocating paperwork (27), and optimized chatbots (eg, Stanford's BioMedLM) (28) might speed up and improve literature search”
§ Opportunities and risks of ChatGPT in medicine, science, and academic publishing: a modern Promethean dilemma > Where do we go from here?
What the sources say
- Not checked yetcites [27] Siegler JE Patel NN Dine CJ Prioritizing paperwork over patient care: why can’t we do both? J Grad Med Educ 2015 7 16 8 10.4300/JGME-D-14-00494.1 26217415 PMC4507919
- Not checked yetcites [28] Bolton E, Hall D, Yasunaga M, Lee T, Manning C, Liang P. PubMedGPT 2.7B.158. Available from: https://crfm.stanford.edu/2022/12/15/pubmedgpt.html. Accessed: February 25, 2023.
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“the models are not only susceptible to availability, selection, and confirmation bias but are also unreluctant to amplify it (12)”
§ Opportunities and risks of ChatGPT in medicine, science, and academic publishing: a modern Promethean dilemma > Can ChatGPT replace physicians?
What the sources say
- Not checked yetcites [12] Rich AS Gureckis TM Lessons for artificial intelligence from the study of natural stupidity. Nat Mach Intell 2019 1 174 80 10.1038/s42256-019-0038-z
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: Current algorithms are often trained on biased data sets.
In the paper
“Blanco-Gonzalez et al assessed the ability of ChatGPT to assist human authors in writing review articles and concluded that “…ChatGPT is not a useful tool for writing reliable scientific texts without strong human intervention. It lacks the knowledge and expertise necessary to accurately and adequately convey complex scientific concepts and information.” (23)”
§ Opportunities and risks of ChatGPT in medicine, science, and academic publishing: a modern Promethean dilemma > A chatbot-scientist
What the sources say
- Not checked yetcites [23] Blanco-Gonzalez A, Cabezon A, Seco-Gonzalez A, Conde-Torres D, Antelo-Riveiro P, Pineiro A, et al. The role of AI in drug discovery: challenges, opportunities, and strategies. 2022. arXiv:2212.08104. PMC10302890 37375838
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“conversational AIs are just language models trained to sound convincing, but without the ability to interpret and understand the content”
§ Opportunities and risks of ChatGPT in medicine, science, and academic publishing: a modern Promethean dilemma > A chatbot-scientist
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“ChatGPT already wrote essays, scholarly manuscripts, and computer code, summarized scientific literature, and performed statistical analyses (18,19)”
§ Opportunities and risks of ChatGPT in medicine, science, and academic publishing: a modern Promethean dilemma > A chatbot-scientist
What the sources say
- Not checked yetcites [18] van Dis EAM Bollen J Zuidema W van Rooij R Bockting CL ChatGPT: five priorities for research. Nature 2023 614 224 6 10.1038/d41586-023-00288-7 36737653
- Not checked yetcites [19] 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 6 10.1016/S2589-7500(23)00019-5 36754725
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper relies on this without support.
The implementation of conversational AI in medicine, science, and academic publishing is inevitable.
“As the inevitable implementation of this disruptive technology will have far-reaching consequences for medicine, science, and academic publishing, we need to discuss both the opportunities and risks of its use.”
The paper relies on this without support.
Conversational AIs are here to stay and will improve with additional training and optimization.
“The powerful disruptive technology of conversational AIs is here to stay, and we can only expect them to improve with additional training and optimization.”
The paper relies on this without support.
A chatbot cannot be held accountable for its work, and there is no legal framework to determine who owns the rights to AI-generated work.
“A chatbot cannot be held accountable for its work, and there is no legal framework to determine who owns the rights to the AI-generated work – the author of the manuscript, the author of the AI, or the (unknown) authors who contributed training data?”
The paper relies on this without support.
ChatGPT often fails to disclose the source of information.
“since ChatGPT often fails to disclose the source of information, who is to blame for plagiarism if the chatbot decides to plagiarize?”
The paper relies on this without support.
Current algorithms are often trained on biased data sets.
“current algorithms are often trained on biased data sets”
The paper relies on this without support.
Context and nuance are critical for safe and effective patient care.
“two things critical for safe and effective patient care, which requires the implementation of medical knowledge, concepts, and principles in real-world settings”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Systems parse biomarker requirements, prior therapies, and performance status from EHRs, identify potentially eligible patients, and flag candidates for screening [56,57,58,59,60,61,62].”
From Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery (2025) · cites this paper as [62]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Opportunities and risks of ChatGPT in medicine, science, and academic publishing: a modern Promethean dilemma > References
“Accessed: February 24, 2022.”
§ Opportunities and risks of ChatGPT in medicine, science, and academic publishing: a modern Promethean dilemma > References
“Bartz D, Bartz D. 2023. As ChatGPT’s popularity explodes, U.S. lawmakers take an interest.”
Citations
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ChatGPT: five priorities for research
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ChatGPT: the future of discharge summaries?
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The role of AI in drug discovery: challenges, opportunities, and strategies
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Abstracts written by ChatGPT fool scientists
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Generating scholarly content with ChatGPT: Ethical challenges for medical publishing
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Active learning machine learns to create new quantum experiments
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ChatGPT passing USMLE shines a spotlight on the flaws of medical education
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Can ChatGPT pass the life support exams without entering the American heart association course?
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Han, J., Davids, J., Ashrafian, H., Darzi, A. W., Elson, D. S., & Sodergren, M. H. (2021) · International Journal of Medical Robotics and Computer Assisted Surgery · doi:10.1002/rcs.2358 · Cited by 86
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Prioritizing Paperwork Over Patient Care: Why Can't We Do Both?
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Cited by
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Sallam, M. (2023) · Healthcare · doi:10.3390/healthcare11060887 · Cited by 3,126
“Thus, the application of ChatGPT to advance academia and health care should be carried out ethically and responsibly, taking into account the potential risks and concerns it entails [47,89].”
BackgroundUnlocking the opportunities through ChatGPT Tool towards ameliorating the education system
Javaid, M., Haleem, A., Singh, R. P., Khan, S., & Khan, I. H. (2023) · BenchCouncil Transactions on Benchmarks, Standards and Evaluations · doi:10.1016/j.tbench.2023.100115 · Cited by 535
ChatGPT for healthcare services: An emerging stage for an innovative perspective
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Imran, M., & Almusharraf, N. (2023) · Contemporary Educational Technology · doi:10.30935/cedtech/13605 · Cited by 421
Younis, H. A., Eisa, T. A. E., Nasser, M., Sahib, T. M., Noor, A. A., Alyasiri, O. M., Salisu, S., Hayder, I. M., & Younis, H. A. (2024) · Diagnostics · doi:10.3390/diagnostics14010109 · Cited by 242
Metadata
- Authors
- Homolak, J.
- Affiliations
- University of Zagreb (HR)
- Corresponding author
- Homolak, Jan
- ORCID
- Homolak, Jan
- Venue
- Croatian Medical Journal
- Publisher
- Croatian Medical Journals
- Published
- 2023-02
- Volume
- 64
- Issue
- 1
- Pages
- 1-3
- ISSN
- 0353-9504 (ISSN-L)
- 1332-8166
- PMC
- PMC10028563
- OpenAlex
- W4323035111
- Semantic Scholar
- 49455e5f5ee94b43c13b039c9353b972ccf05286
- Cited by
- 208
- References
- 23
- Citations by year
- 2026: 17 · 2025: 37 · 2024: 75 · 2023: 79
- FWCI
- 8.28
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- Croatian Medical Journal (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- University of Zagreb University Computing Centre (SRCE) (submittedVersion, other-oa)
- Funders
- Klinička Bolnica Dubrava
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Infectious Diseases › COVID-19 Clinical Research Studies
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- ChatGPT, large language models, artificial intelligence in healthcare, scholarly publishing, research integrity, CURB-65, 4C Mortality Score, 30-day mortality, risk stratification
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-07, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-07 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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