Assessing the performance of ChatGPT in bioethics: a large language model’s moral compass in medicine
Chat Generative Pre-Trained Transformer (ChatGPT) has been a growing point of interest in medical education yet has not been assessed in the field of bioethics. This study evaluated the accuracy of ChatGPT-3.5 (April 2023 version) in answering text-based, multiple choice bioethics questions at the l...
| Κύριοι συγγραφείς: | ; ; ; |
|---|---|
| Τύπος μέσου: | Ηλεκτρονική πηγή Άρθρο |
| Γλώσσα: | Αγγλικά |
| Έλεγχος διαθεσιμότητας: | HBZ Gateway |
| Interlibrary Loan: | Interlibrary Loan for the Fachinformationsdienste (Specialized Information Services in Germany) |
| Έκδοση: |
2024
|
| Στο/Στη: |
Journal of medical ethics
Έτος: 2024, Τόμος: 50, Τεύχος: 2, Σελίδες: 97-101 |
| Διαθέσιμο Online: |
Volltext (lizenzpflichtig) Volltext (lizenzpflichtig) |
MARC
| LEADER | 00000naa a22000002c 4500 | ||
|---|---|---|---|
| 001 | 1918775540 | ||
| 003 | DE-627 | ||
| 005 | 20250228102320.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 250228s2024 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1136/jme-2023-109366 |2 doi | |
| 035 | |a (DE-627)1918775540 | ||
| 035 | |a (DE-599)KXP1918775540 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rda | ||
| 041 | |a eng | ||
| 084 | |a 1 |2 ssgn | ||
| 100 | 1 | |a Chen, Jamie |e VerfasserIn |0 (orcid)0000-0003-0572-2291 |4 aut | |
| 245 | 1 | 0 | |a Assessing the performance of ChatGPT in bioethics: a large language model’s moral compass in medicine |
| 264 | 1 | |c 2024 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 520 | |a Chat Generative Pre-Trained Transformer (ChatGPT) has been a growing point of interest in medical education yet has not been assessed in the field of bioethics. This study evaluated the accuracy of ChatGPT-3.5 (April 2023 version) in answering text-based, multiple choice bioethics questions at the level of US third-year and fourth-year medical students. A total of 114 bioethical questions were identified from the widely utilised question banks UWorld and AMBOSS. Accuracy, bioethical categories, difficulty levels, specialty data, error analysis and character count were analysed. We found that ChatGPT had an accuracy of 59.6%, with greater accuracy in topics surrounding death and patient-physician relationships and performed poorly on questions pertaining to informed consent. Of all the specialties, it performed best in paediatrics. Yet, certain specialties and bioethical categories were under-represented. Among the errors made, it tended towards content errors and application errors. There were no significant associations between character count and accuracy. Nevertheless, this investigation contributes to the ongoing dialogue on artificial intelligence’s (AI) role in healthcare and medical education, advocating for further research to fully understand AI systems’ capabilities and constraints in the nuanced field of medical bioethics. | ||
| 601 | |a Performance | ||
| 601 | |a ChatGPT | ||
| 700 | 1 | |a Cadiente, Angelo |e VerfasserIn |4 aut | |
| 700 | 1 | |a Kasselman, Lora J. |e VerfasserIn |4 aut | |
| 700 | 1 | |a Pilkington, Bryan |e VerfasserIn |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Journal of medical ethics |d London : BMJ Publ., 1975 |g 50(2024), 2, Seite 97-101 |h Online-Ressource |w (DE-627)323607802 |w (DE-600)2026397-1 |w (DE-576)260773972 |x 1473-4257 |7 nnas |
| 773 | 1 | 8 | |g volume:50 |g year:2024 |g number:2 |g pages:97-101 |
| 856 | 4 | 0 | |u https://doi.org/10.1136/jme-2023-109366 |x Resolving-System |z lizenzpflichtig |3 Volltext |
| 856 | 4 | 0 | |u https://jme.bmj.com/content/50/2/97 |x Verlag |z lizenzpflichtig |3 Volltext |
| 951 | |a AR | ||
| ELC | |a 1 | ||
| ITA | |a 1 |t 1 | ||
| LOK | |0 000 xxxxxcx a22 zn 4500 | ||
| LOK | |0 001 4675088260 | ||
| LOK | |0 003 DE-627 | ||
| LOK | |0 004 1918775540 | ||
| LOK | |0 005 20250228102320 | ||
| LOK | |0 008 250228||||||||||||||||ger||||||| | ||
| LOK | |0 040 |a DE-Tue135 |c DE-627 |d DE-Tue135 | ||
| LOK | |0 092 |o n | ||
| LOK | |0 852 |a DE-Tue135 | ||
| LOK | |0 852 1 |9 00 | ||
| LOK | |0 935 |a ixzs |a ixzo |a ixrk | ||
| OAS | |a 1 |b inherited from superior work | ||
| ORI | |a SA-MARC-ixtheoa001.raw | ||