Artificial intelligence in clinical practice: a cross-sectional survey of paediatric surgery residents’ perspectives DOI Creative Commons
Francesca Gigola,

Tommaso Amato,

Marco Del Riccio

и другие.

BMJ Health & Care Informatics, Год журнала: 2025, Номер 32(1), С. e101456 - e101456

Опубликована: Май 1, 2025

Objectives The aim of this study was to compare the performances residents and ChatGPT in answering validated questions assess paediatric surgery residents’ acceptance, perceptions readiness integrate artificial intelligence (AI) into clinical practice. Methods We conducted a cross-sectional using randomly selected cases on topics. examined acceptance AI before after comparing their results ChatGPT’s Unified Theory Acceptance Use Technology 2 (UTAUT2) model. Data analysis performed Jamovi V.2.4.12.0. Results 30 participated. ChatGPT-4.0’s median score 13.75, while ChatGPT-3.5’s 8.75. among 8.13. Differences appeared statistically significant. outperformed specifically definition (ChatGPT-4.0 vs residents, p<0.0001; ChatGPT-3.5 p=0.03). In UTAUT2 Questionnaire, respondents expressed more positive evaluation with higher mean values for each construct lower fear technology learning about test scores. Discussion better than knowledge-based simple cases. accuracy declined when confronted complex questions. UTAUT questionnaire showed that potential could lead shift perception, resulting attitude towards AI. Conclusion Our reveals receptivity AI, especially being its efficacy. These highlight importance integrating AI-related topics medical curricula residency help future physicians surgeons understand advantages limitations

Язык: Английский

Artificial intelligence in public health: promises, challenges, and an agenda for policy makers and public health institutions DOI Creative Commons
Димитра Пантели, Keyrellous Adib, S Buttigieg

и другие.

The Lancet Public Health, Год журнала: 2025, Номер unknown

Опубликована: Фев. 1, 2025

Artificial intelligence (AI) can rapidly analyse large and complex datasets, extract tailored recommendations, support decision making, improve the efficiency of many tasks that involve processing data, text, or images. As such, AI has potential to revolutionise public health practice research, but accompanying challenges need be addressed. used surveillance, epidemiological communication, allocation resources, other forms making. It also productivity in daily work. Core its widespread adoption span equity, accountability, data privacy, for robust digital infrastructures, workforce skills. Policy makers must acknowledge regulatory frameworks covering lifecycle relevant technologies are needed, alongside sustained investment infrastructure development. Public institutions play a key part advancing meaningful use by ensuring their staff up date regarding existing provisions ethical principles development technologies, thinking about how prioritise equity design implementation, investing systems securely process volumes needed applications governance cybersecurity, promoting through clear guidelines align with human rights good, considering AI's environmental impact.

Язык: Английский

Процитировано

1

Psychological Research Contributions to Urgent Global Health Challenges for the Next Decade DOI
Serena Barello

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0

Artificial intelligence in clinical practice: a cross-sectional survey of paediatric surgery residents’ perspectives DOI Creative Commons
Francesca Gigola,

Tommaso Amato,

Marco Del Riccio

и другие.

BMJ Health & Care Informatics, Год журнала: 2025, Номер 32(1), С. e101456 - e101456

Опубликована: Май 1, 2025

Objectives The aim of this study was to compare the performances residents and ChatGPT in answering validated questions assess paediatric surgery residents’ acceptance, perceptions readiness integrate artificial intelligence (AI) into clinical practice. Methods We conducted a cross-sectional using randomly selected cases on topics. examined acceptance AI before after comparing their results ChatGPT’s Unified Theory Acceptance Use Technology 2 (UTAUT2) model. Data analysis performed Jamovi V.2.4.12.0. Results 30 participated. ChatGPT-4.0’s median score 13.75, while ChatGPT-3.5’s 8.75. among 8.13. Differences appeared statistically significant. outperformed specifically definition (ChatGPT-4.0 vs residents, p<0.0001; ChatGPT-3.5 p=0.03). In UTAUT2 Questionnaire, respondents expressed more positive evaluation with higher mean values for each construct lower fear technology learning about test scores. Discussion better than knowledge-based simple cases. accuracy declined when confronted complex questions. UTAUT questionnaire showed that potential could lead shift perception, resulting attitude towards AI. Conclusion Our reveals receptivity AI, especially being its efficacy. These highlight importance integrating AI-related topics medical curricula residency help future physicians surgeons understand advantages limitations

Язык: Английский

Процитировано

0