Medical accuracy of artificial intelligence chatbots in oncology: a scoping review DOI Creative Commons
David Chen,

Kate Avison,

Saif Alnassar

и другие.

The Oncologist, Год журнала: 2025, Номер 30(4)

Опубликована: Март 29, 2025

Abstract Background Recent advances in large language models (LLM) have enabled human-like qualities of natural competency. Applied to oncology, LLMs been proposed serve as an information resource and interpret vast amounts data a clinical decision-support tool improve outcomes. Objective This review aims describe the current status medical accuracy oncology-related LLM applications research trends for further areas investigation. Methods A scoping literature search was conducted on Ovid Medline peer-reviewed studies published since 2000. We included primary that evaluated model applied oncology settings. Study characteristics outcomes were extracted landscape LLMs. Results Sixty based inclusion exclusion criteria. The majority health question-answer style examinations (48%), followed by diagnosis (20%) management (17%). number utility fine-tuning prompt-engineering increased over time from 2022 2024. Studies reported advantages accurate resource, reduction clinician workload, improved accessibility readability information, while noting disadvantages such poor reliability, hallucinations, need oversight. Discussion There exists significant interest application with particular focus decision support tool. However, is needed validate these tools external hold-out datasets generalizability across diverse scenarios, underscoring supervision tools.

Язык: Английский

CNN-BILSTM Based-Hybrid Automated Model for Arabic Medical Question Categorization DOI

Mohammed Bahbib,

Majid Ben Yakhlef, Lahcen Tamym

и другие.

Operations Research Forum, Год журнала: 2025, Номер 6(2)

Опубликована: Март 24, 2025

Язык: Английский

Процитировано

0
Assessing ChatGPT 4.0’s Capabilities in the United Kingdom Medical Licensing Examination (UKMLA): A Robust Categorical Analysis DOI Creative Commons

Octavi Casals-Farre,

Ravanth Baskaran, Aditya Singh

и другие.

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Апрель 15, 2025

Язык: Английский

Процитировано

0
Generative AI responses are a dime a dozen; Making them count is the challenge – Evaluating information presentation styles in healthcare chatbots using hierarchical Bayesian regression models DOI Creative Commons

Samuel N. Koscelny,

Sara Sadralashrafi,

David M. Neyens

и другие.

Applied Ergonomics, Год журнала: 2025, Номер 128, С. 104515 - 104515

Опубликована: Апрель 17, 2025

The emergence of large language models offers new opportunities to deliver effective healthcare information through web-based chatbots. Health is often complex and technical, making it crucial design human-AI interactions that effectively meet user needs. Employing a 2x2 between subjects design, we controlled for two independent variables: communication style (conversational vs. informative) (technical non-technical). We used hierarchical Bayesian regression assess the impact varying presentation styles on effectiveness, trustworthiness, usability. findings revealed perceptions low usability significantly decreased effectiveness chatbot. Additionally, participants exposed conversational chatbot had increased likelihoods perceive with higher but were also more likely be less trusting These results indicate can experience insights future research chatbots other AI systems.

Язык: Английский

Процитировано

0
Adapting Communication Styles in Health Chatbot using Large Language Models to Support Family Caregivers from Multicultural Backgrounds DOI

Rebekah Lee Baik,

Stephanie Lee, Serena Jinchen Xie

и другие.

Опубликована: Апрель 23, 2025

Язык: Английский

Процитировано

0
Medical accuracy of artificial intelligence chatbots in oncology: a scoping review DOI Creative Commons
David Chen,

Kate Avison,

Saif Alnassar

и другие.

The Oncologist, Год журнала: 2025, Номер 30(4)

Опубликована: Март 29, 2025

Abstract Background Recent advances in large language models (LLM) have enabled human-like qualities of natural competency. Applied to oncology, LLMs been proposed serve as an information resource and interpret vast amounts data a clinical decision-support tool improve outcomes. Objective This review aims describe the current status medical accuracy oncology-related LLM applications research trends for further areas investigation. Methods A scoping literature search was conducted on Ovid Medline peer-reviewed studies published since 2000. We included primary that evaluated model applied oncology settings. Study characteristics outcomes were extracted landscape LLMs. Results Sixty based inclusion exclusion criteria. The majority health question-answer style examinations (48%), followed by diagnosis (20%) management (17%). number utility fine-tuning prompt-engineering increased over time from 2022 2024. Studies reported advantages accurate resource, reduction clinician workload, improved accessibility readability information, while noting disadvantages such poor reliability, hallucinations, need oversight. Discussion There exists significant interest application with particular focus decision support tool. However, is needed validate these tools external hold-out datasets generalizability across diverse scenarios, underscoring supervision tools.

Язык: Английский

Процитировано

0