Can Large Language Models Aid Caregivers of Pediatric Cancer Patients in Information Seeking? A Cross‐Sectional Investigation

Cancer Medicine, Год журнала: 2025, Номер 14(1)

Опубликована: Янв. 1, 2025

ABSTRACT Purpose Caregivers in pediatric oncology need accurate and understandable information about their child's condition, treatment, side effects. This study assesses the performance of publicly accessible large language model (LLM)‐supported tools providing valuable reliable to caregivers children with cancer. Methods In this cross‐sectional study, we evaluated four LLM‐supported tools—ChatGPT (GPT‐4), Google Bard (Gemini Pro), Microsoft Bing Chat, SGE—against a set frequently asked questions (FAQs) derived from Children's Oncology Group Family Handbook expert input (In total, 26 FAQs 104 generated responses). Five experts assessed LLM responses using measures including accuracy, clarity, inclusivity, completeness, clinical utility, overall rating. Additionally, content quality was readability, AI disclosure, source credibility, resource matching, originality. We used descriptive analysis statistical tests Shapiro–Wilk, Levene's, Kruskal–Wallis H ‐tests, Dunn's post hoc for pairwise comparisons. Results ChatGPT shows high when by experts. also performed well, especially accuracy clarity responses, whereas Chat SGE had lower scores. Regarding disclosure being AI, it observed less which may have affected maintained balance between response clarity. most readable answered complexity. varied significantly ( p < 0.001) across all evaluations except inclusivity. Through our thematic free‐text comments, emotional tone empathy emerged as unique theme mixed feedback on expectations be empathetic. Conclusion can enhance caregivers' knowledge oncology. Each has strengths areas improvement, indicating careful selection based specific contexts. Further research is required explore application other medical specialties patient demographics, assessing broader applicability long‐term impacts.

Язык: Английский

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Implementation of artificial intelligence approaches in oncology clinical trials: A systematic review

Artificial Intelligence in Medicine, Год журнала: 2025, Номер 161, С. 103066 - 103066

Опубликована: Янв. 18, 2025

Язык: Английский

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AI-generated cancer prevention influencers can target risk groups on social media at low cost

European Journal of Cancer, Год журнала: 2025, Номер 217, С. 115251 - 115251

Опубликована: Янв. 18, 2025

This study explores the potential of Artificial Intelligence (AI)-generated social media influencers to disseminate cancer prevention messages. Utilizing a Generative AI (GenAI) application, we created virtual persona, "Wanda", promote awareness on Instagram. We five posts, addressing most modifiable risk factors for cancer: tobacco consumption, unhealthy diet, sun exposure, alcohol and Human Papillomavirus (HPV) infection. To amplify campaign's reach, posts were boosted using custom-targeted as well an automated advertisement algorithm. An overall budget €100 was equally distributed between two algorithms. Campaign performance assessed based number users reached age distribution audience. The campaign achieved total 9902 recognitions, with cost-efficiency analysis revealing average expenditure €0.013 per reach. economical intervention cost only €0.006 In comparing strategies, observed similar reach but noted differences in demographics Our findings underscore combining generative strategically targeted messages effectively, minimal time financial investment. discuss chances presented by GenAI applications health communication, their implication, impact parasocial relationships content perception. highlights AI-driven scalable tools digital communication.

Язык: Английский

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Cellular Morphometric Biomarkers and Large Language Model Predict Prognosis and Treatment Response in Neuroblastoma Patients: A Retrospective and Double-blind Prospective Single Arm Clinical Study

European Journal of Cancer, Год журнала: 2025, Номер unknown, С. 115273 - 115273

Опубликована: Янв. 1, 2025

Язык: Английский

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Generative AI Chatbots for Reliable Cancer Information: Evaluating web-search, multilingual, and reference capabilities of emerging large language models

European Journal of Cancer, Год журнала: 2025, Номер 218, С. 115274 - 115274

Опубликована: Фев. 4, 2025

Recent advancements in large language models (LLMs) enable real-time web search, improved referencing, and multilingual support, yet ensuring they provide safe health information remains crucial. This perspective evaluates seven publicly accessible LLMs-ChatGPT, Co-Pilot, Gemini, MetaAI, Claude, Grok, Perplexity-on three simple cancer-related queries across eight languages (336 responses: English, French, Chinese, Thai, Hindi, Nepali, Vietnamese, Arabic). None of the 42 English responses contained clinically meaningful hallucinations, whereas 7 294 non-English did. 48 % (162/336) included valid references, but 39 references were.com links reflecting quality concerns. frequently exceeded an eighth-grade level, many outputs were also complex. These findings reflect substantial progress over past 2-years reveal persistent gaps accuracy, reliable reference inclusion, referral practices, readability. Ongoing benchmarking is essential to ensure LLMs safely support global dichotomy meet online standards.

Язык: Английский

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Exploring Caregivers' Acceptance of Conversational AI in Pediatric Cancer Caregiving: A Mixed-Methods Study

Опубликована: Апрель 23, 2025

Язык: Английский

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Demystifying Mental Health Reports Through an LLM-based Approach

Опубликована: Апрель 25, 2025

Язык: Английский

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Large language models for improving cancer diagnosis and management in primary health care settings

Journal of Medicine Surgery and Public Health, Год журнала: 2024, Номер unknown, С. 100157 - 100157

Опубликована: Ноя. 1, 2024

Язык: Английский

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