Published: Nov. 23, 2024
Language: Английский
Cancer Medicine, Journal Year: 2025, Volume and Issue: 14(1)
Published: Jan. 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.
Language: Английский
Citations
3
Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)
Published: Jan. 9, 2025
Large language models (LLMs) are fundamentally transforming human-facing applications in the health and well-being domains: boosting patient engagement, accelerating clinical decision-making, facilitating medical education. Although state-of-the-art LLMs have shown superior performance several conversational applications, evaluations within nutrition diet still insufficient. In this paper, we propose to employ Registered Dietitian (RD) exam conduct a standard comprehensive evaluation of LLMs, GPT-4o, Claude 3.5 Sonnet, Gemini 1.5 Pro, assessing both accuracy consistency queries. Our includes 1050 RD questions encompassing topics proficiency levels. addition, for first time, examine impact Zero-Shot (ZS), Chain Thought (CoT), with Self Consistency (CoT-SC), Retrieval Augmented Prompting (RAP) on responses. findings revealed that while these obtained acceptable overall performance, their results varied considerably different prompts question domains. GPT-4o CoT-SC prompting outperformed other approaches, whereas Pro ZS recorded highest consistency. For 3.5, CoT improved accuracy, RAP was particularly effective answer Expert level questions. Consequently, choosing appropriate LLM technique, tailored specific domain, can mitigate errors potential risks chatbots.
Language: Английский
Citations
3
Advances in Nutrition, Journal Year: 2025, Volume and Issue: unknown, P. 100377 - 100377
Published: Jan. 1, 2025
Personalized Nutrition (PN) represents an approach aimed at delivering tailored dietary recommendations, products or services to support both prevention and treatment of nutrition-related conditions improve individual health using genetic, phenotypic, medical, nutritional, other pertinent information. However, current approaches have yielded limited scientific success in improving diets mitigating diet-related conditions. In addition, PN currently caters a specific subgroup the population rather than having widespread impact on diet level. Addressing these challenges requires integrating traditional biomedical assessment methods with psycho-behavioral, novel digital diagnostic for comprehensive data collection, which holds considerable promise alleviating present shortcomings. This not only allows deriving personalized goals ("what should be achieved") but also customizing behavioral change processes ("how bring about change"). We herein outline discuss concept "Adaptive Advice Systems" (APNASs), blends from three domains: 1) biomedical/health phenotyping; 2) stable dynamic signatures; 3) food environment data. behavior are envisaged no longer based solely static will adapt dynamically in-time in-situ individual-specific To successfully integrate biomedical, environmental guidance, advanced tools (e.g., sensors) artificial intelligence (AI)-based essential. conclusion, integration established paradigms great potential transitioning its focus elite nutrition widely accessible tool that delivers meaningful benefits general population.
Language: Английский
Citations
1
Journal of Nutrition, Journal Year: 2025, Volume and Issue: unknown
Published: Jan. 1, 2025
Citations
1
Journal of Medical Internet Research, Journal Year: 2025, Volume and Issue: 27, P. e70535 - e70535
Published: March 19, 2025
Chronic diseases are a major global health burden, accounting for nearly three-quarters of the deaths worldwide. Large language models (LLMs) advanced artificial intelligence systems with transformative potential to optimize chronic disease management; however, robust evidence is lacking. This review aims synthesize on feasibility, opportunities, and challenges LLMs across management spectrum, from prevention screening, diagnosis, treatment, long-term care. Following PRISMA (Preferred Reporting Items Systematic Reviews Meta-Analysis) guidelines, 11 databases (Cochrane Central Register Controlled Trials, CINAHL, Embase, IEEE Xplore, MEDLINE via Ovid, ProQuest Health & Medicine Collection, ScienceDirect, Scopus, Web Science Core China National Knowledge Internet, SinoMed) were searched April 17, 2024. Intervention simulation studies that examined in included. The methodological quality included was evaluated using rating rubric designed simulation-based research risk bias nonrandomized interventions tool quasi-experimental studies. Narrative analysis descriptive figures used study findings. Random-effects meta-analyses conducted assess pooled effect estimates feasibility management. A total 20 general-purpose (n=17) retrieval-augmented generation-enhanced (n=3) diseases, including cancer, cardiovascular metabolic disorders. demonstrated spectrum by generating relevant, comprehensible, accurate recommendations (pooled rate 71%, 95% CI 0.59-0.83; I2=88.32%) having higher accuracy rates compared (odds ratio 2.89, 1.83-4.58; I2=54.45%). facilitated equitable information access; increased patient awareness regarding ailments, preventive measures, treatment options; promoted self-management behaviors lifestyle modification symptom coping. Additionally, facilitate compassionate emotional support, social connections, care resources improve outcomes diseases. However, face addressing privacy, language, cultural issues; undertaking tasks, medication, comorbidity personalized regimens real-time adjustments multiple modalities. have transform at individual, social, levels; their direct application clinical settings still its infancy. multifaceted approach incorporates data security, domain-specific model fine-tuning, multimodal integration, wearables crucial evolution into invaluable adjuncts professionals PROSPERO CRD42024545412; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024545412.
Language: Английский
Citations
0
Published: March 19, 2025
Language: Английский
Citations
0
ACM Transactions on Multimedia Computing Communications and Applications, Journal Year: 2025, Volume and Issue: unknown
Published: March 20, 2025
Food-centered study has received more attention in the multimedia community for its profound impact on our survival, nutrition and health, pleasure enjoyment. Our experience of food is typically multi-sensory: we see objects, smell odors, taste flavors, feel texture, hear sounds when chewing. Therefore, multimodal learning vital food-centered study, which aims to relate information from multiple modalities support various tasks, ranging recognition, retrieval, generation, recommendation interaction, enabling applications different fields like healthcare agriculture. However, there no surveys this topic knowledge. To fill gap, paper formalizes comprehensively typical technical achievements, existing datasets provide blueprint with researchers practitioners. Based current state art, identify both open research issues promising directions, such as benchmark construction, foundation model construction multimodality diet estimation. We also point out that closer cooperation between science can handle some challenges meanwhile up new opportunities advance fast development learning. This first comprehensive survey anticipate about 170 reviewed articles benefit academia industry beyond.
Language: Английский
Citations
0
Smart Health, Journal Year: 2025, Volume and Issue: unknown, P. 100570 - 100570
Published: March 1, 2025
Language: Английский
Citations
0
World Wide Web, Journal Year: 2025, Volume and Issue: 28(3)
Published: April 7, 2025
Language: Английский
Citations
0
Communications in computer and information science, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 19
Published: Jan. 1, 2025
Language: Английский
Citations
0