ArticleJMIR cancer2026
Quality, Empathy, and Readability of AI Chatbot Responses to the Survivorship Needs of Adolescents and Young Adults With Melanoma: Evaluation Study.
Article in JMIR cancer, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Large Language Model Chatbot Responses to Cancer Survivorship Questions in Hong Kong: Bilingual Evaluation and Prompt Optimization Study.Journal of medical Internet research · 2026Article
- Large language model chatbots as sources of pediatric anesthesia health advice: An evaluation of reliability and readability.Digital healthArticle
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Authors and funding
9 authors.
Funding
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Abstract
Background: Melanoma, a highly aggressive form of skin cancer, is the second most common type of cancer for adolescent and young adult (AYA, ages 15-39 years) patients. AYA patients with melanoma may turn to internet sources, especially artificial intelligence (AI) chatbots, to manage uncertainty about prognosis and treatment. Objective: This study aims to evaluate the quality, empathy, and readability of responses generated by leading AI chatbots when addressing the top unmet needs of AYA patients with melanoma receiving treatment. Methods: Our research team recently surveyed 152 AYA patients with melanoma using the Needs Assessment Service Bridge, a validated instrument that assesses psychosocial needs for AYA patients with cancer. The survey identified the top 5 needs for advanced AYA patients with melanoma receiving treatment. Each need was reframed into a question and brief clinical history, then entered into each chatbot by 5 individuals who cleared their prequestion and postquestion history. Chatbot responses were evaluated to assess information quality (Global Quality Score [GQS] and DISCERN), accessibility and readability (GQS, Flesch Kincaid Grade Level, Flesch Reading Ease), and perceived empathy (Perceived Empathy of Technology Scale [PETS], including domains of Emotional Responsiveness [PETS-ER], Understanding and Trust [PETS-UT]). Results: Across 75 chatbot responses, ChatGPT achieved the highest average quality (mean GQS 4.42, SD 0.32; mean DISCERN 3.24, SD 0.31) and empathy (mean PETS-ER 5.35, SD 1.85; mean PETS-UT 6.36, SD 1.83), though with greater variability. Copilot produced the lowest quality and empathy scores, while Gemini responses were consistently midrange. PETS-UT exceeded PETS-ER across all models, suggesting stronger cognitive empathy than emotional responsiveness. Readability analysis showed outputs exceeded the average US reading level (mean Flesch Kincaid Grade Level 11.82, SD 1.44; mean FRE 38.60, SD 9.00), limiting accessibility. The most readable responses were found in question 2, which also scored higher in quality and empathy, whereas questions 4 and 5 produced the most complex, difficult-to-read responses corresponding with lower quality and empathy ratings. Conclusions: AI chatbots can provide moderately accurate and supportive responses to needs of AYA patients with melanoma, but outputs are inconsistent, written above the recommended reading level for health information, and limited in empathy. Question framing strongly influenced chatbot performance, with more emotional prompts drawing greater empathy, and readability aligning with both quality and empathy. Chatbot use in this population should remain adjunctive, with further research needed to standardize quality, improve readability, and enhance empathetic communication.
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Registered trials
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