Free-tier large language models as de facto cancer information sources in low- and middle-income countries
Abstract
Eleven free-tier conversational AI assistants (ChatGPT, Google Gemini, Microsoft Copilot, Meta AI, Perplexity, Grok, Mistral Le Chat, DeepAI, DuckDuckGo AI, DeepSeek, Kimi) were each given the same 24-item cancer prompt library, in a fresh session, verbatim, with no follow-up turns. Responses were collected 21-24 July 2026 from a device in Bangladesh. That gives 11 x 24 = 264 responses. Every response was scored on six 0-5 domains by two reviewers with the platform masked; scores more than one point apart were adjudicated to consensus, and the two safety domains (D2, D6) were resolved to the more conservative value: D1 clinical correctness D2 patient safety D3 care-seeking appropriateness D4 LMIC feasibility D5 lay clarity D6 absence of harm composite = sum of D1..D6, range 0-30 Prompt library structure (4 domains, 24 items): S1-S6 cancer symptom interpretation SC1-SC6 cancer screening guidance T1-T6 treatment decision support L1-L6 LMIC-specific resource-constrained scenarios Within S, SC and T the odd-numbered item is the "standard" version and the even-numbered item is the "LMIC-paired" version of the same clinical topic, so there are 9 matched standard/LMIC pairs. L1-L6 are LMIC-specific and have no standard counterpart. Net: 9 standard + 9 LMIC-paired + 6 LMIC-specific = 24.
