Bayesian mediation analysis using patient-reported outcomes from AI chatbots to infer causal pathways in clinical trials.
The integration of artificial intelligence (AI) chatbots into clinical trials offers a transformative approach to collecting patient-reported outcomes (PROs). Despite the increasing use of AI chatbots for real-time, interactive data gathering, systematic frameworks for analyzing these rich datasets-...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0326517 |
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| author | Shihao Shen Jun Yin |
| author_facet | Shihao Shen Jun Yin |
| author_sort | Shihao Shen |
| collection | DOAJ |
| description | The integration of artificial intelligence (AI) chatbots into clinical trials offers a transformative approach to collecting patient-reported outcomes (PROs). Despite the increasing use of AI chatbots for real-time, interactive data gathering, systematic frameworks for analyzing these rich datasets-especially in uncovering causal relationships-remain limited. This study addresses this gap by applying a Bayesian mediation framework to PROs collected via AI chatbot interactions, uncovering causal pathways linking treatment effects to outcomes through mediators like adverse events and patient-specific covariates. Using a simulation-based approach with GPT-4o, synthetic patient-chatbot dialogues were generated to evaluate the performance of the Bayesian mediation framework, which effectively decomposed total effects into direct and indirect components while quantifying uncertainty through credible intervals. The results demonstrated low bias (<0.05), robust coverage (>85%), in estimation of the direct, indirect effect and other variables of the mediation pathways, underscoring its potential to improve clinical trial data accuracy and depth. By integrating AI chatbot-based PRO collection with Bayesian mediation analysis, this study presents a scalable and adaptive framework for quantifying causal pathways, enhancing the quality of patient-reported data, and supporting personalized, data-driven decision-making in clinical trials. |
| format | Article |
| id | doaj-art-e35e18cdad684e62aed089291acce10c |
| institution | DOAJ |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-e35e18cdad684e62aed089291acce10c2025-08-20T02:49:43ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01207e032651710.1371/journal.pone.0326517Bayesian mediation analysis using patient-reported outcomes from AI chatbots to infer causal pathways in clinical trials.Shihao ShenJun YinThe integration of artificial intelligence (AI) chatbots into clinical trials offers a transformative approach to collecting patient-reported outcomes (PROs). Despite the increasing use of AI chatbots for real-time, interactive data gathering, systematic frameworks for analyzing these rich datasets-especially in uncovering causal relationships-remain limited. This study addresses this gap by applying a Bayesian mediation framework to PROs collected via AI chatbot interactions, uncovering causal pathways linking treatment effects to outcomes through mediators like adverse events and patient-specific covariates. Using a simulation-based approach with GPT-4o, synthetic patient-chatbot dialogues were generated to evaluate the performance of the Bayesian mediation framework, which effectively decomposed total effects into direct and indirect components while quantifying uncertainty through credible intervals. The results demonstrated low bias (<0.05), robust coverage (>85%), in estimation of the direct, indirect effect and other variables of the mediation pathways, underscoring its potential to improve clinical trial data accuracy and depth. By integrating AI chatbot-based PRO collection with Bayesian mediation analysis, this study presents a scalable and adaptive framework for quantifying causal pathways, enhancing the quality of patient-reported data, and supporting personalized, data-driven decision-making in clinical trials.https://doi.org/10.1371/journal.pone.0326517 |
| spellingShingle | Shihao Shen Jun Yin Bayesian mediation analysis using patient-reported outcomes from AI chatbots to infer causal pathways in clinical trials. PLoS ONE |
| title | Bayesian mediation analysis using patient-reported outcomes from AI chatbots to infer causal pathways in clinical trials. |
| title_full | Bayesian mediation analysis using patient-reported outcomes from AI chatbots to infer causal pathways in clinical trials. |
| title_fullStr | Bayesian mediation analysis using patient-reported outcomes from AI chatbots to infer causal pathways in clinical trials. |
| title_full_unstemmed | Bayesian mediation analysis using patient-reported outcomes from AI chatbots to infer causal pathways in clinical trials. |
| title_short | Bayesian mediation analysis using patient-reported outcomes from AI chatbots to infer causal pathways in clinical trials. |
| title_sort | bayesian mediation analysis using patient reported outcomes from ai chatbots to infer causal pathways in clinical trials |
| url | https://doi.org/10.1371/journal.pone.0326517 |
| work_keys_str_mv | AT shihaoshen bayesianmediationanalysisusingpatientreportedoutcomesfromaichatbotstoinfercausalpathwaysinclinicaltrials AT junyin bayesianmediationanalysisusingpatientreportedoutcomesfromaichatbotstoinfercausalpathwaysinclinicaltrials |