Safeguarding Patient Trust in the Age of AI: Tackling Health Misinformation with Explainable AI
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908519037403136 |
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| author | Hong, Sueun Fu, Shuojie Serban, Ovidiu Bao, Brianna Kinross, James Toni, Francesa Martin, Guy Vaghela, Uddhav |
| author_facet | Hong, Sueun Fu, Shuojie Serban, Ovidiu Bao, Brianna Kinross, James Toni, Francesa Martin, Guy Vaghela, Uddhav |
| contents | AI-generated health misinformation poses unprecedented threats to patient safety and healthcare system trust globally. This white paper presents an explainable AI framework developed through the EPSRC INDICATE project to combat medical misinformation while enhancing evidence-based healthcare delivery. Our systematic review of 17 studies reveals the urgent need for transparent AI systems in healthcare. The proposed solution demonstrates 95% recall in clinical evidence retrieval and integrates novel trustworthiness classifiers achieving 76% F1 score in detecting biomedical misinformation. Results show that explainable AI can transform traditional 6-month expert review processes into real-time, automated evidence synthesis while maintaining clinical rigor. This approach offers a critical intervention to preserve healthcare integrity in the AI era. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_04052 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Safeguarding Patient Trust in the Age of AI: Tackling Health Misinformation with Explainable AI Hong, Sueun Fu, Shuojie Serban, Ovidiu Bao, Brianna Kinross, James Toni, Francesa Martin, Guy Vaghela, Uddhav Information Retrieval AI-generated health misinformation poses unprecedented threats to patient safety and healthcare system trust globally. This white paper presents an explainable AI framework developed through the EPSRC INDICATE project to combat medical misinformation while enhancing evidence-based healthcare delivery. Our systematic review of 17 studies reveals the urgent need for transparent AI systems in healthcare. The proposed solution demonstrates 95% recall in clinical evidence retrieval and integrates novel trustworthiness classifiers achieving 76% F1 score in detecting biomedical misinformation. Results show that explainable AI can transform traditional 6-month expert review processes into real-time, automated evidence synthesis while maintaining clinical rigor. This approach offers a critical intervention to preserve healthcare integrity in the AI era. |
| title | Safeguarding Patient Trust in the Age of AI: Tackling Health Misinformation with Explainable AI |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2509.04052 |