--- title: "Decoding Informed Consent: Evaluating AI Disclosures in Clinical Research" description: Explore the critical gaps in informed consent for AI in clinical trials and discover a proposed framework to enhance transparency and patient autonomy. image: https://blog.jmir.org/hubfs/StoryTap%20Blog%20-%20Hankun%20Su.png --- [![JMIR Publications logo](https://blog.jmir.org/hs-fs/hubfs/JMIR-Publications_logo_2020_original.png?width=550&height=109&name=JMIR-Publications_logo_2020_original.png "JMIR Publications logo")](http://www.jmirpublications.com) Open main menu Close main menu [Research News](https://blog.jmir.org/tag/research-news) [Artificial Intelligence](https://blog.jmir.org/tag/artificial-intelligence) # Decoding Informed Consent: Evaluating AI Disclosures in Clinical Research ![Liana Ramos, Marketing Associate](https://blog.jmir.org/hs-fs/hubfs/Liana%20Ramos.jpeg?width=45&name=Liana%20Ramos.jpeg) [Liana Ramos, Marketing Associate](https://blog.jmir.org/author/liana-ramos) Published Date: 12 August 2026 12:06 p.m. [Share this blog post on Twitter](https://twitter.com/intent/tweet?text=I+found+this+interesting+blog+post&url=https://blog.jmir.org/decoding-informed-consent-evaluating-ai-disclosures-in-clinical-research) [Share this blog post on Facebook](http://www.facebook.com/share.php?u=https://blog.jmir.org/decoding-informed-consent-evaluating-ai-disclosures-in-clinical-research) [Share this blog post on LinkedIn](http://www.linkedin.com/shareArticle?mini=true&url=https://blog.jmir.org/decoding-informed-consent-evaluating-ai-disclosures-in-clinical-research) ![Decoding Informed Consent: Evaluating AI Disclosures in Clinical Research](https://blog.jmir.org/hubfs/StoryTap%20Blog%20-%20Hankun%20Su.png) Artificial intelligence is rapidly shifting from experimental code to point-of-care medical decision-making. As machine learning models gain autonomy in diagnosing diseases and tailoring treatments, ensuring true patient autonomy through informed consent has become a vital ethical challenge. | --- **Key Takeaways** | | --- | | Significant Transparency Gaps: Current informed consent documentation often fails to provide essential information; over half (58%) of the reviewed documents did not disclose the role of the AI system, and 18.4% omitted potential risks entirely, which creates a risk of therapeutic misconception. | | Poor Accessibility and Readability: Most existing consent forms are too complex for the general public, with only 14% of documents meeting basic criteria for brevity and readability, presenting a major barrier to true patient autonomy. | | Call for Standardized Frameworks: To address these deficits, researchers proposed the MRIC-AI (Minimum Requirements for Informed Consent in AI-Related Clinical Trials) checklist, which mandates clear disclosure of AI autonomy, balanced risk-benefit communication, participant-centered language, and explicit protocols for data governance. --- | Listen to this blog post 3:41 ---   In a [cross-sectional study](https://www.jmir.org/2026/1/e94504/) published in the [*Journal of Medical Internet Research*](https://www.jmir.org/), researcher Hankun Su and colleagues at [Xiangya Hospital Central South University](https://xysm.csu.edu.cn/EN/Home.htm) evaluated the transparency, readability, and data governance of informed consent documentation across 114 AI-involved clinical trials registered on ClinicalTrials.gov. Their findings reveal a significant gap between ethical principles and real-world consent practices. ### Critical Gaps in Transparency and Readability While public surveys show that over 80% of individuals believe they should be explicitly notified whenever AI is used in their care, current consent documentation often keeps participants in the dark: - **Omission of AI Mechanisms:** Over half (**58%**, 66/114) of informed consent documents failed to disclose the type or intended role of the AI system, leaving participants unaware of the technology driving their care. - **Incomplete Risk Disclosure:** While 88.6% of forms outlined potential benefits, **18.4%** (21/114) omitted risks entirely, creating a risk of therapeutic misconception. - **Dense and Complex Language:** Only **14%** (16/114) of documents met basic criteria for brevity (fewer than 15,000 characters) and readability (Simple Measure of Gobbledygook score under 13). Forms for higher-risk trials were no easier to comprehend than those for low-risk studies. - **Inconsistent Post-Withdrawal Data Handling:** When participants withdraw from a trial, protocols for handling their data varied widely: 44.7% provided no clear policy, 26.3% specified data destruction, 25.4% allowed continued data use, and only **3.5%** offered participants a choice. ### A Framework for Minimum Requirements in AI Consent To address these deficits, Su and colleagues proposed the Minimum Requirements for Informed Consent in AI-Related Clinical Trials (MRIC-AI) checklist to help researchers and institutional review boards upgrade consent quality: 1. **AI-Specific Disclosure:** Clearly state the AI type (e.g., deep learning vs. machine learning), its specific clinical role, and its degree of autonomy. 2. **Balanced Risk-Benefit Communication:** Present algorithmic risks (e.g., bias, re-identification, misdiagnosis) with equal prominence to potential clinical benefits. 3. **Participant-Centered Readability:** Target a reading level of 10th grade or lower for high-risk trials, keep length under 15,000 characters, and incorporate tested visual aids. 4. **Transparent Data Governance:** Clearly outline data storage, retention, applicable privacy laws, and explicit participant choices regarding post-withdrawal data use. 5. **Dynamic Consent:** Establish protocols to provide accessible updates if an AI model evolves during the trial. |   | In this video, researcher Hankun Su from Xiangya Hospital Central South University presents a cross-sectional content analysis evaluating the prevalence, clarity, and completeness of informed consent disclosures in artificial intelligence (AI) clinical trials registered on ClinicalTrials.gov. |   | | --- | --- | --- |   Why JMIR? The authors selected the *Journal of Medical Internet Research* to present this work due to its focus on digital health ethics, AI governance, and clinical trial transparency. As AI systems become deeply embedded in human research, this study provides a crucial roadmap for ensuring that patient consent remains meaningful, transparent, and ethically robust. Curious about how AI transparency frameworks are reshaping patient rights in clinical research? [Watch the video](https://www.youtube.com/watch?v=mU5a9Ux4UoY) featuring Hankun Su and [read the full study](https://www.jmir.org/2026/1/e94504/) to explore the MRIC-AI checklist, expert validation results, and regulatory analysis. --- Su H, Xiao F, Chau H, Tong Y, Han S, Cheng X, Che Z, Sun L, Yang Y, Zhao J, Li Y, Li H Informed Consent Disclosures and Minimum Requirements in AI Clinical Trials: Cross-Sectional Analysis J Med Internet Res 2026;28:e94504 URL: [https://www.jmir.org/2026/1/e94504](https://www.jmir.org/2026/1/e94504/) DOI: [10.2196/94504](https://www.jmir.org/2026/1/e94504/) ![Liana Ramos, Marketing Associate](https://blog.jmir.org/hs-fs/hubfs/Liana%20Ramos.jpeg?width=150&name=Liana%20Ramos.jpeg) #### Liana Ramos, Marketing Associate [Follow me on LinkedIn](https://www.linkedin.com/in/lianaramos/) Liana Ramos is a Marketing Associate at JMIR Publications, dedicated to building data-driven systems that connect scholarly content with the communities that need it most. With five years of experience at the intersection of media, publishing, and audience behaviour, she handles automated lifecycle journeys, author recognition programs, and record-scale digital events for JMIR. Passionate about storytelling and digital strategy, she focuses on making open access digital health research discoverable, scalable, and highly engaging. ### Leave a Comment ## Related Articles [![Solving the Privacy Paradox: Generating Anonymous Clinical Trial Data with AI](https://blog.jmir.org/hubfs/Pierre-Antoine%20Gourraud%20%20blog.png)](https://blog.jmir.org/synthetic-data-solving-the-clinical-trial-privacy-paradox) [Research News](https://blog.jmir.org/tag/research-news) ### [Synthetic Data: Solving the Clinical Trial Privacy Paradox](https://blog.jmir.org/synthetic-data-solving-the-clinical-trial-privacy-paradox) The foundation of modern, data-driven medicine rests on high-quality empirical evidence, primarily derived from **Randomized Clinical Trials (RCTs)**. 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Traditionally, this... ![Reviewed by Kayleigh-Ann Clegg, PhD](https://blog.jmir.org/hs-fs/hubfs/Headshot.jpg?width=45&name=Headshot.jpg) [Reviewed by Kayleigh-Ann Clegg, PhD](https://blog.jmir.org/author/reviewed-by-kayleigh-ann-clegg-phd) [Read More](https://blog.jmir.org/fhir4pharma-digitalizing-clinical-trial-protocols-with-fhir) #### About - Menu Item One - Menu Item Two - Menu Item Three #### Services - Menu Item One - Menu Item Two - Menu Item Three #### News - Menu Item One - Menu Item Two - Menu Item Three Follow us on Facebook Follow us on LinkedIn Follow us on Twitter Follow us on Instagram ```json { "@context" : "https://schema.org", "@type" : "BlogPosting", "author" : { "@type" : "Person", "name" : "Liana Ramos, Marketing Associate", "url" : "https://blog.jmir.org/author/liana-ramos" }, "dateModified" : "2026-09-16T19:36:33.507Z", "datePublished" : "2026-08-12T16:06:44.000Z", "headline" : "Decoding Informed Consent: Evaluating AI Disclosures in Clinical Research", "image" : [ "https://blog.jmir.org/hubfs/StoryTap%20Blog%20-%20Hankun%20Su.png" ], "mainEntityOfPage" : { "@id" : "https://blog.jmir.org/decoding-informed-consent-evaluating-ai-disclosures-in-clinical-research", "@type" : "WebPage" }, "publisher" : { "@type" : "Organization", "logo" : { "@type" : "ImageObject", "url" : "https://blog.jmir.org/hubfs/JMIR-Publications_logo_2020_original.png" }, "name" : "JMIR Publications" } } ```