| Title: | Artificial intelligence to revolutionize IBD clinical trials : a comprehensive review |
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| Authors: | ID Sedano, Rocio (Author)
ID Solitano, Virginia (Author)
ID Vuyyuru, Sudheer Kumar (Author)
ID Yuan, Yuhong (Author)
ID Hanžel, Jurij (Author)
ID Ma, Christopher (Author)
ID Nardone, Olga Maria (Author)
ID Jairath, Vipul (Author) |
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| Files: |  PDF - Presentation file, download (269,85 KB)
MD5: CDD9307DF03AD2F6F43913118190E2D1
 URL - Source URL, visit https://journals.sagepub.com/doi/10.1177/17562848251321915
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| Language: | English |
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| Typology: | 1.02 - Review Article |
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| Organization: |  UKC LJ - Ljubljana University Medical Centre
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| Abstract: | Integrating artificial intelligence (AI) into clinical trials for inflammatory bowel disease (IBD) has potential to be transformative to the field. This article explores how AIdriven technologies, including machine learning (ML), natural language processing, and predictive analytics, have the potential to enhance important aspects of IBD trials—from patient recruitment and trial design to data analysis and personalized treatment strategies. As AI advances, it has potential to improve long-standing challenges in trial efficiency, accuracy, and personalization with the goal of accelerating the discovery of novel therapies and improve outcomes for people living with IBD. AI can streamline multiple trial phases, from target identification and patient recruitment to data analysis and monitoring. By integrating multiomics data, electronic health records, and imaging repositories, AI can uncover molecular targets and personalize trial strategies, ultimately expediting drug development. However, the adoption of AI in IBD clinical trials encounters significant challenges. These include technical barriers in data integration, ethical concerns regarding patient privacy, and regulatory issues related to AI validation standards. Additionally, AI models risk producing biased outcomes if training datasets lack diversity, potentially impacting underrepresented populations in clinical trials. Addressing these limitations requires standardized data formats, interdisciplinary collaboration, and robust ethical frameworks to ensure inclusivity and accuracy. Continued partnerships among clinicians, researchers, data scientists, and regulators will be essential to establish transparent, patient-centered AI frameworks. By overcoming these obstacles, AI has the potential to enhance the efficiency, equity, and efficacy of IBD clinical trials, ultimately benefiting patient care |
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| Keywords: | AI, artificial intelligence, clinical trials |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Year of publishing: | 2025 |
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| Number of pages: | str. 1-20 |
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| Numbering: | Vol. 18 |
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| PID: | 20.500.12556/DiRROS-27841  |
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| UDC: | 61 |
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| ISSN on article: | 1756-2848 |
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| DOI: | 10.1177/17562848251321915 |
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| COBISS.SI-ID: | 227469827 |
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| Note: | Nasl. z nasl. zaslona;
Opis vira z dne 26. 2. 2025;
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| Publication date in DiRROS: | 26.02.2026 |
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| Views: | 130 |
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| Downloads: | 74 |
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