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PERSPECTIVE ARTICLE

Model-as-evidence framework established using AI-integrated organoids and organs-on-chips systems

Peixi Wang1,2† ,  Berenica Santoso1† ,  Songlin He1,2† ,  Liangbin Zhou1,2 ,  Yuwei Zhang1,2 ,  Yichi Zhang1 ,  Shing Yui Ho1,2 ,  Rocky S. Tuan2,3,4* ,  Zhong Alan Li1,2,3,4*
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1 Department of Biomedical Engineering, Faculty of Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China
2 InnoHK Center for Neuromusculoskeletal Restorative Medicine, Hong Kong Science and Technology Park, Hong Kong SAR, China
3 Institute for Tissue Engineering and Regenerative Medicine, School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
4 Peter Hung Pain Research Institute, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
†These authors contributed equally to this work.
IJAMD, 026150008 https://doi.org/10.36922/IJAMD026150008
Received: 11 April 2026 | Revised: 17 May 2026 | Accepted: 29 May 2026 | Published online: 12 June 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

The predictive capability crisis in drug discovery and development remains a significant factor hindering medical progress. Among emerging solutions, organoids offer the ability to reflect the biological fidelity of tissues, whereas organs-on-chips (OoCs) demonstrate strengths in simulating dynamic tissue microenvironments and enabling multi‑organ interactions. Herein, we present a unifying artificial intelligence (AI)-orchestrated framework that incorporates both platforms into verifiable and regulatable “model-as-evidence” (MAE) pathways capable of self-optimization with researcher oversight. AI is employed as an indispensable computational integration layer addressing three bottlenecks that render conventional approaches infeasible: (i) combinatorial explosion in high-dimensional parameter spaces, (ii) multi-scale coupling between molecular interactions and tissue-scale level morphogenesis, and (iii) requirement for real-time adaptive control of dynamic microenvironments exceeding human cognitive bandwidth. At the organoid level, this integration enables multi‑objective optimization to improve reproducibility, reduce resource use, and achieve predictive modeling to capture extracellular matrix–cells interactions. For OoCs, active learning algorithms condense protracted design cycles into efficient, goal-directed experimentation, and deep reinforcement learning sustains physiological steady states through adaptive, real-time control. The convergence of these systems within an integrated platform enables standardized, cross-comparable phenotyping pipelines that transform heterogeneous experimental data into a universal quantitative language, and harmonized evaluation benchmarks align experimental outputs across laboratories. Together, this framework generates curated evidence packages that directly address regulatory concerns related to efficacy and safety. By systematizing experimental design, parameter control, and interpretive analytics, this AI-orchestrated approach elevates organoid and OoC technologies from specialized crafts to a robust engineering discipline. It establishes a virtuous cycle wherein AI accelerates iterative validation and optimization, freeing researchers to focus on higher-order hypothesis generation and translational strategy. Thus, through AI-orchestrated quantification of biological fidelity and engineering rigor, these platforms may mature into credible, scalable, and transformative toolboxes for next-generation biomedical translation.

Graphical abstract
Keywords
Organoid
Organ-on-a-chip
Artificial intelligence
Organoid-on-a-chip
Funding
This work was supported in part by: (1) CUHK Peter Hung Pain Research Institute (to ZAL and RST, PHPRI/2024 /122); (2) InnoHK Center for Neuromusculoskeletal Restorative Medicine (to RST and ZAL), under the Health@InnoHK program, Innovation and Technology Commission (ITC), Hong Kong SAR; (3) National Natural Science Foundation of China (to ZAL, 82302753); (4) Hong Kong Research Grants Council (to ZAL, 24203523); and (5) the Mainland-Hong Kong Technology Cooperation Funding Scheme (MHKTCFS) of ITC, Hong Kong SAR (to RST and ZAL, project #GHP-260-23SZ and project #MHP/101/23; to ZAL, project #GHP/140/22GD). ZAL acknowledges support from the CUHK Vice-Chancellor Early Career Professorship Scheme. RST is supported by the CUHK Lee Quo Wei and Lee Yick Hoi Lun Professorship in Tissue Engineering and Regenerative Medicine. The funders had no role in study design, data collection and analysis, decisions to publish, or preparation of the manuscript.
Conflict of interest
Zhong Alan Li serves as the Editorial Board Member of this journal, but was not in any way involved in the editorial and peer-review process conducted for this paper, directly or indirectly. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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International Journal of AI for Materials and Design, Electronic ISSN: 3029-2573 Print ISSN: 3041-0746, Published by AccScience Publishing
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