{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T15:00:21Z","timestamp":1785337221985,"version":"3.55.0"},"reference-count":22,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T00:00:00Z","timestamp":1757462400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:p>The Exposome\u2014the totality of environmental exposures across a lifetime\u2014remains one of the most significant challenges in understanding and preventing human disease. Translating its vast, heterogeneous data streams into actionable knowledge requires artificial intelligence (AI) integrated with human-relevant experimental systems. We propose a unifying vision in which Microphysiological Systems (MPS) and multi-omics platforms generate high-quality, context-specific data that iteratively calibrate AI models, enabling the creation of digital twins of organs, individuals, and ultimately populations. This \u201cExposome Moonshot\u201d parallels the Apollo program in ambition, with MPS as the rocket, multi-omics as the lunar module, and AI as the guidance computer. Early applications demonstrate that deep learning can already outperform canonical animal tests for several toxicological endpoints, while reducing cost and time to decision. Realizing the full potential of Exposome intelligence will require expanding the applicability domain of models, implementing robust data security, and prioritizing transparent, interpretable algorithms. By linking predictive AI with experimental feedback, we can move toward a prevention-driven, personalized paradigm for human health and regulatory science.<\/jats:p>","DOI":"10.3389\/frai.2025.1632520","type":"journal-article","created":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T05:26:38Z","timestamp":1757481998000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["AI: the Apollo guidance computer of the Exposome moonshot"],"prefix":"10.3389","volume":"8","author":[{"given":"Fenna C. M.","family":"Sill\u00e9","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"Hartung","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,9,10]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1038\/s41746-024-01402-3","article-title":"Challenges and opportunities for digital twins in precision medicine from a complex systems perspective","volume":"8","author":"De Domenico","year":"2025","journal-title":"NPJ Digit. Med."},{"key":"ref2","doi-asserted-by":"publisher","first-page":"3035","DOI":"10.1021\/acs.jcim.5c00032","article-title":"Bidirectional long short-term memory (BiLSTM) neural networks with conjoint fingerprints: application in predicting skin-sensitizing agents in natural compounds","volume":"65","author":"Duy","year":"2025","journal-title":"J. Chem. Inf. 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