{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T16:57:46Z","timestamp":1782233866077,"version":"3.54.5"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI-SS"],"abstract":"<jats:p>Real-world medical AI systems rarely operate under ideal conditions: multimodal clinical data are frequently incomplete due to resource constraints, equipment limitations, and workflow disruptions\u2014challenges amplified in under-resourced hospitals and community clinics where infrastructure gaps make missing modalities the norm. Existing multimodal learning methods typically assume data completeness or treat missing modalities as a narrow technical problem, limiting their reliability in practice.\nWe posit that missing-modality learning should be reframed as a core challenge of AI resilience in medicine, where systems must maintain reliable performance under real-world constraints while transparently communicating uncertainty. To this end, we propose a two-tier framework aligned with clinical workflows. Tier 1 enables efficient, uncertainty-aware inference under missing modalities using prompt-enhanced modality encoding and learnable pseudo-embeddings. Cases with elevated uncertainty are routed to Tier 2, which performs high-fidelity generative recovery using conditional diffusion models for precision-critical decisions.\nComponent-level validation on a multi-center colorectal cancer cohort (1,679 patients, four centers) demonstrates that each design element contributes measurably to downstream prognostic performance: learnable pseudo-embeddings improve the concordance index (C-index) by 10.1 points over zero-filling in feature space, while missing-aware prompts and modality-aware prompts contribute 4.4 and 3.7 points, respectively. Full evaluation of uncertainty-guided routing and generative recovery across brain tumor, cardiac, and CT\u2013MRI benchmarks is ongoing.<\/jats:p>","DOI":"10.1609\/aaaiss.v9i1.42917","type":"journal-article","created":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T16:34:12Z","timestamp":1782232452000},"page":"148-151","source":"Crossref","is-referenced-by-count":0,"title":["Toward Resilient Medical Multimodal AI: A Framework for Missing Modality Recovery Under Clinical Constraints"],"prefix":"10.1609","volume":"9","author":[{"given":"Jiahe","family":"Hou","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John","family":"Moraros","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangliang","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuihua","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"9382","published-online":{"date-parts":[[2026,6,23]]},"container-title":["Proceedings of the AAAI Symposium Series"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI-SS\/article\/download\/42917\/50477","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI-SS\/article\/download\/42917\/50477","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T16:34:13Z","timestamp":1782232453000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI-SS\/article\/view\/42917"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,23]]},"references-count":0,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,6,23]]}},"URL":"https:\/\/doi.org\/10.1609\/aaaiss.v9i1.42917","relation":{},"ISSN":["2994-4317"],"issn-type":[{"value":"2994-4317","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,23]]}}}