ArticleNPJ digital medicine2022
Integrated multimodal artificial intelligence framework for healthcare applications.
Article in NPJ digital medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 130 papers, 4 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
130 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- "First, do no harm" in the digital era: examining the practicality of the European Health Data Space proposal and ethical implications of artificial intelligence: A systematic literature review.BMC medical ethics · 2026Pooled it
- Current Applications of Artificial Intelligence for Fuchs Endothelial Corneal Dystrophy: A Systematic Review.Translational vision science & technology · 2025Pooled it
- Prevention and management of degenerative lumbar spine disorders through artificial intelligence-based decision support systems: a systematic review.BMC musculoskeletal disorders · 2025Pooled it
- Exploring trends and autonomy levels of adaptive business intelligence in healthcare: A systematic review.PloS one · 2024Pooled it
- Integrating mathematical modelling and artificial intelligence to combat emerging viral syndemics: A systematic review.New microbes and new infections · 2026Review
- Advancing Patient-Centred AI With Adaptive Machine Learning.Cancer medicine · 2026Review
- Artificial intelligence in otolaryngology: current applications, limitations, and future perspectives.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Review
- Artificial intelligence in hepatocellular carcinoma diagnosis and AI-assisted treatment support: Current landscape and future directions.Liver research (Beijing, China) · 2026Review
- Large language models in spine care and research.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Review
- Human-AI Interaction With AI-Assisted Tumor Overlays in Pediatric Whole-Body Magnetic Resonance Imaging: Exploratory Reader Study.JMIR human factors · 2026Article
- Benchmarking Open-Source Pathology Foundation Models for Breast Cancer Biomarker Prediction from H&E Whole-Slide Images.Cancers · 2026Article
- Clinical Applications of Multimodal Artificial Intelligence in Otolaryngology: A State-of-the-Art Review.Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery · 2026Review
- Generative AI, foundation models and large language models in radiation therapy physics: Clinical applications, challenges, and future directions.Medical physics · 2026Review
- Ethical and Regulatory Perception of Artificial Intelligence Among Dental Students: A Cross-Sectional Study.European journal of dental education : official journal of the Association for Dental Education in Europe · 2026Article
- Correlation-Induced Accessibility Bridges in Biomedical Networks: A Proof-of-Concept Relational Graph Model.Entropy (Basel, Switzerland) · 2026Article
- Generative AI in healthcare: redefining clinical practice through digital transformation.Health economics review · 2026Article
- Handling missing modalities in multimodal survival prediction for non-small cell lung cancer.NPJ digital medicine · 2026Article
- Article
- Fuzzy min-max dual motif-guided heterogeneous Dandelion graph multi-scale residual attention network for healthcare AI misclassification reduction.Scientific reports · 2026Article
- From Integrated Care to Learning Systems.Healthcare (Basel, Switzerland) · 2026Review
70 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) systems hold great promise to improve healthcare over the next decades. Specifically, AI systems leveraging multiple data sources and input modalities are poised to become a viable method to deliver more accurate results and deployable pipelines across a wide range of applications. In this work, we propose and evaluate a unified Holistic AI in Medicine (HAIM) framework to facilitate the generation and testing of AI systems that leverage multimodal inputs. Our approach uses generalizable data pre-processing and machine learning modeling stages that can be readily adapted for research and deployment in healthcare environments. We evaluate our HAIM framework by training and characterizing 14,324 independent models based on HAIM-MIMIC-MM, a multimodal clinical database (N = 34,537 samples) containing 7279 unique hospitalizations and 6485 patients, spanning all possible input combinations of 4 data modalities (i.e., tabular, time-series, text, and images), 11 unique data sources and 12 predictive tasks. We show that this framework can consistently and robustly produce models that outperform similar single-source approaches across various healthcare demonstrations (by 6-33%), including 10 distinct chest pathology diagnoses, along with length-of-stay and 48 h mortality predictions. We also quantify the contribution of each modality and data source using Shapley values, which demonstrates the heterogeneity in data modality importance and the necessity of multimodal inputs across different healthcare-relevant tasks. The generalizable properties and flexibility of our Holistic AI in Medicine (HAIM) framework could offer a promising pathway for future multimodal predictive systems in clinical and operational healthcare settings.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.