ReviewKidney3602024
Multimodal Artificial Intelligence in Medicine.
Review in Kidney360, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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
10 citing papers in PubMed.
- From patient notes to prognostication: The revolutionary potential of event-based foundation models in orthopaedics.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2026Article
- A Multimodal Approach for Deep-Learning Classification of Vocal Fold Pathologies in Stroboscopy.The Laryngoscope · 2026Article
- A Language Model for Pediatric Occupational Therapy Documentation: Model Development and Pilot Study.JMIR AI · 2026Article
- Artificial Intelligence in Voice Disorders: Current Landscape, Emerging Applications and Future Directions.World journal of otorhinolaryngology - head and neck surgery · 2026Review
- From static snapshots to longitudinal trajectories: artificial intelligence in women's reproductive and ovarian health.Frontiers in endocrinology · 2026Review
- Clinical applications of artificial intelligence in the histopathology of lymphoma: diagnosis, treatment and prognosis.Discover oncology · 2025Review
- Multimodal and Multidimensional Artificial Intelligence Technology in Obesity.Journal of obesity & metabolic syndrome · 2025Review
- Scoping review of deep learning research illuminates artificial intelligence chasm in otolaryngology-head and neck surgery.NPJ digital medicine · 2025Article
- AI agents in Alzheimer's disease management: challenges and future directions.Frontiers in aging neuroscience · 2025Article
- Assessments of Generative Artificial Intelligence as Clinical Decision Support Ought to be Incorporated Into Randomized Controlled Trials of Electronic Alerts for Acute Kidney Injury.Mayo Clinic proceedings. Digital health · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Traditional medical artificial intelligence models that are approved for clinical use restrict themselves to single-modal data ( e.g ., images only), limiting their applicability in the complex, multimodal environment of medical diagnosis and treatment. Multimodal transformer models in health care can effectively process and interpret diverse data forms, such as text, images, and structured data. They have demonstrated impressive performance on standard benchmarks, like United States Medical Licensing Examination question banks, and continue to improve with scale. However, the adoption of these advanced artificial intelligence models is not without challenges. While multimodal deep learning models like transformers offer promising advancements in health care, their integration requires careful consideration of the accompanying ethical and environmental challenges.
Indexed as
Identifiers
What OpenQuestion holds
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.