ReviewEClinicalMedicine2025
Clinicians must participate in the development of multimodal AI.
Review in EClinicalMedicine, 2025. 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.
- Automated Extraction of Postoperative Cancer Recurrence and Metastasis From Computed Tomography (CT) Reports: Semisupervised Deep Learning Study.JMIR medical informatics · 2026Article
- Artificial intelligence in oncology: empowering clinicians for responsible integration.ESMO real world data and digital oncology · 2026Review
- Beyond Implementation: Neuroanesthesiologists as Artificial Intelligence CoCreators.Journal of neurosurgical anesthesiology · 2026Article
- Vision-Enabled AI scribes reduce omissions in clinical conversations: evidence from simulated medication histories.NPJ digital medicine · 2026Article
- Generative Artificial Intelligence in Healthcare: Automation Bias, Deskilling, and Cognitive Implications - A Systematic Review.Journal of healthcare leadership · 2026Review
- A Fully Expert Human-Based Retrieval Augmented Generation (FEH-RAG) Framework: A Proof of Concept Study in Labelling Patients with Sjögren Syndrome.Medical journal of the Islamic Republic of Iran · 2026Article
- Multimodal artificial intelligence in urologic precision oncology: from algorithm to translational medicine (a systemized narrative review).Frontiers in oncology · 2026Review
- SpatialFinder: a human-in-the-loop vision-language framework for prioritizing high-value regions in spatial transcriptomics.Frontiers in bioinformatics · 2026Article
- Article
- The Rise of Generalist Foundation Models and Quantum Computing in Oncology.Technology in cancer research & treatmentReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Multimodal artificial intelligence (AI) is a powerful new technological advance, capable of simultaneously learning from diverse data types, such as text, images, video, and audio. Because clinical decisions are usually based on information from multiple sources, multimodal AI has the potential to significantly improve clinical practice. However, unlike most developed multimodal AI workflows, clinical medicine is both a dynamic and interventional process in which the clinician continually learns about the patient's health and acts accordingly as data is collected. In this article we argue that multimodal clinical AI must be fully attuned to the particular challenges and constraints of the clinic, and clinician involvement is needed throughout development-not just at clinical deployment. We propose ways that clinician involvement can add value at each stage of the multimodal AI development pipeline, and argue for the establishment of actively managed multidisciplinary communities to work collaboratively towards the shared goal of improving the health of all.
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.