ReviewNPJ digital medicine2024
Orchestrating explainable artificial intelligence for multimodal and longitudinal data in medical imaging.
Review in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis 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
29 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Explainable artificial intelligence (XAI) in medical imaging: a systematic review of techniques, applications, and challenges.BMC medical imaging · 2026Pooled it
- Artificial intelligence for postoperative plain radiographic assessment after reverse total shoulder arthroplasty: current evidence, clinical readiness, and limitations.Clinics in shoulder and elbow · 2026Article
- Explainable artificial intelligence reveals key surgical parameters in robot-assisted and open radical prostatectomy.Scientific reports · 2026Article
- NEXIM: A Nash Equilibrium-Based Framework for Stable Explainable AI in Medical Applications.medRxiv : the preprint server for health sciences · 2026Article
- Deep learning prediction of nocturnal hypertension for patients intolerant to ambulatory blood pressure monitoring.Communications medicine · 2026Article
- A novel multimodal AI-based radiomics approach for precision assessment of pain intensity in chronic nonspecific low back pain.Journal of orthopaedic translation · 2026Article
- Bridging modalities with AI: a review of AI advances in multimodal biomedical imaging.Communications engineering · 2026Review
- Foundation model embeddings for multimodal oncology data integration.NPJ digital medicine · 2026Article
- Uric acid-associated mechanisms of coronary artery calcification in diabetic kidney disease: evidence, hypotheses, and translational perspectives.Frontiers in cardiovascular medicine · 2026Review
- Myocarditis in the Modern Era: Navigating Diagnosis, Innovative Treatments, the COVID-19 Challenge and the Role of Artificial Intelligence.Cardiology research and practice · 2026Review
- Investigating discrepancies in accuracy, agreement and interpretability for single-frame embryo classification tasks conducted by embryologists and deep learning models.Frontiers in reproductive health · 2026Article
- Anatomy at the threshold: Teaching the human body in a hybrid age.Anatomical sciences education · 2025Article
- HONeYBEE: enabling scalable multimodal AI in oncology through foundation model-driven embeddings.NPJ digital medicine · 2025Article
- Clinician perspectives on explainability in AI-driven closed-loop neurotechnology.Scientific reports · 2025Article
- A Multi-Dimensional Framework for Data Quality Assurance in Cancer Imaging Repositories.Cancers · 2025Article
- The STARD-AI reporting guideline for diagnostic accuracy studies using artificial intelligence.Nature medicine · 2025Review
- Trends in Multimodal Artificial Intelligence for Autism Research in Children and Adolescents: A Scientometric Study.Journal of the Korean Academy of Child and Adolescent Psychiatry · 2025Review
- Explainability in the age of large language models for healthcare.Communications engineering · 2025Article
- Comparative analysis of the performance of the large language models DeepSeek-V3, DeepSeek-R1, open AI-O3 mini and open AI-O3 mini high in urology.World journal of urology · 2025Article
- European advances in digital rheumatology: explainable insights and personalized digital health tools for psoriatic arthritis.EClinicalMedicine · 2025Review
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
Explainable artificial intelligence (XAI) has experienced a vast increase in recognition over the last few years. While the technical developments are manifold, less focus has been placed on the clinical applicability and usability of systems. Moreover, not much attention has been given to XAI systems that can handle multimodal and longitudinal data, which we postulate are important features in many clinical workflows. In this study, we review, from a clinical perspective, the current state of XAI for multimodal and longitudinal datasets and highlight the challenges thereof. Additionally, we propose the XAI orchestrator, an instance that aims to help clinicians with the synopsis of multimodal and longitudinal data, the resulting AI predictions, and the corresponding explainability output. We propose several desirable properties of the XAI orchestrator, such as being adaptive, hierarchical, interactive, and uncertainty-aware.
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.