ReviewJournal of the American College of Radiology : JACR2024
Establishing a Validation Infrastructure for Imaging-Based Artificial Intelligence Algorithms Before Clinical Implementation.
Review in Journal of the American College of Radiology : JACR, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 citing papers in PubMed.
- The current state of demographic subgroup reporting for commercially available AI for radiology: a scoping review.European radiology · 2026Article
- The ethical and regulatory framework of medical artificial intelligence (AI) system's behavioral performance for image-based diagnostics: An ethical evaluation of the International and European Union Scope.PLOS digital health · 2026Article
- Artificial Intelligence in Point-of-Care Ultrasound: Domains, Barriers and a Framework for Future Development.Journal of the American College of Emergency Physicians open · 2026Article
- Cutaneous Thermography in Arthropathies: Quantitative Imaging, Machine Learning, and Clinical Translation.Journal of imaging · 2026Review
- Clinical Potential of Artificial Bone Scintigraphy from Early-Phase Bone Scintigraphy Using Unpaired Image-to-Image Translation in Patients with Breast Cancer: A Single-Center Prospective Study.Tomography (Ann Arbor, Mich.) · 2026Article
- AI-driven healthcare: a trend toward better healthcare or the emergence of public health burden.Frontiers in digital health · 2026Article
- Feasibility of deep learning-based image reconstruction using TrueFidelity for coronary calcium scoring.PloS one · 2026Article
- Precision oncology in gynecologic cancers: molecular taxonomy, biomarker-guided therapeutics, and the challenge of therapeutic resistance.Frontiers in oncology · 2026Review
- The expanding role of artificial intelligence in personalised medicine: from innovation to individualized care.Frontiers in medicine · 2026Review
- Article
- Top three priorities for artificial intelligence integration into emergency, critical, and perioperative medicine: an interdisciplinary clinical expert consensus.Journal of anesthesia, analgesia and critical care · 2025Review
- From Echocardiography to CT/MRI: Lessons for AI Implementation in Cardiovascular Imaging in LMICs-A Systematic Review and Narrative Synthesis.Bioengineering (Basel, Switzerland) · 2025Review
- Clinical obstacles to machine-learning POCUS adoption and system-wide AI implementation (The COMPASS-AI survey).The ultrasound journal · 2025Article
- Multi-Modal Graph Neural Networks for Colposcopy Data Classification and Visualization.Cancers · 2025Article
- Evolution of an Artificial Intelligence-Powered Application for Mammography.Diagnostics (Basel, Switzerland) · 2025Article
- ClinValAI: A framework for developing Cloud-based infrastructures for the External Clinical Validation of AI in Medical Imaging.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2025Article
- Global Research Landscape of Artificial Intelligence in Urology: A Systematic Analysis of Emerging Trends, Clinical Impact, and Collaborative Networks (1971-2024).Medical journal of the Islamic Republic of Iran · 2025Article
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
Abstract
With promising artificial intelligence (AI) algorithms receiving FDA clearance, the potential impact of these models on clinical outcomes must be evaluated locally before their integration into routine workflows. Robust validation infrastructures are pivotal to inspecting the accuracy and generalizability of these deep learning algorithms to ensure both patient safety and health equity. Protected health information concerns, intellectual property rights, and diverse requirements of models impede the development of rigorous external validation infrastructures. The authors propose various suggestions for addressing the challenges associated with the development of efficient, customizable, and cost-effective infrastructures for the external validation of AI models at large medical centers and institutions. The authors present comprehensive steps to establish an AI inferencing infrastructure outside clinical systems to examine the local performance of AI algorithms before health practice or systemwide implementation and promote an evidence-based approach for adopting AI models that can enhance radiology workflows and improve patient outcomes.
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.