ArticleFrontiers in medicine2023
Real-world post-deployment performance of a novel machine learning-based digital health technology for skin lesion assessment and suggestions for post-market surveillance.
Article in Frontiers in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
21 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Prospective Evidence on Artificial Intelligence-Assisted Melanoma Diagnostics: A Systematic Review and Meta-Analysis.JAMA dermatology · 2026Pooled it
- Artificial Intelligence technologies for assessing skin lesions for referral on the urgent suspected cancer pathway to detect benign lesions and reduce secondary care specialist appointments: early value assessment.Health technology assessment (Winchester, England) · 2026Pooled it
- The Evolving Role of Artificial Intelligence in Dermatology: A Meta-Analysis of Diagnostic Performance, Clinical Applications, and Implementation Challenges (2003-2025).Diagnostics (Basel, Switzerland) · 2026Review
- Real-world diagnostic performance of artificial intelligence in skin cancer screening.Skin health and disease · 2026Article
- Clinical AI Beyond Development: A Scoping Review of Deployment-Related Robustness, Algorithmovigilance, and Lifecycle Oversight.Healthcare (Basel, Switzerland) · 2026Review
- Digital diagnostics, biomarkers and therapeutics in an evolving healthcare system: From promise to practice.British journal of clinical pharmacology · 2026Review
- Digital health technologies in medicine: evidence, artificial intelligence integration, and ethical challenges.Infectious agents and cancer · 2026Review
- Advancing skin cancer detection through deep learning and fusion of patient metadata and skin lesion images.Scientific reports · 2026Article
- Screening and novel diagnostic technologies for melanoma: an update.Melanoma management · 2025Review
- Mitigated deployment strategy for ethical AI in clinical settings.BMJ health & care informatics · 2025Article
- The illusion of safety: A report to the FDA on AI healthcare product approvals.PLOS digital health · 2025Article
- Exploring the Views of Dermatologists, General Practitioners, and Melanographers on the Use of AI Tools in the Context of Good Decision-Making When Detecting Melanoma: Qualitative Interview Study.JMIR dermatology · 2025Article
- Bias recognition and mitigation strategies in artificial intelligence healthcare applications.NPJ digital medicine · 2025Review
- Article
- Who moved my scan? Early adopter experiences with pre- and post-market healthcare AI regulation challenges.Frontiers in medicine · 2025Article
- Assessing the performance of AI-assisted technicians in liver segmentation, Couinaud division, and lesion detection: a pilot study.Abdominal radiology (New York) · 2024Article
- Leveraging AI and patient metadata to develop a novel risk score for skin cancer detection.Scientific reports · 2024Article
- Review
- Accuracy of an artificial intelligence as a medical device as part of a UK-based skin cancer teledermatology service.Frontiers in medicine · 2024Article
- Patient perspectives of artificial intelligence as a medical device in a skin cancer pathway.Frontiers in medicine · 2023Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Deep Ensemble for Recognition of Malignancy (DERM) is an artificial intelligence as a medical device (AIaMD) tool for skin lesion assessment. Methods: We report prospective real-world performance from its deployment within skin cancer pathways at two National Health Service hospitals (UK) between July 2021 and October 2022. Results: A total of 14,500 cases were seen, including patients 18-100 years old with Fitzpatrick skin types I-VI represented. Based on 8,571 lesions assessed by DERM with confirmed outcomes, versions A and B demonstrated very high sensitivity for detecting melanoma (95.0-100.0%) or malignancy (96.0-100.0%). Benign lesion specificity was 40.7-49.4% (DERM-vA) and 70.1-73.4% (DERM-vB). DERM identified 15.0-31.0% of cases as eligible for discharge. Discussion: We show DERM performance in-line with sensitivity targets and pre-marketing authorisation research, and it reduced the caseload for hospital specialists in two pathways. Based on our experience we offer suggestions on key elements of post-market surveillance for AIaMDs.
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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.