Clinicians' confidence in deep-learning-based models
Research gap analysis derived from 3 medicine papers in our local library.
The gap
Clinicians' confidence in deep-learning-based models is limited due to their inability to generalize across multiple scanners, protocols, and medical systems. The paper notes that a critical meta-analysis of the reviewed literature reveals
Evidence profile
Sourced from the stated research gap and limitations section of the source papers, classified as general, spanning 3 journals. Those papers have been cited 3 times in total.
Research trend
Established — well-defined area with open sub-problems.
Supporting evidence — 3 representative gaps
- Artificial intelligence in neurovascular surgery: advancing diagnosis, treatment, and outcomes (2026) · Frontiers in Surgery · doi
The complexity of neurovascular pathologies and the variability in clinical presentation hinder timely and accurate diagnosis, precise risk stratification, and effective intervention. The limited generalizability of AI systems across heterogeneous clinical populations is a critical barrier to adoption. The reliance on imaging data alone is a key limitation, as comprehensive risk prediction requires integration with electronic health records.
generalstated research gapevidence 5/5Keywords: complexity neurovascular pathologies variability clinical presentation hinder timely - Intelligent Fusion of Multi-Modal Medical Imaging: A Comprehensive Review of Methods, Challenges, and Clinical Integration (2026) · Journal of Electronics, Electromedical Engineering, and Medical Informatics · doi
Clinicians' confidence in deep-learning-based models is limited due to their inability to generalize across multiple scanners, protocols, and medical systems. The paper notes that a critical meta-analysis of the reviewed literature reveals substantial heterogeneity in reported performance, with conflicting results frequently arising from dataset composition, evaluation metric selection, and deployment context.
generallimitations sectionevidence 5/5Keywords: clinicians confidence deep-learning-based models limited due inability generalize - Artificial Intelligence in Implant Dentistry: Clinical Validity, Diagnostic Performance, Surgical Planning, and Medico-Legal Implications—A Narrative Review (2026) · Dentistry Journal · cited 3× · doi
Methodological heterogeneity, - Dataset variability and study design influence the reliability of predictive models, - Technical accuracy achieved under controlled experimental conditions does not necessarily translate into real-world clinical effectiveness, - The transition from technical performance to clinical utility remains less straightforward, - Variability in image acquisition, anatomical presentation, and clinical context may influence the reliability of AI-supported outputs
generallimitations sectionevidence 5/5Keywords: methodological heterogeneity dataset variability study design influence reliability
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