Dataset bias and generalizability are major challenges

The gap

Evidence profile

Sourced from the limitations section and stated research gap of the source papers, classified as general, spanning 4 journals. Those papers have been cited 3 times in total.

Supporting evidence — 4 representative gaps

• 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 section
  Keywords: methodological heterogeneity dataset variability study design influence reliability
• 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 5/5
  Keywords: 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 5/5
  Keywords: clinicians confidence deep-learning-based models limited due inability generalize
• Clinical applications of machine learning for infection assessment in diabetic foot ulcers (2026) · Frontiers in Physiology · doi

  Dataset bias and generalizability are major challenges, with many existing models trained on relatively narrow image repositories. Limited clinical validation is a significant limitation, with many models not being tested in real-world clinical settings. Image variability is another challenge, with differences in camera equipment, wound care standards, and clinical practice affecting model performance.

  generallimitations sectionevidence 5/5
  Keywords: dataset bias generalizability major challenges many existing models

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