Validate the AI model in larger patient populations

The gap

Evidence profile

Sourced from the inline gaps and future-work section of the source papers, classified as general, spanning 3 journals.

Supporting evidence — 3 representative gaps

• A Systematic Review of Machine Learning, Deep Learning, and Explainable AI Approaches for Cardiac Disease Prediction (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi

  Lastly, there is a lack of attention to issues associated with data privacy, ethical considerations, and fairness, and rather limited literature suggests privacy-conserving or fairness- aware models that are also highly performing and explainable. Although machine learning (ML), deep learning (DL), and hybrid artificial intelligence methods have advanced a lot in predicting heart diseases, the comparative study of the existing literature indicates that there are still multiple gaps in the research, which restrict their clinical implementation in the real world. The literature review has identified major gaps in research that still restrict the clinical implementation of an article in machine learning-based cardiac disease prediction systems.

  generalinline gapsevidence 5/5
  Keywords: literature learning there privacy fairness machine still gaps restrict clinical implementation lastly lack attention issues
• Artificial intelligence–based detection of acute postoperative airway complications following anterior cervical spine surgery: a retrospective imaging evaluation (2026) · Asian Spine Journal · doi

  Further studies are needed to validate the AI model in larger patient populations. The use of other machine learning algorithms, such as deep learning, may improve the predictive performance of the model. The integration of the AI model with clinical decision support systems can enhance its practical applications.

  generalfuture-work sectionevidence 5/5
  Keywords: further studies needed validate model larger patient populations
• Multi-Model Machine Learning for Survival Predictions for Castration-Resistant Prostate Cancer (2026) · Cancers · doi

  Further studies are needed to validate the findings and explore the generalizability of the machine learning models to other populations. The use of other machine learning approaches and techniques, such as deep learning, should be explored. The integration of machine learning models with clinical decision support systems should be investigated.

  generalfuture-work sectionevidence 4/5
  Keywords: further studies needed validate findings explore generalizability machine

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