The paper acknowledges that the current regulatory

The gap

Evidence profile

Sourced from the limitations and recommendations and inline gaps and future-work section and limitations section of the source papers, classified as general, spanning 5 journals. Those papers have been cited 2 times in total.

Supporting evidence — 7 representative gaps

• Artificial Intelligence in Obstetrics and Gynecology Nursing: Clinical, Educational, and Ethical Perspectives (2026) · Cureus · doi

  This narrative review has several limitations. As a narrative synthesis, it is inherently susceptible to selection bias and does not employ formal meta-analytic techniques. Although a structured search strategy was followed, the review was not conducted according to formal systematic review guidelines, and a fully reproducible study selection process may not have been achieved. In addition, no formal quality appraisal of included studies was undertaken, which may affect the strength and reliability of the conclusions drawn. The included studies varied considerably in design, outcome measures, validation settings, and stages of implementation, limiting direct comparison. Furthermore, many AI applications in OBG nursing remain in early developmental or pilot phases, with limited large-scale validation in diverse healthcare systems. Evidence from low- and middle-income countries remains comparatively sparse, which may restrict the generalizability of findings. Continued high- quality, multicenter research, including rigorous validation studies and context-specific implementation research, is required to strengthen the evidence base.

  generallimitationsevidence 5/5
  Keywords: review formal validation narrative selection quality included implementation evidence several limitations synthesis inherently susceptible bias
• Integrating Artificial Intelligence and Point-of-Care Ultrasound Within the Clinical-Scientific Method: A Framework for Safer, Smarter Medicine (2026) · Cureus · doi

  This work is a narrative review and conceptual synthesis rather than a systematic review. As such, it does not employ a predefined search strategy, formal inclusion or exclusion criteria, or risk of bias assessment. Consequently, the selection of literature may not fully capture the breadth of available evidence, particularly in rapidly evolving fields such as AI in healthcare. This limitation should be considered when interpreting the scope and generalizability of the proposed framework. The scope of this manuscript is intentionally focused. It is not intended to provide a technical review of AI, nor to offer detailed discussion of model architectures, prompting strategies, or specialized computational methods. Instead, the objective is to examine the role of AI and POCUS within the clinical method and its parallelism with the scientific method. Finally, the proposed framework has not yet been empirically validated and should be interpreted as a conceptual model intended to guide future research, education, and clinical integration. Given the rapid evolution of AI, some elements of this framework may require refinement as new evidence, technologies, and regulatory standards emerge. Further studies are needed to evaluate its applicability across different healthcare settings and specialties.

  generallimitationsevidence 5/5
  Keywords: review framework conceptual evidence healthcare scope proposed intended model clinical narrative synthesis rather systematic employ
• Risk and liability in the deployment of AI systems for surgery: a SAGES white paper (2026) · Surgical Endoscopy · doi

  To address the aforementioned risks associated with the implementation and utilization of AI systems, we recom- mend creating an AI Governance Blueprint to more clearly communicate and mitigate the potential harm to patients, clinicians, and institutions. how inpatient clinical pharmacists monitor the appropri- ate use of drugs in a hospital, ensuring that the technol- ogy is deployed correctly and its outputs are appropriately integrated into surgical decision-making [38]. While these measures may not solve the issue of liability when there is harm, they are important for decreasing the risk of clinical AI use in the first place.

  generalrecommendationsevidence 5/5
  Keywords: harm clinical address aforementioned risks associated implementation utilization systems recom mend creating governance blueprint clearly
• Clinical decision accuracy in endodontic treatment of patients with systemic diseases: a comparative analysis using different artificial intelligence models (2026) · Odontology · cited 1× · doi

  Several limitations of this study should be acknowl- edged. Another limitation of this study is the equal weighting of all scoring domains within the composite clinical deci- sion accuracy score, which sums multiple decision-making domains, including systemic risk assessment, treatment planning, medication-related recommendations, and the indication for medical consultation. Finally, the evaluation was limited to a small number of AI models, and rapid model updates or architectural changes may affect performance in future applications.

  generalinline gapsevidence 5/5
  Keywords: domains several limitations acknowl edged limitation equal weighting scoring within composite clinical deci sion accuracy
• Evidence-like error and physician responsibility in retrieval-augmented clinical artificial intelligence (2026) · AboutOpen · doi

  The paper suggests that future research should focus on the development of regulatory frameworks that prioritize clinical validity, epistemic transparency, and compatibility with physicians’ professional duties. The paper argues that future research should also focus on the development of AI systems that are transparent, explainable, and fair. The paper suggests that future research should investigate the impact of AI on clinical decision-making and patient outcomes.

  generalfuture-work sectionevidence 5/5
  Keywords: paper suggests future research focus development regulatory frameworks
• Evidence-like error and physician responsibility in retrieval-augmented clinical artificial intelligence (2026) · AboutOpen · doi

  The paper acknowledges that the current regulatory framework is insufficient to address the concerns raised by the integration of AI in clinical practice. The paper also acknowledges that the analysis of AI-generated errors is limited by the availability of data and the complexity of the systems. The paper notes that the paper’s findings are based on a theoretical analysis and may not be generalizable to all clinical settings.

  generallimitations sectionevidence 5/5
  Keywords: paper acknowledges current regulatory framework insufficient address concerns
• Artificial intelligence in acute and critical care: current challenges and strategic solutions (2026) · Frontiers in Public Health · cited 1× · doi

  The narrative design of the review and the heterogeneity of included evidence types limited the ability to conduct a formal quality assessment - The review highlights the need for further research to address the challenges and fully realize the potential of AI in acute and critical care - The paper notes that many AI models in acute and critical care have shown good predictive performance, but prediction alone does not establish clinical utility

  generallimitations sectionevidence 5/5
  Keywords: narrative design review heterogeneity included evidence types limited

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