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 po

The gap

Consensus across the literature

Supporting evidence — 3 representative gaps

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

  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) · 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.

  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) · 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.

  Keywords: harm clinical address aforementioned risks associated implementation utilization systems recom mend creating governance blueprint clearly