Artificial intelligence and automation of systematic reviews in women's health
Carmen Amezcua‐Prieto, Juan M. Fernández‐Luna, Juan F. Huete, Aurora Bueno‐Cavanillas, Khalid S. Khan
Abstract
PURPOSE OF REVIEW: Evidence-based women's healthcare is underpinned by systematic reviews and guidelines. Generating an evidence synthesis to support guidance for clinical practice is a time-consuming and labour-intensive activity that delays transfer of research into practice. Artificial intelligence has the potential to rapidly collate, combine, and update high-quality medical evidence with accuracy and precision, and without bias. RECENT FINDINGS: This article describes the main fields of artificial intelligence with examples of its application to systematic reviews. These include the capabilities of processing natural language texts, retrieving information, reasoning, and learning. The complementarity and interconnection of the various artificial intelligence techniques can be harnessed to solve difficult problems in automation of reviews. Computer science can advance evidence-based medicine through development, testing, and refinement of artificial intelligence tools to deploy automation, creating 'living' evidence syntheses. SUMMARY: Groundbreaking, high-quality, and impactful artificial intelligence will accelerate the transfer of individual research studies seamlessly into evidence syntheses for contemporaneously improving the quality of healthcare.