Evidence-Based Cytopathology: A Call for Implementation

Evidence-based medicine (EBM) emerged in the mid-1990s as a systematic and data-driven approach to clinical practice. It plays a crucial role in cytopathology, which focuses on identifying both neoplastic and non-neoplastic disorders. Despite its importance, cytopathology faces significant challenges in implementing EBM principles. These include diagnostic variability, interobserver discrepancies, and subjective interpretations, which can impact diagnostic consistency and patient outcomes. These issues often lead to inconsistencies in diagnosis, underdiagnosis, or overdiagnosis, ultimately compromising patient outcomes. To address these challenges, evidence-based cytopathology (EBC) emphasises the critical evaluation of diagnostic tests and the utilisation of well-designed and high-quality data to establish robust guidelines. EBC promotes the adoption of standardised diagnostic criteria, integrates robust study designs with the implementation of advanced technologies like artificial intelligence (AI), and leverages clinical decision support systems (CDSS) to enhance diagnostic accuracy and consistency. However, realising the full benefits of EBC requires collaborative research, multicenter trials, and continuous updates to guidelines. In this paper, we discuss the future efforts in improving EBC outcomes, with a focus on developing precise diagnostic instruments, refining methodologies, and exploring machine learning applications to improve diagnostic reliability, enhance patient care, and support informed clinical decision-making.