Transforming early cancer detection in primary care: harnessing the power of machine learning.

Abstract

Cancer remains a significant global health burden, and early detection plays a crucial role in improving patient outcomes. Primary care settings serve as frontline gatekeepers, providing an opportunity for early detection through symptom assessment and targeted screening. However, detecting early-stage cancer and identifying individuals at high risk can be challenging due to the complexity and subtlety of symptoms [1]. The challenging nature of early detection is revealed by diagnostic errors in primary care, with cancer being one of the most frequently missed or delayed diagnoses [2]. In recent years, the emergence of machine learning (ML) techniques has shown promise in revolutionizing early detection efforts [3]. This editorial explores the potential of ML in enhancing early cancer detection in primary care.