Artificial intelligence for early diagnosis of lung cancer through incidental nodule detection in low- and middle-income countries-acceleration during the COVID-19 pandemic but here to stay.

Although the coronavirus disease of 2019 (COVID-19) pandemic had profound pernicious effects, it revealed deficiencies in health systems, particularly among low- and middle-income countries (LMICs). With increasing uncertainty in healthcare, existing unmet needs such as poor outcomes of lung cancer (LC) patients in LMICs, mainly due to late stages at diagnosis, have been challenging-necessitating a shift in focus for judicious health resource utilization. Leveraging artificial intelligence (AI) for screening large volumes of pulmonary images performed for noncancerous reasons, such as health checks, immigration, tuberculosis screening, or other lung conditions, including but not limited to COVID-19, can facilitate easy and early identification of incidental pulmonary nodules (IPNs), which otherwise could have been missed. AI can review every chest X-ray or computed tomography scan through a trained pair of eyes, thus strengthening the infrastructure and enhancing capabilities of manpower for interpreting images in LMICs for streamlining accurate and early identification of IPNs. AI can be a catalyst for driving LC screening with enhanced efficiency, particularly in primary care settings, for timely referral and adequate management of coincidental IPN. AI can facilitate shift in the stage of LC diagnosis for improving survival, thus fostering optimal health-resource utilization and sustainable healthcare systems resilient to crisis. This article highlights the challenges for organized LC screening in LMICs and describes unique opportunities for leveraging AI. We present pilot initiatives from Asia, Latin America, and Russia illustrating AI-supported IPN identification from routine imaging to facilitate early diagnosis of LC at a potentially curable stage. AJCR