HARNESSING ARTIFICIAL INTELLIGENCE FOR EARLY DETECTION AND DIAGNOSIS OF CANCER: INTEGRATING IMAGING, GENOMICS, AND CLINICAL DATA FOR PRECISION ONCOLOGY

Abstract

AI integration in oncology revolutionized early cancer detection using imaging, genomics, and clinical data. Diagnostics are bettered by AI models and thereby reduce human error while also promoting good patient outcomes by way of precision medicine. Machine learning algorithms pick up subtle indications of malignancy in radiology and histopathology images, whereas deep learning algorithms analyze genomic data for early diagnosis-related biomarkers. Combining AI and clinical data empowers predictive analytics and personalized treatment approaches. However, challenges such as data standardization, ethical issues, and interpretability of the models still loom. This review takes an overarching look at how AI is working to promote early cancer detection while focusing on recent advances, challenges, and future research propositions regarding precision oncology.