The Role of Artificial Intelligence in Early Cancer Detection: Exploring Early Clinical Applications

Abstract

Introduction: Early cancer detection can lead to improved outcomes and a shift toward a prevention model. This review explores the role of artificial intelligence (AI) in early cancer detection, focusing on its application in various clinical settings. Methods: A literature search was conducted using databases such as PubMed, Scopus, Web of Science, and IEEE Xplore, focusing on studies that applied AI in early cancer detection. The search, covering articles up to November 2023, included studies discussing clinical applications and trials. The findings were categorizing studies based on AI techniques, cancer types, and clinical application stages. Results: AI in oncology, particularly machine learning (ML) and deep learning, has shown promise in enhancing early cancer detection, including breast, lung, prostate, skin, gastrointestinal, and other cancers. These technologies have been applied to various data types, including electronic health records, medical imaging, and biomarkers such as genomics and proteomics. AI/ML has numerous indications throughout the continuum of oncology care, including screening, detection, classification, characterization, segmentation, and monitoring. AI has contributed to numerous early clinical applications in major cancer types such as breast, lung, skin, and prostate cancer, including early symptom detection, clinical decision support, radiology, pathology, medical image and video analysis, biomarker testing including liquid biopsies, detection of recurrence, remote monitoring, and risk stratification. Conclusion: AI may advance early cancer detection, and holds promise of revolutionizing health care and diagnosis processes. However, data privacy, ethical considerations, and potential biases require addressing. Implementing quality assurance frameworks and standardization initiatives is vital for the future quality and adoption of AI models in oncology. Future AI applications will include integrating multimodal models, interpretable models, and digital twin technologies, ensuring model transparency and generalizability.