Revolutionizing colorectal cancer screening with artificial intelligence

Abstract

Colorectal cancer (CRC) is the third most commonly diagnosed malignancy in men and women and a leading cause of cancer-related deaths globally1. Leveraging cutting-edge screening technologies, particularly artificial intelligence (AI), can revolutionize the fight against CRC by emphasizing the significance of early-stage detection2. AI has accompanied remarkable advancements in health care, particularly in colorectal screening. Through this letter, we aim to highlight the outstanding potential of AI technology in CRC screening and diagnosis, providing you with a comprehensive understanding and analysis of its applications3. Traditional colorectal screening techniques like colonoscopy and fecal occult blood testing have been effective but still have some drawbacks, such as their invasiveness, high expense, and discomfort for patients limit their efficacy. As no screening procedure for CRC can be categorically deemed the best, it is impossible to determine which method is unquestionably superior. The most effective plan of action is the one that can be implemented affordably and that the patient can best stick with over time4. So the development of AI technologies in this field is changing the setting of colorectal screening in several new and exciting ways5. Improved accuracy and efficiency AI-assisted techniques in routine screening represent a pivotal step in declining incidence rates of this malignancy. Computer-aided detection and characterization systems have been developed to increase the rates of missed adenomas and the risk of developing cancer by improving CRC screening outcomes. AI in CRC screening increases the adenoma detection rate, decreases existing technical variation among colonoscopists, and enables the characterization of diminutive polyps with high accuracy for further management6. AI algorithms can quickly and precisely analyze large volumes of medical data, recognizing patterns, anomalies, and early symptoms of CRC in colonoscopy images, computed tomography scans, and magnetic resonance imaging scans. As a result, anomaly diagnoses are made with greater precision and the number of false-negative or false-positive outcomes is decreased7. Risk stratification The ability to identify those who are more likely to acquire CRC has been demonstrated to be a valuable application of AI-driven risk stratification models. These algorithms can pinpoint high-risk people and prescribe focused screening and preventive interventions by analyzing various risk factors, including age, family history, lifestyle, and genetic markers8. Virtual colonoscopy AI technology enables the development of virtual colonoscopy, or computed tomography colonography, which uses advanced imaging methods and AI algorithms to provide accurate 3-dimensional images of the colon. This noninvasive procedure offers similar accuracy to regular colonoscopy while being less invasive and more comfortable for patients7. Enhanced detection of polyps To avoid CRC, precancerous polyps must be found and removed. AI-driven colonoscopy devices with computer-aided detection capabilities can improve the detection of polyps, particularly those that could be missed during a standard colonoscopy. This lowers the rate of neglected adenoma and improves the screening procedure’s efficiency9. Genomic analysis for personalized treatment approaches AI technology allows personalized treatment options by examining an individual’s health information and genetic makeup. Health care professionals can adapt medications to maximize effectiveness and minimize side effects, improving patient outcomes and quality of life10. Remote monitoring and telemedicine AI-driven remote monitoring systems have increased patient access to health care services and brought colorectal screening to impoverished areas. Health care practitioners can remotely analyze patients’ symptoms, offer counsel, and offer preliminary diagnoses using telemedicine platforms with AI capabilities, ensuring prompt intervention and reducing health care disparities10. Despite the significant advantages of AI in CRC diagnosis and treatment, there are limitations and risks to consider, such as data privacy, regulatory compliance, and the ongoing evaluation and improvement of AI systems. Health care workers need proper training to evaluate the results produced by AI and base judgments on the outputs of the AI system11. In conclusion, applying AI technology in colorectal screening constitutes a paradigm change in contemporary medicine and offers a wide range of advantages, including increased precision, personalized care, and improved patient care. To ensure the appropriate and efficient use of AI in colorectal health, as we embrace these innovations, it is critical to maintain a collaborative approach among health care professionals, academics, policymakers, and technology developers. Ethical approval Not applicable. Sources of funding No funding received for this work Author contributions K.F. and M.A.: data collection, analysis, and written original draft. E.N.: conceptualized the study and interpretation of the data. I.A.: reviewed, edited, and approved the final version of the manuscript. M.H.B.: supervision. Conflict of interest disclosures The authors declare that there are no conflicts of interest. Research registration unique identifying number (UIN) Not applicable Guarantor All authors.