The role of artificial intelligence based systems for cost optimization in colorectal cancer prevention programs

Machine learning in colorectal polyp surveillance: A paradigm shift in post-endoscopic mucosal resection follow-up

Colorectal cancer remains a major health concern, with colorectal polyps as key precursors. Endoscopic mucosal resection (EMR) is a common treatment, but recurrence rates remain high. Traditional surveillance strategies rely on polyp characteristics and completeness of the resection potentially missing key risk factors. Machine learning (ML) offers a transformative approach by integrating patient-specific data to refine risk stratification. Recent studies highlight ML models, such as Extreme Gradient Boosting, which outperform conventional methods in predicting polyp recurrence within one-year post-EMR. These models incorporate factors like age, smoking status, family history, and pathology, optimizing follow-up recommendations and minimizing unnecessary procedures. Artificial intelligence (AI)-driven tools and web-based calculators enhance clinical workflow by providing real-time, personalized risk assessments. However, challenges remain in external validation, model interpretability, and clinical integration. Future surveillance strategies should combine expert judgment with AI insights to optimize patient outcomes. As gastroenterology embraces AI, ML-driven surveillance represents a paradigm shift, advancing precision medicine in colorectal polyp management. This editorial explores AI’s role in transforming post-EMR follow-up, addressing benefits, limitations, and future directions.

Empowering cancer prevention with AI: unlocking new frontiers in prediction, diagnosis, and intervention

Artificial intelligence is rapidly changing our world at an exponential rate and its transformative power has extensively reached important sectors like healthcare. In the fight against cancer, AI proved to be a novel and powerful tool, offering new hope for prevention and early detection. In this review, we will comprehensively explore the medical applications of AI, including early cancer detection through pathological and imaging analysis, risk stratification, patient triage, and the development of personalized prevention approaches. However, despite the successful impact AI has contributed to, we will also discuss the myriad of challenges that we have faced so far toward optimal AI implementation. There are problems when it comes to the best way in which we can use AI systemically. Having the correct data that can be understood easily must remain one of the most significant concerns in all its uses including sharing information. Another challenge that exists is how to interpret AI models because they are too complicated for people to follow through examples used in their developments which may affect trust, especially among medical professionals. Other considerations like data privacy, algorithm bias, and equitable access to AI tools have also arisen. Finally, we will evaluate possible future directions for this promising field that highlight AI's capacity to transform preventative cancer care.

Artificial Intelligence and Early Detection of Breast, Lung, and Colon Cancer: A Narrative Review

Artificial intelligence (AI) is revolutionizing early cancer detection by enhancing the sensitivity, efficiency, and precision of screening programs for breast, colorectal, and lung cancers. Deep learning algorithms, such as convolutional neural networks, are pivotal in improving diagnostic accuracy by identifying patterns in imaging data that may elude human radiologists. AI has shown remarkable advancements in breast cancer detection, including risk stratification and treatment planning, with models achieving high specificity and precision in identifying invasive ductal carcinoma. In colorectal cancer screening, AI-powered systems significantly enhance polyp detection rates during colonoscopies, optimizing the adenoma detection rate and improving diagnostic workflows. Similarly, low-dose CT scans integrated with AI algorithms are transforming lung cancer screening by increasing the sensitivity and specificity of early-stage cancer detection, while aiding in accurate lesion segmentation and classification. This review highlights the potential of AI to streamline cancer diagnosis and treatment by analyzing vast datasets and reducing diagnostic variability. Despite these advancements, challenges such as data standardization, model generalization, and integration into clinical workflows remain. Addressing these issues through collaborative research, enhanced dataset diversity, and improved explainability of AI models will be critical for widespread adoption. The findings underscore AI's potential to significantly impact patient outcomes and reduce cancer-related mortality, emphasizing the need for further validation and optimization in diverse healthcare settings.

Imaging and Metabolic Diagnostic Methods in the Stage Assessment of Rectal Cancer

Rectal cancer (RC) is a prevalent malignancy with significant morbidity and mortality rates. The accurate staging of RC is crucial for optimal treatment planning and patient outcomes. This review aims to summarize the current literature on imaging and metabolic diagnostic methods used in the stage assessment of RC. Various imaging modalities play a pivotal role in the initial evaluation and staging of RC. These include magnetic resonance imaging (MRI), computed tomography (CT), and endorectal ultrasound (ERUS). MRI has emerged as the gold standard for local staging due to its superior soft tissue resolution and ability to assess tumor invasion depth, lymph node involvement, and the presence of extramural vascular invasion. CT imaging provides valuable information about distant metastases and helps determine the feasibility of surgical resection. ERUS aids in assessing tumor depth, perirectal lymph nodes, and sphincter involvement. Understanding the strengths and limitations of each diagnostic modality is essential for accurate staging and treatment decisions in RC. Furthermore, the integration of multiple imaging and metabolic methods, such as PET/CT or PET/MRI, can enhance diagnostic accuracy and provide valuable prognostic information. Thus, a literature review was conducted to investigate and assess the effectiveness and accuracy of diagnostic methods, both imaging and metabolic, in the stage assessment of RC.

Artificial intelligence in the diagnosis and treatment of acute appendicitis: a narrative review

Artificial intelligence is transforming healthcare. Artificial intelligence can improve patient care by analyzing large amounts of data to help make more informed decisions regarding treatments and enhance medical research through analyzing and interpreting data from clinical trials and research projects to identify subtle but meaningful trends beyond ordinary perception. Artificial intelligence refers to the simulation of human intelligence in computers, where systems of artificial intelligence can perform tasks that require human-like intelligence like speech recognition, visual perception, pattern-recognition, decision-making, and language processing. Artificial intelligence has several subdivisions, including machine learning, natural language processing, computer vision, and robotics. By automating specific routine tasks, artificial intelligence can improve healthcare efficiency. By leveraging machine learning algorithms, the systems of artificial intelligence can offer new opportunities for enhancing both the efficiency and effectiveness of surgical procedures, particularly regarding training of minimally invasive surgery. As artificial intelligence continues to advance, it is likely to play an increasingly significant role in the field of surgical learning. Physicians have assisted to a spreading role of artificial intelligence in the last decade. This involved different medical specialties such as ophthalmology, cardiology, urology, but also abdominal surgery. In addition to improvements in diagnosis, ascertainment of efficacy of treatment and autonomous actions, artificial intelligence has the potential to improve surgeons' ability to better decide if acute surgery is indicated or not. The role of artificial intelligence in the emergency departments has also been investigated. We considered one of the most common condition the emergency surgeons have to face, acute appendicitis, to assess the state of the art of artificial intelligence in this frequent acute disease. The role of artificial intelligence in diagnosis and treatment of acute appendicitis will be discussed in this narrative review.

Overcoming geographical and socioeconomic limitations in colorectal cancer screening

Despite colorectal cancer's (CRC) high global incidence, residents of low- and middle- income countries, as well as low-income minorities in advanced economies have low screening rates. Observational studies demonstrate that in these groups higher incidence of CRC is observed, yet screening rates remain low for consistent reasons. Low income, low educational background, and lack of awareness in combination with inadequate social security of certain population groups impede access and compliance rates to CRC screening. On the other hand, despite the global availability of multiple screening approaches (colonoscopy, sigmoidoscopy, faecal occult blood test, faecal immunochemical test, computed tomography-colonography, etc.) with proven diagnostic validity, many low-income countries still lack established screening programs. The absence of screening guidelines in these countries along with the heterogeneity of guidelines in the rest of the world, demonstrate the need for global measures to tackle this issue comprehensively. An essential step forward is to develop a global approach that will link specific elements of screening with the incidence and available resources in each country, to ensure the achievement of at least a minimum screening program in low-income countries. Utilizing cheaper, cost-effective techniques, which can be carried out by less specialized healthcare providers, might not be equivalent to endoscopy for CRC screening but seems more realistic for areas with fewer resources. Awareness has been highlighted as the most pivotal element for the effective implementation of any screening program concerning CRC. Moreover, multiple studies have demonstrated that outreach strategies and community-based educational programs are associated with encouraging outcomes, yet a centrally coordinated expansion of these programs could provide more consistent results. Additionally, patient navigator programs, wherever implemented, have increased CRC screening and improved follow-up. Therefore, global coordination and patient education seem to be the main areas on which policy making needs to focus.