Abstract
Background:
Colorectal cancer (CRC) remains a major contributor to cancer-related mortality globally, with a concerning increase in early-onset cases. Although colonoscopy is the established gold standard for CRC screening, its effectiveness is constrained by operator variability, inconsistent bowel preparation, and disparities in access. Artificial intelligence (AI) has emerged as a promising adjunct across the CRC care continuum, offering potential enhancements in screening, diagnosis, and risk stratification. This review aims to examine the current applications of AI in CRC screening and diagnosis, with particular emphasis on AI-assisted endoscopy, non-invasive screening modalities, and digital pathology. Key implementation challenges are also discussed.
Methods:
This narrative review synthesizes evidence from randomized controlled trials, prospective cohort studies, meta-analyses, and emerging translational research. It evaluates AI-based computer-aided detection (CADe) and computer-aided diagnosis (CADx) systems in colonoscopy, machine learning–driven risk prediction models and biomarker integration for non-invasive screening, and deep learning applications in whole-slide histopathology for CRC.
Results and Discussion:
AI-assisted colonoscopy has demonstrated consistent improvements in adenoma detection rates, particularly through enhanced identification of diminutive lesions. Several multicenter trials have also reported increased detection of advanced adenomas. CADx systems employing enhanced imaging modalities have achieved optical diagnostic performance comparable to expert endoscopists and may facilitate cost-saving strategies such as “resect-and-discard” or “diagnose-and-leave.” Beyond the endoscopy suite, AI and machine learning algorithms can integrate multimodal data, including demographic, dietary, biomarker, and circulating cell-free DNA (cfDNA) profiles, to identify individuals at elevated risk and strengthen non-invasive screening approaches. In pathology, AI-powered systems have shown promise in reducing interobserver variability, detecting subtle morphologic and molecular features (e.g., microsatellite instability), and informing treatment planning. Despite these advances, translation into routine clinical practice remains limited by several factors: heterogeneity in training datasets, potential algorithmic bias, insufficient real-world validation, substantial infrastructure requirements, and the need for interpretable outputs that clinicians can trust. Addressing these barriers will be essential to ensure safe, and effective integration of AI into CRC care.
Conclusion:
Artificial intelligence holds substantial promise for enhancing the accuracy, scalability, and personalization of CRC screening and diagnosis. Realizing this potential will require rigorous multicenter prospective validation, standardization of datasets and reporting frameworks, and the development of clinical workflows that preserve provider judgment and promote equitable access to care.
Introduction
Colorectal cancer (CRC) holds the dubious distinction of being the third most common cancer globally and the second leading cause of cancer-related deaths. Although the overall rate of CRC cases has dropped in the United States, there’s a concerning trend of increasing early-onset CRC, diagnosed in individuals under 50. This shift places a growing health burden on younger demographics. Despite strides in screening initiatives, CRC is often caught at later stages, leading to grim outcomes and elevated mortality. The American Society of Colorectal Surgery reveals that up to 70% of CRC cases are identified when patients already exhibit symptoms, highlighting the urgent need for better early detection strategies. 1
Traditional diagnostic modalities for CRC include laboratory tests, endoscopy, and histopathological examination, with colonoscopy remaining the gold standard. However, the effectiveness of these methods is often hindered by operator variability, inadequate preparation, and limited access in underserved populations. In this context, emerging technologies such as artificial intelligence (AI) are being increasingly recognized for their potential to revolutionize CRC screening, diagnosis, and management.2,3
In the ever-evolving landscape of medicine, AI stands as a beacon of innovation, transforming the way health care professionals tackle complex challenges with unmatched precision and scalability. As a branch of computer science, AI empowers machines to mimic human intelligence, leading to revolutionary breakthroughs in medical diagnostics and treatment strategies. From the foundational rule-based systems to the sophisticated realms of machine learning (ML) and deep learning (DL) algorithms, AI is reshaping the health care industry by its ability to process enormous datasets, identify intricate patterns, and predict patient outcomes with remarkable accuracy.
The surge in computational power, coupled with access to vast clinical datasets, has propelled AI into the realm of personalized medicine, particularly in the early detection and screening of CRC. This lethal disease demands a proactive approach, and AI is at the forefront, offering innovative solutions that promise to change the narrative.
AI’s role in CRC screening can be divided into two main categories: virtual and physical tools. Virtual tools utilize advanced algorithms to sift through noninvasive data—such as patient demographics, lifestyle factors, and biomarkers—to stratify risk and personalize screening protocols. By doing so, AI helps identify high-risk individuals who may benefit from more intensive screening, potentially catching cancer at an earlier, more treatable stage.
On the other hand, physical AI applications are revolutionizing traditional diagnostic methodologies. AI-enhanced colonoscopy and histopathological examinations are setting new standards by significantly improving polyp detection rates (PDR) and adenoma detection rates (ADR). These advancements not only reduce the number of missed adenomas but also lower the incidence of interval cancers, thus enhancing patient outcomes.
This comprehensive review explores the cutting-edge applications of AI in CRC diagnosis and screening, with a special focus on both endoscopic and noninvasive techniques. It also sheds light on the challenges and opportunities that accompany the integration of AI into clinical settings. The potential for AI to elevate early detection, boost diagnostic precision, and facilitate the shift toward personalized medicine is immense, paving the way for a future where health care is as individualized as it is effective. As we navigate this exciting frontier, the promise of AI in medicine beckons a new era of hope and healing.
Application of AI in endoscopic diagnosis
Colonoscopy is the gold standard procedure for diagnosing CRC and is strongly recommended as a screening tool for early detection. 4 However, studies have shown a 2–8% risk of new or missed CRC after a colonoscopy.5,6 Also, it is limited by the high operator variability and quality of preparation, which leads to high variability in the PDR and ADR. Colonoscopy quality metrics such as ADR have been shown to decrease the risk of development of interval cancer, with every 1% increase in ADR correlated with a 3% decrease in the risk of interval CRC.7,8 As a result, polyp/adenoma detection is a key area of interest, and there has been a focus on the development and application of AI-assisted colonoscopy techniques.
Polyp detection
In recent years, several computer-aided detection (CADe) systems have been tested to improve the accuracy of CRC detection. Most novel systems use DL techniques, with computer algorithms that are driven by convolutional neural networks (CNN). 9 These are complexes of three layers: convolutional, pooling, and fully connected layers. The first two layers play a role in feature extraction from input, while the fully connected layers are used to map the features into a final output. One of the earliest models was proposed by Urban et al., who utilized 8461 colonoscopy images with a total of 4088 unique polyps derived from 2000 colonoscopy videos to train the system and achieved an accuracy of 96.4%. 10 Since then, numerous other CADe systems have been developed.
Wang et al., in 2019, were the first to study the role of CADe systems in a randomized controlled trial (RCT). They demonstrated a significant increase in ADR (29.1% vs. 20.3%, p < 0.001); however, this was mostly contributed to by higher detection of diminutive adenomas (185 vs. 102; p < 0.001), with no significant increase in large adenomas (77 vs. 58, p = 0.075) noted. 11 Interestingly, despite the increased number of polyps noted in the CADe group, the total withdrawal time was similar (6.07 min vs. 6.18 min, p = 0.15) between the two groups. Since then, multiple RCTs have demonstrated similar results. However, most of the studies have been limited by their sample size and lack of increased detection of polyps with clinical significance, that is, large polyps.12–17
In a recent RCT by Xu et al. (patient population of 3059), they demonstrated an increased detection of advanced adenomas (6.6% vs. 4.9%, p = 0.041). 18 They also demonstrated an increase in the ADR of both experts (42.3% vs. 32.8%; p < 0.001) and nonexpert endoscopists (37.5% vs. 32.1%; p = 0.023). However, the median withdrawal time (8.3 min vs. 7.8 min; p = 0.004) was slightly longer in the AI-assisted colonoscopy group. Meta-analyses have also shown varied results over the years. Spadaccini et al. in a meta-analysis of RCTs noted increased odds of detection of large polyps (>10 mm) with CADa systems (R 1.69 [95% CI: 1.10–2.60]); however, multiple studies done since have been unable to demonstrate similar findings. 19
When analyzing the utility of CADe systems in a real-world study setting, the data are further unclear. In the last 3–4 years, numerous studies have been done with variable outcomes. Patel et al., in a meta-analysis of nonrandomized studies, noted no increase in the ADR or mean adenoma per colonoscopy between CADe systems and standard colonoscopy. 20 They also did not note an increase in the withdrawal time for the procedure. Thus, even though controlled studies have demonstrated some positive impact of CADe systems, this has not been noted during real-world studies.
Polyp characterization
Advanced imaging techniques, such as blue light imaging (BLI), narrowband imaging (NBI), and endocytoscopy, can be used to predict the histology of polyps in situ. 21 Classification systems such as the NBI International Colorectal Endoscopic Classification and the BLI Adenoma Serrated International Classification aid in confidently differentiating adenomatous polyps from nonadenomatous polyps. This prediction of polyp histology is referred to as optical diagnosis. Guidelines by the American Society for Gastrointestinal Endoscopy and the European Society of Gastrointestinal Endoscopy (ESGE) set thresholds for accurate optical diagnosis. These include the Preservation and Incorporation of Valuable Endoscopic Innovations (PIVI) and Simple Optical Diagnosis Accuracy criteria, which establish a 90% negative predictive value (NPV) threshold for strategies like “Diagnose and Leave.”22,23
ML has significantly advanced optical diagnosis, with multiple computer-aided diagnosis (CADx) systems demonstrating performance comparable to that of expert endoscopists. Gross et al. conducted the first prospective study using Zoom NBI colonoscopy to develop a CADx system. The system was tested on 434 polyps 10 mm or smaller in size. They demonstrated similar sensitivity (93.4% vs. 95%), specificity (91.8% vs. 90.3%), and accuracy (92.7% vs. 93.1%) between the experts and CADx alone groups. Both the expert and the CADx groups significantly outperformed the nonexpert group. 24 In 2018, Mori et al. created a CADx system using a large training set of 61,952 images. The system was tested on 466 diminutive adenomas (size <5 mm), achieving an NPV of 95% for rectosigmoid adenomas and 60% for proximally located adenomas. 25
CAD EYE is a CADx system based on BLI, which was tested in a recent multicenter clinical trial. Rondotti et al. studied the role of CAD EYE in assisting endoscopists in making confident and accurate optical diagnoses. 26 It was noted that both the endoscopist alone group and the AI-assisted group (final diagnosis was provided by combining the polyp characterization by the endoscopist and AI) were able to achieve the required threshold of NPV > 90% and met the PIVI criteria. In addition, the AI-alone group demonstrated a diagnostic accuracy similar to that of the endoscopist, with or without assistance. This study design mimics the real-time setting in which AI would assist physicians’ decisions. However, this study was confined to diminutive rectosigmoid polyps (DRSP), which limits its generalizability.
Lange et al. explored the CAD EYE system for 253 polyps of varied sizes (1–35 mm) and locations (throughout the colon). 27 The results showed comparable sensitivity (88% vs. 80%), specificity (83% vs. 83%), and PPV (89% vs. 88%), but higher NPV (82% vs. 73%) for endoscopists than for CAD EYE alone. Therefore, further large studies are required to validate this system. Despite promising outcomes, the CAD EYE did not meet the PIVI criteria, necessitating further large-scale studies. Kobayashi et al. evaluated expert versus nonexpert endoscopists’ diagnostic accuracy with or without the CAD EYE assistance. The nonexpert group established average optical diagnostic accuracies of 85.3% and 100% with and without CAD EYE, respectively. For experts, the diagnostic accuracies were 92.3% and 100% for patients with and without CAD, respectively. 28 The AI identified specific polyp characteristics that were frequently missed by the trainees, suggesting a role for CAD EYE in the training of endoscopists.
“GI Genius Intelligent Endoscopy Module” is another CADx-based system that utilizes white light technology. Hassan, in a recent study that evaluated this system on DSRP, achieved an NPV of 97.6%. Furthermore, it was estimated that the use of CADx could potentially reduce polypectomies by 44% and the requirement for biopsy by 83% in the “diagnose and leave” and “resect and discard” strategies, respectively. 29 Therefore, CADx has shown potential of reducing procedure-related complications and costs.
Therefore, CADx has proven to be significantly accurate in optical diagnosis as a standalone and while assisting endoscopist. Further studies are required to evaluate the use of CADx to verify and refine its quality for the diagnosis of polyps other than DRSP. As CADx technology depends on high-quality views to accurately predict histology it requires a skilled endoscopist for optimal use. Although CADx might play role in training of endoscopists, the implementation of CADx should be approached cautiously because excessive reliance on technology by trainees may hinder their ability to develop independent skills in making accurate optical diagnoses.
Application of AI in noninvasive screening
Noninvasive screening tests are characterized by their convenience, safety, and affordability, although these often come at the cost of accuracy. Among current noninvasive screening options for CRC, Cologuard is the most sensitive modality, boasting a sensitivity of 92.3% for CRC detection. 30 The Septin 9 test is an FDA-approved blood-based test for individuals aged 50 and older who decline other recommended screening modalities; however, it is not endorsed in screening guidelines due to its lower sensitivity (48–64%) and specificity (88–92%). 31 AI and ML algorithms, such as artificial neural networks (ANNs), offer the potential to improve noninvasive screening. These technologies integrate multiple parameters—age, sex, dietary habits, routine blood tests, and genetic and proteomic biomarkers—to stratify individual risk for CRC.
Nartowt et al. trained and evaluated seven ML algorithms using datasets from the Pancreatic, Lung, Colorectal, and Ovarian Cancer Screening trial and the National Health Interview Survey. Cross-testing between the two datasets was done to ensure robustness and generalizability, achieving a Transparent Reporting of Multivariable Prediction Model for Individual Prognosis or Diagnosis Level 3 evaluation. Among the models an ANN with Gaussian expectation-maximization imputation performed best, achieving a concordance of 0.70 ± 0.02, sensitivity of 0.63 ± 0.06, and specificity of 0.82 ± 0.04. 32 Notably, the ANN excelled among individuals aged 18–49, demonstrating high specificity through accurate negative predictions. This ANN-based model highlights the potential for noninvasive, cost-effective CRC risk stratification using personal health data.
Kinar et al. developed another ML-based risk-prediction model using complete blood cell counts (3–6 months before the diagnosis of CRC), age, and sex. This model, trained on 80% of an Israeli dataset (n = 606,403, including 3135 CRC cases), was validated on the remaining 20% and a UK dataset (5061 CRC cases and 25,613 controls). The model achieved a specificity of 88 ± 2% at 50% sensitivity in both validation datasets. 33 Age emerged as the most critical risk factor, but combining parameters significantly improved diagnostic accuracy. Integration with fecal occult blood testing doubled the CRC detection rate.
Liquid biopsies analyzing cell-free DNA (cfDNA) profiles have shown promise in early detection of CRC. 34 Wan et al. developed an ML model based on cfDNA, achieving 85% sensitivity and specificity for early-stage CRC detection. 35 Similarly, AI-EMERGE is an ongoing clinical trial where an ML model using cfDNA, epigenetic, and protein biomarkers is being used. were identified using ML. This model achieved 92% sensitivity at 90% specificity in a validation cohort of 591 participants. 36 Despite these advances, sensitivity for advanced adenomas remains suboptimal. The PREEMPT and ECLIPSE are similar clinical trials working to develop and validate blood-based CRC screening tests.37,38
MicroRNAs (miRNAs), a subset of noncoding RNA deregulated in CRC, offer another avenue for noninvasive screening. However, miRNA levels are nonlinearly correlated with tumor staging. AI models like ANN can be trained to individualize miRNA expressions and create linear relationships with miRNA and CRC. Afshar et al. trained an ANN model and identified 4 miRNAs’ (miR-1247-3p, miR-614, miR-6726-5p, and miR-7111-5p) with the highest signal-to-noise ratio, achieving an area under the curve of 1. 39
Peptide biomarkers like hemoglobin (FIT) are widely accepted for screening purposes but they lack sensitivity for precancerous lesions. Similarly, serum peptides like CEA, CA11-19, and immunoregulatory proteins did not achieve sufficient accuracy to get widely accepted as screening test. However, ML shows a potential to identify and integrate multiple biomarkers to develop an ideal screening tool. Ivancic et al. discovered 5 peptide biomarkers (LRG1, EGFR, ITIH4, HPX, and SOD3) that are strongly associated with CRC. For CRC detection, this AI algorithm achieved 70% specificity and 89% sensitivity. 40 However, they were unable to identify specific peptides to differentiate advanced adenomas (precancerous lesions) from healthy controls.
An ML algorithm incorporating 26 parameters—including CEA, AFP, CA19-9, CBC, liver function, renal function, blood glucose, and lipid panels—has shown potential for CRC and adenoma detection. A support vector machine achieved 90.4% sensitivity and 77.1% specificity for colorectal adenoma, while a random forest model achieved 90.2% sensitivity and 91.2% specificity for CRC. 41 Despite promising results, the study’s small sample size and single-institution design limit its generalizability.
Dietary factors are among the most significant modifiable risk factors for CRC. Abdul Rahman et al. developed an AI model incorporating dietary data from a multinational cohort of 109,342 individuals (7326 CRC cases). This algorithm accurately classified 97–99% of the cases. 42 Dietary fiber (negatively correlated to CRC) and fat intake (positively correlated to CRC) were the most important contributors to this risk-prediction model. Turgeon et al. developed a stool-based AI algorithm that uses mRNA biomarkers and FIT, achieving 92.3% sensitivity for CRC and 82% sensitivity for advanced adenomas/precancerous lesions. 43
In conclusion, AI and ML are revolutionizing noninvasive CRC screening by integrating multimodal data, including genetic, proteomic, and routine health markers. Emerging AI models significantly enhance diagnostic accuracy, particularly for early-stage CRC detection, where traditional methods fall short. However, challenges persist in detecting precancerous lesions and advanced adenomas. Future advancements should focus on improving sensitivity while maintaining accessibility and cost-effectiveness, paving the way for comprehensive, noninvasive CRC screening solutions.
Histopathological Diagnosis of CRC: An Overview
Histopathological assessment remains the “gold standard” for diagnosis of CRC. Biopsies obtained via endoscopic biopsy or through surgical resection of the primary tumor site or metastatic sites if necessary are fixed to preserve tissue integrity, sectioned for slides, and stained prior to microscopic evaluation by a pathologist. Pathologists assess morphological and molecular features to make a comprehensive diagnosis and detail these assessments following the standardized checklist provided by the College of American Pathologists (CAP).44,45 The goal of a standardized pathology report is to ensure consistent diagnosis and treatment for patients and to facilitate the exchange of information in multicenter clinical trials or international studies.
The CAP protocol for examination of excisional biopsy specimens from patients with primary carcinoma of the colon and rectum include the following key criteria: specimen type, tumor site and size, macroscopic tumor perforation, histological type and grade, microscopic tumor extension, margins (proximal, distal and radial), treatment effect (for tumors treated with neoadjuvant therapy), lymphovascular invasion, perineural invasion, tumor deposits (discontinuous extramural extension), and TNM staging (including the total number of lymph nodes examined and the total number of nodes involved). Some reports may also include leading edge of tumor (infiltrative or expansile), tumor budding presence or absence, or histological features that are suggestive of Microsatellite Instability (MSI). 46
However, this process requires not only in-depth knowledge of pathological aspects of CRC but also skill and experience, given that an accurate diagnosis may be subject to personal interpretation of subtle differences. Previous studies have demonstrated significant interobserver variability in the assessment of pathological parameters of CRC, some of which are considered critical to further evaluation and management of malignant lesions or surveillance colonoscopy interval.47,48 Not unexpectedly, specialist pathologists tend to have a higher degree of consensus than general pathologists which supports the need for consultation with specialists or peers in difficult cases. 49
While multiple studies have demonstrated this variability, there has been little investigation of the root cause beyond differences in pathologist interpretation. A larger study in breast cancer suggests there may be overestimation of the extent of pathologist-related variability, instead suggesting some variability may be due to differences in diagnostic criteria, diagnostic philosophies (diagnosis with morphological features alone compared with morphological features and clinical correlation), diagnostic coding, poor slide quality, and slide artifacts. 50
Additional studies have sought to determine if this discordance could be pinpointed on the spectrum from benign polyps to advanced carcinomas or if certain pathological features were more commonly disputed. One study demonstrated inconsistencies across the full spectrum with only moderate agreement on nonadvanced adenomas versus advanced adenomas and significant discrepancies characterizing villous lesions despite additional efforts to standardize reporting.51,52 Lymphovascular invasion and tumor grading were found to be more inconsistent features in one study where second opinion significantly altered risk perception (by up to 10%) for endoscopically removed early CRC. 53
AI Applications in Histopathological Diagnosis of CRC
Digital imaging was the first step in computer-aided analysis, followed shortly by the introduction of whole-slide imaging where an entire sample on a glass slide can be converted into a high-resolution virtual slide utilizing a robotically driven trinocular microscope to capture a sequence of images. 54 This trend toward digitization has facilitated not only sharing slides for telepathology, specialist consultation, or second review without the threat of lost or degraded samples during shipping, but also the use of computational analysis to extract information from digitized images in combination with their associated metadata, which can be further processed using algorithms.
Algorithms are categorized into “hypothesis-driven” or “targeted” algorithms and AI algorithms, which can be considered to be more data-driven. Hypothesis-driven or targeted algorithms rely on programming to perform set tasks or calculations based on prior knowledge of the target morphology, disease mechanism, and/or pathogenesis. The positive pixel count algorithm is an example of a straightforward “targeted” algorithm, which works by counting the image pixels that are classified as “positive” based on specific hue criteria defined by the user. 55 This type of algorithm is being used to investigate the potential of radiomics texture features as potential biomarkers. One study demonstrated that CRC tumors showing less texture heterogeneity behaved more aggressively and were associated with unfavorable 5-year overall survival compared with those showing more heterogeneity. 56
AI algorithms revolve around the use of DL, particularly CNN, which are trained on large datasets of labeled images to automatically detect relevant features, classify or detect cancer, or predict genetic alterations from histopathological images. Within CRC, there are already studies implementing AI to distinguish between metastatic and nonmetastatic tissue, identify early-stage colorectal tumor, detect infiltrating lymphocytes, correlate tissue patterns with MSI status, and predict metastasis based on histopathological features.57–62
Beyond diagnosis, AI could play a crucial role in improving outcomes for CRC patients by tailoring more personalized treatment plans. An AI tool developed by researchers at Harvard Medical School and National Cheng Kung University analyzed microscopic images of colorectal tumor samples to look for visual markers related to tumor types, genetic mutations, epigenetic alterations, disease progression, and patient survival. 63 This model was able to detect subtle patterns in the images to not only accurately predict overall survival following diagnosis but also predict patient response to certain therapies such as immune checkpoint inhibitors. This model is particularly exciting as it outperformed human pathologists as well as current AI models, maintaining accuracy when fed “real-world” data.
AI Applications in Histopathological Diagnosis of CRC: Challenges and Limitations
While AI has shown great promise in pathology, there are significant challenges and limitations that must be addressed to ensure that AI tools are reliable, effective, and safe for widespread use in diagnosing diseases like CRC. This starts with the development of AI models, given the requirement for large, high-quality datasets that represent real-world variability for training. This challenge is twofold: While collaboration is essential to generate a large volume of annotated dataset, variability in preparation of histopathological slides, image acquisition techniques, and annotation methods across laboratories and institutions can introduce inconsistencies that affect the model’s performance.
During development, most AI models are tested and validated using retrospective datasets from a single institution, which may not reflect the diversity of real-world clinical data. AI models can inherit biases present in the training data, such as demographic biases related to race, gender, or socioeconomic status. In an era where we are working to eliminate health care disparities, we must be mindful that if AI tools deployed without consideration for the diversity of patient populations may result in disparate diagnostic accuracy and potentially worsened health care inequalities. The success of AI tools will require prospective validation where the AI tool is tested on new, real-time data from multiple institutions with diverse patient populations.
In addition to incorporation into the existing diagnostic framework to complement rather than disrupt the clinical decision-making process, the transparency of AI models may limit their utility, particularly in high-risk fields like oncology. Clinicians must be able to justify their diagnostic decisions, and they may be reluctant to adopt AI tools if they cannot understand the rationale behind the AI’s decision-making process. There are ongoing efforts to delineate accountability in case of diagnostic errors that may occur with the use of AI tools.
Last, high costs associated with the necessary infrastructure to implement and maintain AI models may limit the widespread adoption. The use of AI requires ongoing investment in data collection, cybersecurity, and retraining for model optimization as new data or medical knowledge becomes available, which may be resource-intensive and challenging for many institutions.
Discussion
The advent of AI in CRC screening and diagnosis is revolutionizing the field, offering new opportunities to improve patient outcomes. AI technologies, such as computer-aided detection (CADe) systems, have enhanced ADR and polyp characterization in colonoscopy, demonstrating potential to standardize and elevate diagnostic accuracy across diverse clinical settings. These advancements are critical in addressing the inherent limitations of traditional screening methods, such as operator variability and accessibility issues, which have historically impeded optimal CRC detection.
In the area of noninvasive screening, AI-driven models have shown promise by integrating multifaceted datasets—ranging from demographic and genetic to proteomic information—to effectively stratify CRC risk and refine screening protocols. These innovations are pivotal in identifying high-risk individuals early, thereby facilitating timely interventions and improving prognostic outcomes. However, the journey to fully integrating AI into CRC screening is fraught with challenges. Key among these are data variability, potential biases in algorithm training, and the significant resource investment required for developing and maintaining AI infrastructure. The cost and complexity of implementing AI solutions necessitate careful planning and collaboration among stakeholders to ensure these technologies can be equitably deployed across different health care environments.
Moreover, while AI has shown considerable promise in enhancing histopathological diagnosis by automating whole-slide image analyses, thus increasing consistency and efficiency, its integration into routine practice is not without hurdles. The interpretability of AI models remains a major concern, as clinicians must be able to trust and understand AI-generated insights to make informed decisions. Additionally, high implementation costs and the need for extensive validation studies pose significant barriers that must be overcome to ensure widespread adoption.
Conclusion
In conclusion, AI stands as a transformative force in CRC screening and diagnosis, offering robust tools that can enhance accuracy, efficiency, and personalization of care. As we continue to navigate this promising frontier, it is imperative to address the challenges of data bias, transparency, and cost. Ensuring rigorous validation through large-scale, multicenter studies will be crucial to cement AI’s role in routine clinical practice. By fostering collaboration across technological, clinical, and policy domains, we can unlock the full potential of AI to revolutionize CRC care, ultimately paving the way for a future where early detection and personalized medicine are not just aspirations but realities that significantly improve patient outcomes.
Authors’ Contributions
Anmol S., R.W., and V.S. conceptualized and designed the study. Anmol S., R.W., and N.S. conducted the literature review, interpreted data, and drafted the original article. Anjali S. and V.S. supervised the study and made critical revisions.
Footnotes
Author Disclosure Statement
The content is solely the responsibility of the authors and does not necessarily represent the official views of the institutions. V.S. reports: Research funding for clinical trials paid to institution from Abbvie, Agensys, Alfasigma, Altum, Amgen, Bayer, BERG Health, Blueprint Medicine, Boston Biomedical, Boston Pharmaceuticals, D3 Bio, Dragonfly Therapeutics, Exelixis, Fujifilm, GlaxoSmith-Kline, Idera Pharmaceuticals, Incyte, Inhibrix, Eli Lilly/Loxo Oncology, MedImmune, NanoCarrier, Novartis, PharmaMar, Pfizer, Relay Therapeutics, Roche/Genentech, Takeda, Turning Point Therapeutics, and Vegenics. Consulting/advisory role (paid to institution) from Abbvie, Astex Pharmaceuticals, AstraZeneca, Bayer, Genmab, Incyte, Lilly/Loxo Oncology, Novartis, Obsidian Therapeutics, Pfizer, Pheon Therapeutics, Regeneron, Relay Therapeutics, Roche, Endeavor Biomedicines, RevMed, LabGenius therapeutics, other consulting/advisory role/CME from Helsinn Healthcare, Jazz Pharmaceuticals, Incyte, Loxo Oncology/Lilly, Novartis, Relay Therapeutics, Daiichi Sankyo, Illumina, Bayer, Medscape, OncLive, Clinical Care Communications, PERS, and Med learning group. Other authors have no disclosures.
Funding Information
No funding was received for this article.