Recent medical research reveals that artificial intelligence systems designed to help Doctors detect colorectal cancer might actually be creating an unexpected dependency. While AI-assisted colonoscopy procedures increased adenoma detection rates by up to 24% in multiple studies, this technological advancement raises a critical question: are we inadvertently dulling the diagnostic skills that have taken healthcare practitioners years to develop?
The promise vs. reality of AI-assisted healthcare
What we currently know about AI in medical practice
Three major randomised controlled trials examining AI-assisted colonoscopy have provided compelling evidence of technology’s diagnostic capabilities. In a comprehensive study involving 3,059 patients across six medical centres, researchers found that AI-assisted colonoscopy achieved a 39.9% adenoma detection rate compared to 32.4% with conventional methods (Xu et al., 2023). Similarly, researchers reported that AI systems improved adenoma detection rates from 40.5% to 50.2% across 1,158 patients (Spada et al., 2025).
These numbers paint an impressive picture of technological advancement. Kamba et al., 2021 demonstrated that AI assistance reduced adenoma miss rates from 36.7% to just 13.8%. On the surface, these results suggest AI is unquestionably beneficial for patient care.
However, these studies reveal something troubling beneath the statistics. The technology consistently performed better at detecting small, diminutive lesions that experienced doctors might overlook. While this sounds positive, it raises concerns about whether doctors are becoming overly dependent on AI to spot what they should be capable of identifying themselves.
The science behind skill deterioration concerns
The research demonstrates that AI systems excel at identifying specific types of lesions that require careful observation and pattern recognition. Spada et al., 2025 found that 78% of polyps detected by AI were 5mm or smaller, compared to 71.2% in standard procedures. These diminutive lesions demand acute visual skills and sustained attention from endoscopists.
The concern emerges from understanding how human expertise develops. Medical professionals hone their diagnostic abilities through thousands of procedures, gradually building pattern recognition capabilities. When AI systems consistently highlight areas of concern, Doctors may unconsciously rely on these prompts rather than developing their own observational skills.
Kamba et al.,(2021) provides particularly relevant insight into this phenomenon. Researchers found that both expert endoscopists (with over 5,000 procedures) and non-expert practitioners (fewer than 5,000 procedures) showed improved detection rates with AI assistance. While this demonstrates AI’s broad applicability, it also suggests that even experienced doctors are becoming dependent on technological assistance.
The technology works by processing video frames in real-time, placing green markers or blue tracking boxes around suspected lesions. This immediate visual feedback creates a form of cognitive offloading, where doctors may begin to wait for AI prompts rather than actively scanning for abnormalities themselves.
Addressing the skill atrophy debate
Critics argue that constant AI assistance could lead to a form of diagnostic muscle atrophy. The concern is that doctors might lose their edge in pattern recognition if they consistently rely on computer assistance. However, the research provides some reassurance about this fear.
Xu et al (2023) found that withdrawal times only increased slightly with AI assistance, from 7.78 minutes to 8.25 minutes. This modest increase suggests that doctors aren’t simply passively waiting for AI alerts but are still actively conducting thorough examinations.
Moreover, the technology didn’t increase detection of non-neoplastic lesions significantly. The Spada et al., 2025 reported no significant difference in hyperplastic polyp removal between AI-assisted and standard procedures. This finding suggests that doctors maintain their diagnostic discrimination abilities even when using AI assistance.
The research also shows that AI assistance didn’t compromise doctors’ ability to detect advanced adenomas or larger lesions. These findings indicate that fundamental diagnostic skills remain intact, with AI serving more as an enhancement tool rather than a replacement for clinical judgment.
Real-world implications of AI dependence in healthcare
Personal impact on medical decision-making
For patients undergoing colonoscopy procedures, the immediate implications appear overwhelmingly positive. The research shows that AI assistance increases the likelihood of detecting potentially problematic lesions by up to 24% (Spada et al., 2025). This improvement could translate to earlier intervention and better long-term health outcomes.
However, the dependency concern becomes relevant when considering healthcare continuity. If doctors become accustomed to AI assistance, their performance might suffer when technology isn’t available. Equipment malfunctions, software updates, or resource limitations could leave practitioners temporarily without their technological support systems.
Kamba et al., 2021 noted that AI systems occasionally experienced
“ irrecoverable malfunction,” requiring patient exclusion from procedures. While these instances were rare, they highlight the potential vulnerability of over-reliance on technology.
Patients might also develop expectations for AI-assisted procedures, potentially viewing standard examinations as inferior. This perception could create pressure on healthcare systems to implement AI across all facilities, regardless of cost-effectiveness or actual necessity.
Broader societal effects on healthcare
The widespread adoption of AI in colonoscopy represents a significant shift in medical practice patterns. Healthcare institutions are investing substantially in AI systems, with the expectation that improved detection rates will justify these costs through better patient outcomes and reduced liability.
The research indicates that AI assistance benefits both experienced and inexperienced practitioners. However, this finding raises questions about medical training and competency standards. If novice doctors can achieve similar results to experienced practitioners when using AI, healthcare systems might reduce emphasis on traditional skill development.
Kamba et al., 2021 involved 32 endoscopists across multiple facility types, from major medical centres to small clinics. This broad implementation suggests that AI assistance is becoming standard practice rather than a specialised tool. The normalisation of AI dependency could fundamentally alter how medical professionals approach diagnostic challenges.
Economic implications also emerge from these findings. While AI systems represent significant upfront investments, the improved detection rates might reduce long-term healthcare costs through earlier intervention. However, if practitioners lose diagnostic skills over time, healthcare systems might face increased costs for additional procedures or second opinions.
What this means going forward
The trajectory suggested by this research points toward increasing AI integration across medical specialties. The colonoscopy studies demonstrate proof-of-concept for AI assistance in diagnostic procedures, likely encouraging development of similar systems for other medical applications.
Within the next decade, medical training programmes may need to balance traditional skill development with AI literacy. Future doctors will need to maintain independent diagnostic capabilities while effectively utilising technological assistance. This dual competency requirement could extend training periods or modify curriculum structures.
The research suggests that AI assistance will become particularly valuable in addressing healthcare disparities. Less experienced practitioners in underserved areas could potentially provide higher-quality care with AI support, improving overall population health outcomes.
However, the dependency risk remains a long-term concern requiring active management. Healthcare systems will need to implement policies ensuring that practitioners maintain proficiency in unassisted procedures while maximising AI benefits.
References
Kamba, S., Tamai, N., Saitoh, I., Matsui, H., Horiuchi, H., Kobayashi, M., Sakamoto, T., Ego, M., Fukuda, A., Tonouchi, A., Shimahara, Y., Nishikawa, M., Nishino, H., Saito, Y., & Sumiyama, K. (2021). Reducing adenoma miss rate of colonoscopy assisted by artificial intelligence: A multicenter randomized controlled trial. Journal of Gastroenterology, 56, 746-757. https://doi.org/10.1007/s00535-021-01808-w
Spada, C., Salvi, D., Ferrari, C., Hassan, C., Barbaro, F., Belluardo, N., Minelli Grazioli, L., Milluzzo, S. M., Olivari, N., Papparella, L. G., Pecere, S., Pesatori, E. V., Petruzziello, L., Piccirelli, S., Quadarella, A., Cesaro, P., & Costamagna, G. (2025). A comprehensive RCT in screening, surveillance, and diagnostic AI-assisted colonoscopies (ACCENDO-Colo study). Digestive and Liver Disease. https://doi.org/10.1016/j.dld.2024.12.023
Xu, H., Tang, R. S. Y., Lam, T. Y. T., Zhao, G., Lau, J. Y. W., Liu, Y., Wu, Q., Rong, L., Xu, W., Li, X., Wong, S. H., Cai, S., Wang, J., Liu, G., Ma, T., Liang, X., Mak, J. W. Y., Xu, H., Yuan, P., Cao, T., Li, F., Ye, Z., Shutian, Z., & Sung, J. J. Y. (2023). Artificial intelligence–assisted colonoscopy for colorectal cancer screening: A multicenter randomized controlled trial. Clinical Gastroenterology and Hepatology, 21, 337-346. https://doi.org/10.1016/j.cgh.2022.07.006