## 6 Further Risks and Limitations from Marginalising Socio-Technical Factors in CRC  
In this section, the paper highlights how even if technical and socio-technical concerns are addressed through robust evaluation standards in the interest of patient safety, AI system development and investment should be directed towards problems that have the most measurable impact on patient morbidity and mortality outcomes. Much of the AI optimism in CRC screening has been driven by the fact that it presents a potential solution to the problem that in a routine screening colonoscopy, between 17-28% of colorectal polyps (adenomas) are missed, which is concerning given that for every 1% increase in a clinician's adenoma detection rate (ADR), there is a 3% decrease in the risk of interval cancer [115; 116; 117]. Several of the vendor-backed AI augmented diagnostic systems have proposed to address this issue by providing clinicians with a real-time AI polyp detection system, and the evidence provided by recent RCTs is promising. It suggests that the ADR increases by up to 50% with the inclusion of an AI detection system, with the most pronounced effect on trainee gastroenterologists [7; 8; 9]. Similarly, in AI-enhanced capsule endoscopy (CE), experimental evidence suggests that AI augmentation consistently outperforms a conventional CE reader in terms of both accuracy and time, showing a 99.88% vs. 74.57% sensitivity in the per-patient detection of abnormalities and, significantly, a 5.9 vs. 96.6-min recording on per-patient reading time between the AI vs. human respectively [10].  
While such results are exciting and show some promise in improving patient outcomes, it is also important to recognise that one of the biggest influences on CRC-related mortality is not fundamentally due to the nature of the current technology, but rather due to the prevalence of low rates of participation in CRC screening [118]. Various studies from Australia, which implemented one of the first national approaches to bowel cancer screening, have suggested that at the current participation rate of ~40%, a 15-36% reduction in CRC-related mortality can be expected, and if participation rates in the screening population were to increase to 70%, a 59% reduction in CRC-related mortality would be observed [119; 120; 121; 122; 123; 124]. The problem is that several high-income nations have failed to reach their desired target of 65-80% screening coverage, even in the presence of wide-scale public health campaigns to raise awareness about its importance [81; 125]. Unfortunately, it seems that these rates are not likely to increase, given that the rate of FOBT-based screening coverage has plateaued in Australia over the last five years [126]. Participation in follow-up colonoscopy, where many of the AI-enhanced methods are poised to transform outcomes are equally discouraging. Studies from Europe, the United States, Canada, and Australia show that even in the presence of a positive FOBT, only between 50-70% of patients proceed for a diagnostic examination via colonoscopy [126; 127; 128; 129]. Participation among the most marginalised groups that have historically experienced social disadvantage, such as those from Indigenous populations, low socio-economic status, cultural and ethnic diversity, or disability, tends to be the lowest in either stage of the screening process [69; 130; 131].  
Consequently, it is possible that the extensive focus of AI adoption and integration into the CRC diagnostic pathway may not have the drastic impact it has promised on patient outcomes. In the following sections, we highlight how the barriers to participation in CRC screening is permeated by human factors, and that if we are sensitive to these factors, we can capitalise on AI methods in a way that can lead to a more significant impact to patient outcomes by developing technology that increases the uptake of screening coverage in high-risk population groups.