## 4 AI in CRC: Limitations and Risks of Algorithmic Bias in Clinical Decision Making
### CRC, AI and Models
#### 4.2.1 Temporal Context  
An issue that has been widely discussed in the literature is the problem of model adaptation in the presence of distributional data shift [89], where there is (1) a mismatch between the training data used by an algorithm at one point in time, and unanticipated and/or evolving patient/healthcare contexts that emerge at a later point in time; and (2) an inability of an AI algorithm to accurately adapt to such non-stationary clinical operational environments due to the way that an AI model frames its assumptions of the world [55]. This tends to manifest most when historical EHRs are used to train a ML algorithm and new data are later recorded and captured in the EHR, which was absent in the historical data due to the evolution of clinical practice. This leads to a problem known as model drift [90, 84, 91]. This is not an easy problem to solve, since historical data cannot suddenly be updated according to new knowledge, and new knowledge is often not voluminous enough to train a new algorithm efficaciously. Even though more contemporary methods allow for continuous learning via a process known as incremental learning [52], it is important to recognise that this process of adaptation is still bound within the initial constraints of the problem definition and an AI algorithm cannot epistemologically adapt outside of the computational model \"frame\" imposed by the engineer. Importantly, the ML community has not agreed on the best approach to handling class imbalances that emerge during incremental learning, since real-time learning may inadvertently undo any efforts used to balance initial data distributions and may skew the algorithm back too heavily in favour of diseases or population groups that present commonly. To show how these issues manifest first-hand in a healthcare setting, [PERSON] et al. [92] revealed how decreasing rates of acute kidney injury (AKI) led ML models to drift towards a state of over-prediction of AKI within one year of development and this had the negative unintended consequence of altering clinical decision making in a way that misdirected resource allocation and expenditure. A more extreme example of this problem can be seen in the emergence of the recent COVID-19 pandemic, which witnessed an unprecedented shift in the patient landscape of a typical emergency department (ED), where an exponential increase in ED visits for COVID-19 matched an exponential decline in acute visits for stroke and heart attacks [91]. This presents a potential issue for CRC, given that since the early 1990s, the incidence of colorectal cancer in patients below the age of 50 has nearly doubled, but this population is often not captured by most screening programs [93, 94]. While it is true that the absolute number of these patients are currently small and are not necessarily included in screening pathways, it does demonstrate how disease patterns evolve over time, and this has consequences to how AI systems are developed and evaluated. Observations in younger CRC patients who present with more advanced stages of CRC have suggested that there are multifactorial genetic and environmental components that influence the nature of the underlying disease [93]. How does this then influence prognostic model performances, e.g., if the underlying biological mechanisms are discovered to have shifted over time? Therefore, it is important to consider that we need more robust guidelines around how and when algorithms should be re-trained or re-calibrated, to maintain their performance across shifting distributions to ensure that clinical decision making, which is inevitably influenced by algorithmic decisions, remains robust to evolving clinical knowledge and dynamic clinical settings. The ramifications of temporal context on algorithmic predictability can be quite elusive, particularly when we also consider how AI integration into clinical decision making along therapeutic pathways is quite sensitive to the specific circumstances of the patient's own temporal context, independent of the broader population.