## 3 AI in Health: Opportunities and Concerns for Clinical Decision Making  
AI in health is a rapidly developing field that has recently demonstrated a remarkable capacity to learn interrelationships within data in a way that offers utility for clinical decision making [19; 20; 21; 22]. Most of the recent AI optimism for healthcare has been driven by the phenomenal development and success of deep learning (DL) [21; 22; 23]. In this section, we briefly review the different supervised, unsupervised, and reinforcement DL approaches [24; 25] that have been used across the screening, diagnostic, therapeutic, and prognostic pathways of clinical decision making. In navigating the application of thesemethods in medical practice, we introduce some of the problematic assumptions that are embedded in AI system development. Some of the problems are general in nature and implicit to the process of AI system development (e.g., the biases embedded in data used to train AI systems), while others are specific to certain AI algorithmic methodologies that become more visible in the healthcare space of CRC. The purpose of this section is not to undertake an extensive review of AI methodologies, but to highlight that there are inherent methodological considerations that underpin their development that impact on clinical decision making. This indicates that a more nuanced approach to examining AI in CRC is necessary if we are to optimise AI benefits and limit its harms in clinical practice. The medical literature is ripe with examples that demonstrate the highly efficacious modelling capacity of supervised ML across myriad medical modalities. These have included predicting: (a) benign/malignant cancerous states from pixel data found in photographs of skin lesions [26]; (b) classifications of disease states in chest radiographs (e.g., normal vs. abnormal radiographs, presence of pneumonia, presence of malignant pulmonary nodules) [27; 28; 29]; (c) risk stratification and prognosis from whole slide images (WSI) of histopathological tissue specimens that are used to inform therapeutic pathways in areas such as gastroenterology [12; 13; 30]; (d) arrythmia, atrial fibrillation, or coronary heart disease from wave data in electrocardiograms [31; 32; 33]; (e) the likelihood of sepsis based on clinical observation notes and test results found in EHRs [34]; (f) the presence or future onset of neurological diseases, such as brain tumours or Alzheimer's disease, from CT, MRI, or positron emission tomography [35; 36; 37]; (g) cardiovascular risk from fundus photography [38]; and (h) colonic polyps in colonoscopy [39], among innumerable other examples [40; 41]. Despite the success of supervised ML across myriad medical contexts, researchers have started to identify that there are complex nuances that underpin (a) AI and data, and (b) AI and models, that pose significant challenges to supervised learning systems when modelling the heterogeneity of the real-world, due to its dependency on large volumes of labelled data and narrow task definitions framed by the human observer. One of these issues includes representational harms introduced through data collection and/or labelling practices [42; 43]. For example, in 2019, a landmark study was published in _Science_, where [PERSON] and colleagues [44] revealed how systemic racial biases emerged after auditing a proprietary ML algorithm used routinely on 200 million people in the United States of America each year that was tasked with assigning risk scores on patients that would be eligible for subsided \"high-risk care management\". While the algorithm was well intentioned, the elusive nature of systemic racial biases meant that the AI was unable to recognise that generational inequalities in healthcare access between two sociodemographic groups resulted in a situation where less money was spent caring for less-healthy Black patients compared to more-healthy White patients. Therefore, at a given risk score, Black patients were considerably more ill than White patients.