Translating ethical and quality principles for the effective, safe and fair development, deployment and use of artificial intelligence technologies in healthcare.

OBJECTIVE: The complexity and rapid pace of development of algorithmic technologies pose challenges for their regulation and oversight in healthcare settings. We sought to improve our institution's approach to evaluation and governance of algorithmic technologies used in clinical care and operations by creating an Implementation Guide that standardizes evaluation criteria so that local oversight is performed in an objective fashion. MATERIALS AND METHODS: Building on a framework that applies key ethical and quality principles (clinical value and safety, fairness and equity, usability and adoption, transparency and accountability, and regulatory compliance), we created concrete guidelines for evaluating algorithmic technologies at our institution. RESULTS: An Implementation Guide articulates evaluation criteria used during review of algorithmic technologies and details what evidence supports the implementation of ethical and quality principles for trustworthy health AI. Application of the processes described in the Implementation Guide can lead to algorithms that are safer as well as more effective, fair, and equitable upon implementation, as illustrated through 4 examples of technologies at different phases of the algorithmic lifecycle that underwent evaluation at our academic medical center. DISCUSSION: By providing clear descriptions/definitions of evaluation criteria and embedding them within standardized processes, we streamlined oversight processes and educated communities using and developing algorithmic technologies within our institution. CONCLUSIONS: We developed a scalable, adaptable framework for translating principles into evaluation criteria and specific requirements that support trustworthy implementation of algorithmic technologies in patient care and healthcare operations.

Novel cancer therapies and their association with diabetes.

Over the last decade, there has been a shift in the focus of cancer therapy from conventional cytotoxic drugs to therapies more specifically directed to cancer cells. These novel therapies include immunotherapy, targeted therapy and precision medicine, each developed in great part with a goal of limiting collateral destruction of normal tissues, while enhancing tumor destruction. Although this approach is sound in theory, even new, specific therapies have some undesirable, 'off target effects', in great part due to molecular pathways shared by neoplastic and normal cells. One such undesirable effect is hyperglycemia, which results from either the loss of immune tolerance and autoimmune destruction of pancreatic ß-cells or dysregulation of the insulin signaling pathway resulting in insulin resistance. These distinct pathogenic mechanisms lead to clinical presentations similar to type 1 (T1DM) and type 2 (T2DM) diabetes mellitus. Both types of diabetes have been reported in patients across clinical trials, and data on the mechanism(s) for developing hyperglycemia, prevalence, prognosis and effect on cancer mortality is still emerging. With the rapidly expanding list of clinical indications for new cancer therapies, it is essential to understand the impact of their adverse effects. In this review, we focus on hyperglycemia and diabetes related to cancer therapies, describe what is known about mechanism(s) leading to dysregulated glucose metabolism and provide a guide to management of complex oncology patients with a new diagnosis of diabetes.