Machine intelligence in non-invasive endocrine cancer diagnostics

Artificial intelligence (AI) has illuminated a clear path towards an evolving health-care system replete with enhanced precision and computing capabilities. Medical imaging analysis can be strengthened by machine learning as the multidimensional data generated by imaging naturally lends itself to hierarchical classification. In this Review, we describe the role of machine intelligence in image-based endocrine cancer diagnostics. We first provide a brief overview of AI and consider its intuitive incorporation into the clinical workflow. We then discuss how AI can be applied for the characterization of adrenal, pancreatic, pituitary and thyroid masses in order to support clinicians in their diagnostic interpretations. This Review also puts forth a number of key evaluation criteria for machine learning in medicine that physicians can use in their appraisals of these algorithms. We identify mitigation strategies to address ongoing challenges around data availability and model interpretability in the context of endocrine cancer diagnosis. Finally, we delve into frontiers in systems integration for AI, discussing automated pipelines and evolving computing platforms that leverage distributed, decentralized and quantum techniques. This Review explains core concepts in artificial intelligence (AI) and machine learning for endocrinologists. AI applications in endocrine cancer diagnostics are highlighted as well as research challenges and future directions for the field. Key points Developments in machine intelligence have been made possible by the increase in data ubiquity and computing power and have the potential to enhance image segmentation, analysis and workflow in non-invasive endocrine cancer diagnostics. Improved adherence to consensus reporting standards and evaluation criteria in artificial intelligence (AI) for medical image analysis is urgently needed in the field of endocrine cancer diagnostics as this will enable meaningful cross-study comparison. A centralized inventory to track diagnostic algorithms in oncologic endocrinology that are in active clinical use would improve performance auditing and algorithm stewardship. The looming risk of excessive intervention in endocrine cancers can be addressed with the improved detection facilitated by AI, possibly via correlation with prognostic data for improved risk stratification. Poor data availability continues to stymie the development of robust machine learning applications, particularly in rare endocrine