Machine learning in medicine—focus on radiology

Abstract

Artificial intelligence (AI) is increasingly being used in a variety of health care applications, including drug development, remote patient monitoring, medical diagnostics and imaging, wearables, virtual assistants, and hospital administration. Many areas that rely on large data are also predicted to gain from the deployment of AI. Medical fields that rely on imaging data, such as radiology, pathology, dermatology, and ophthalmology, have already reaped the benefits of AI applications. Radiologists are trained to visually examine medical imaging and report results to detect, define, and monitor various diseases. Such evaluations are frequently based on education, training, and experience. Over time, radiologists are trained to a diagnose complex constellation of findings with medical insights and reasoning, although as with any human-centric tasks, these may be prone to subjectivity. Emerging now are various AI radiological applications, although these have been mainly targeting certain specific diagnostic tasks. In comparison to human-centric qualitative reasoning, the strength of AI may be to tackle repetitive but straightforward tasks or deliver an automated quantitative extraction of imaging features longitudinally. When AI is integrated into the clinical process as a tool to aid clinicians, more accurate and repeatable radiological assessments may be performed. This section will review the application of AI technologies in the radiology imaging field.

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