Medical imaging informatics is a rapidly growing field that combines the principles of medical imaging and informatics to improve the acquisition, management, and interpretation of medical images. This chapter introduces the basic concepts of medical imaging informatics, including image processing, feature engineering, and machine learning. It also discusses the recent advancements in computer vision and deep learning technologies and how they are used to develop new quantitative image markers and prediction models for disease detection, diagnosis, and prognosis prediction. By covering the basic knowledge of medical imaging informatics, this chapter provides a foundation for understanding the role of informatics in medicine and its potential impact on patient care.

Medical Imaging Informatics and Artificial Intelligence at UCSF is headed by Dr. Dugyu Tosun-Torgut and brings together world-class researchers from multiple disciplines in order to find new, innovative ways to use artificial intelligence and imaging for medical diagnosis. By uniting neurologists, engineers, and data scientists Medical Imaging Informatics and Artificial Intelligence will be extremely impactful in increasing the scope of our current imaging systems when it comes to the brain. The Medical Imaging Informatics and Artificial Intelligence Lab at UCSF aims to foster a truly collaborative environment. All team members are expected to contribute and participate in meaningful ways as we seek to discover novel new ways to utilize technology to better diagnose and treat patients. We value long term partnerships and create a trusting environment for all to succeed.

The idea that AI will replace radiologists comes from the fact that today's AI models models are very good at pattern recognition. The interesting thing in radiology are the NLP models mining radiology reports,says Woojin Kim, Chief Medical Information Officer at Nuance, former Chief of Radiography Modality, Director of Center for Translational Imaging Informatics, Associate Director of Imaging Informatics, and Assistant Professor of Radiology at the Hospital of the University of Pennsylvania.

How do clinicians set rules that allow professionals "to make good use of technology to find patterns in complex data" but also "stop companies from extracting unethical value from those data?" Geis, from the American College of Radiology (ACR) Data Science Institute, is one of the authors of a joint statement that addresses the potential for the unethical use of data, the bias inherent in datasets, and the limits of algorithmic learning, and was the moderator of a session on the topic at the Radiological Society of North America (RSNA) 2019 Annual Meeting in Chicago. There's a very big grey area between an absolute ethical approach to data use and decisions that are profit-driven, he told Medscape Medical News. "Sitting on the sainthood side, I can stick to doing only what I see as good for my patients, maybe even taking vows of poverty," he said. "On the extreme other side, I'm doing things that put me in prison."

This is a condensed summary of an international multisociety statement on ethics of artificial intelligence (AI) in radiology produced by the ACR, European Society of Radiology, RSNA, Society for Imaging Informatics in Medicine, European Society of Medical Imaging Informatics, Canadian Association of Radiologists, and American Association of Physicists in Medicine. AI has great potential to increase efficiency and accuracy throughout radiology, but it also carries inherent pitfalls and biases. Widespread use of AI-based intelligent and autonomous systems in radiology can increase the risk of systemic errors with high consequence and highlights complex ethical and societal issues. Currently, there is little experience using AI for patient care in diverse clinical settings. Extensive research is needed to understand how to best deploy AI in clinical practice.

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Earlier this week, a consensus draft document dealing with the ethics of AI in medical imaging was posted on the ACR website. I would like to congratulate the authors, listed with their affiliations below, on a collaborative effort to address this important topic. This was a multi-society effort including the American College of Radiology (ACR), American Association of Physicists in Medicine (AAPM), Canadian Association of Radiologists (CAR), European Society of Radiology (ESR), Radiological Society of North American (RSNA), Society for Imaging Informatics in Medicine (SIIM) and European Society of Medical Imaging Informatics (EuSoMII). Importantly, the group included trainees, patients and other stakeholders such as an ethicist from MIT. But despite the wide ranging backgrounds and expert input that created this draft, the writing group and our Societies' leaders are very clear that this is just that: a draft.