Machine Learning for Clinical Decision-Making: Challenges and Opportunities in Cardiovascular Imaging - Sanchez-Martinez, Sergio; Camara, Oscar; Piella, Gemma; Cikes, Maja; González-Ballester, Miguel Ángel; Miron, Marius; Vellido, Alfredo; Gómez, Emilia; Fraser, Alan G.; Bijnens, Bart (Frontiers in Cardiovascular Medicine, 2022)
The use of machine learning (ML) approaches to target clinical problems is called to revolutionize clinical decision-making in cardiology. The success of these tools is dependent on the understanding of the intrinsic processes being used during the conventional pathway by which clinicians make decisions. In a parallelism with this pathway, ML can have an impact at four levels: for data acquisition, predominantly by extracting standardized, high-quality information with the smallest possible learning curve; for feature extraction, by discharging healthcare practitioners from performing tedious measurements on raw data; for interpretation, by digesting complex, heterogeneous data in order to augment the understanding of the patient status; and for decision support, by leveraging the previous steps to predict clinical outcomes, response to treatment or to recommend a specific intervention. This paper discusses the state-of-the-art, as well as the current clinical status and challenges associated with the two later tasks of interpretation and decision support, together with the challenges related to the learning process, the auditability/traceability, the system infrastructure and the integration within clinical processes in cardiovascular imaging.