Dr Luke Oakden-Rayner co-wrote the chapter Artificial Intelligence in Medicine: Validation and Study Design and Dr Johan Verjans co-wrote the chapter Cardiovascular Diseases. The book, Artificial Intelligence in Medical Imaging, looks at the application and evolution of AI within healthcare and radiology.

The medical industry is one of the most prominent industries where artificial intelligence (AI) can play a prominent role, especially when it comes to medical imaging.

Radiologists, who are on the forefront of the digital era in medicine, can help guide the introduction of AI into healthcare, and will see a significant impact on their profession. There are numerous papers that prove the potential and limitations of the use of AI/Machine Learning in the field, and this number is rapidly growing. This is one of the first books in AI in Medical Imaging and outlines the state-of-the-art in machine learning for medical imaging, including applications, challenges and ethics.

 
Book cover, Artifical Intelligence in Medical Imaging

From the introduction chapter:

“Due to continuing technological advances in medical image acquisition, novel imaging modalities are being introduced in medical practices, such as multi-slice (volumetric) and multienergy CT, multi-parametric and multi-frame (dynamic) MRI, multi-dimensional (3D+time) US, multi-planar interventional imaging, or multi-modal (hybrid) PET/CT and PET/MRI imaging technologies. The analysis of the large amounts of imaging data created by these modalities has become a tremendous challenge and a real bottleneck for diagnosis, therapy planning and follow-up, and biomedical research. At the same time, the general adoption of digital picture archiving and communication systems (PACS) in radiology, and their integration within the overall hospital information system, makes that large databases of medical images and associated relevant medical information of patients (including demographics, clinical findings, blood tests, pathology, genomics, proteomics) are being built up. It is to be expected that such databases will become more and more accessible for research purposes, provided that technical challenges and privacy issues can be properly dealt with. The availability of welldocumented medical imaging “big data” offers new opportunities for groupwise analyses within specific subject groups, such as characterization of normal and abnormal variation between subjects and detection of individual patient anomalies (computer-aided diagnosis), discovery of early markers of disease onset and progression (imaging biomarkers), optimal therapy selection and prediction of therapy outcome (radiomics in radiotherapy), and correlation of genotype and phenotype related findings (imaging genetics). In order to optimally exploit all available imaging data and to support the effective use of “big data” involving medical images in the context of personalized medicine, reliable computer-aided image analysis becomes indispensable to extract and quantify the relevant information from the imaging data, to fuse complementary information and to support the interpretation thereof.”

You can download a free copy from Springer Link.