For the purpose of this narrative review, we searched PubMed and MEDLINE databases with no date restriction using search terms related to AI and medicine and cardiology subspecialties. Articles were reviewed and selected for inclusion on the basis of relevance. This article highlights that the role of ML in cardiovascular medicine is rapidly emerging, and mounting evidence indicates it will power the new tools that drive the field. Among other uses, AI has been deployed to interpret echocardiograms, to automatically identify heart rhythms from an ECG, to uniquely identify an individual using the ECG as a biometric signal, and to detect the presence of heart disease such as left ventricular dysfunction from the surface ECG.6x6Attia, Z.I., Kapa, S., Lopez-Jimenez, F. et al.

Artificial Intelligence (AI) has emerged as one of the most important topics in radiology today. The Association of University Radiologists (AUR9) in its role of organizing and representing the interests of academic radiologists and those of radiology at large, convened a roundtable to help radiologists and industry leaders share their points of view and their goals in order to foster a shared understanding about the impact and benefits of AI applications in the field of radiology. There is a clear mutual interdependence between the radiology community and industry partners, which, in the case of AI, should foster collaboration between the two groups. In order to advance radiological sciences and to bridge the gap between clinicians and engineers, members of both groups need to work together so as to ensure the development of common goals, shared understanding, and mutually productive efforts. This type of collaboration occurs most frequently at the local level between a single radiology academic department and a single manufacturer.

Deep learning, a recently described AI machine learning technique, when applied to image analysis, allows the algorithm to analyze data using multiple processing layers to extract different image features,1x1LeCun, Y., Bengio, Y., and Hinton, G. Deep learning. In ophthalmology, many groups have reported exceptional diagnostic performance using deep learning algorithms to detect various ocular conditions based on anterior segment topography (e.g., keratoconus),5x5Hwang, E.S., Perez-Straziota, C.E., Kim, S.W. et al. Distinguishing highly asymmetric keratoconus eyes using combined Scheimpflug and spectral-domain OCT analysis. Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Efficacy of a deep learning system for detecting glaucomatous optic neuropathy based on color fundus photographs.

With the advancement of artificial intelligence in medical diagnostics, many reader studies have been carried out to determine whether man or machine is the better diagnostician [1x[1]Esteva, A. et al. Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. In these past studies, in particular in the field of skin cancer detection, man and machine were always regarded as competitors. However, this setting does not reflect clinical reality, where team-based diagnoses are considered to be more accurate than individual diagnoses [5x[5]Barnett, M.L. et al. Collective intelligence meets medical decision-making: the collective outperforms the best radiologist.

Skin cancer is the most common malignancy in fair-skinned populations, and melanoma accounts for the majority of skin cancer–related deaths worldwide [1x[1]Schadendorf, D., van Akkooi, A.C., Berking, C., Griewank, K.G., Gutzmer, R., Hauschild, A. et al. Pattern analysis, not simplified algorithms, is the most reliable method for teaching dermoscopy for melanoma diagnosis to residents in dermatology. The CNN deconstructed digital images of skin lesions and generated its own diagnostic criteria for melanoma detection during training. Several follow-up publications by other authors have demonstrated dermatologist-level skin cancer classification by using deep neural networks (CNN) [4x[4]Marchetti, M.A., Codella, N.C., Dusza, S.W., Gutman, D.A., Helba, B., Kalloo, A. et al. Results of the 2016 international skin imaging collaboration international symposium on biomedical imaging challenge: comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images. A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task.

Artificial intelligence (AI) has the potential to revolutionize disease diagnosis and management by performing classification difficult for human experts and by rapidly reviewing immense amounts of images. Despite its potential, clinical interpretability and feasible preparation of AI remains challenging. The traditional algorithmic approach to image analysis for classification previously relied on (1) handcrafted object segmentation, followed by (2) identification of each segmented object using statistical classifiers or shallow neural computational machine-learning classifiers designed specifically for each class of objects, and finally (3) classification of the image (Goldbaum et al., 1996xSee all ReferencesGoldbaum et al., 1996). Creating and refining multiple classifiers required many skilled people and much time and was computationally expensive (Chaudhuri et al., 1989xDetection of blood vessels in retinal images using two-dimensional matched filters. The development of convolutional neural network layers has allowed for significant gains in the ability to classify images and detect objects in a picture (Krizhevsky et al., 2017xImageNet classification with deep convolutional neural networks. These are multiple processing layers to which image analysis filters, or convolutions, are applied.