One of the most promising areas of health innovation is the application of artificial intelligence (AI), primarily in medical imaging. This article provides basic definitions of terms such as “machine/deep learning” and analyses the integration of AI into radiology. Publications on AI have drastically increased from about 100–150 per year in 2007–2008 to 700–800 per year in 2016–2017. Magnetic resonance imaging and computed tomography collectively account for more than 50% of current articles. Neuroradiology appears in about one-third of the papers, followed by musculoskeletal, cardiovascular, breast, urogenital, lung/thorax, and abdomen, each representing 6–9% of articles. With an irreversible increase in the amount of data and the possibility to use AI to identify findings either detectable or not by the human eye, radiology is now moving from a subjective perceptual skill to a more objective science. Radiologists, who were on the forefront of the digital era in medicine, can guide the introduction of AI into healthcare. Yet, they will not be replaced because radiology includes communication of diagnosis, consideration of patient’s values and preferences, medical judgment, quality assurance, education, policy-making, and interventional procedures. The higher efficiency provided by AI will allow radiologists to perform more value-added tasks, becoming more visible to patients and playing a vital role in multidisciplinary clinical teams.

In implant dentistry, three-dimensional (3D) imaging can be realised by dental cone beam computed tomography (CBCT), offering volumetric data on jaw bones and teeth with relatively low radiation doses and costs. The latter may explain why the market has been steadily growing since the first dental CBCT system appeared two decades ago. More than 85 different CBCT devices are currently available and this exponential growth has created a gap between scientific evidence and existing CBCT machines. Indeed, research for one CBCT machine cannot be automatically applied to other systems. Supported by a narrative review, recommendations for justified and optimized CBCT imaging in oral implant dentistry are provided. The huge range in dose and diagnostic image quality requires further optimization and justification prior to clinical use. Yet, indications in implant dentistry may go beyond diagnostics. In fact, the inherent 3D datasets may further allow surgical planning and transfer to surgery via 3D printing or navigation. Nonetheless, effective radiation doses of distinct dental CBCT machines and protocols may largely vary with equivalent doses ranging between 2 to 200 panoramic radiographs, even for similar indications. Likewise, such variation is also noticed for diagnostic image quality, which reveals a massive variability amongst CBCT technologies and exposure protocols. For anatomical model making, the so-called segmentation accuracy may reach up to 200 μm, but considering wide variations in machine performance, larger inaccuracies may apply. This also holds true for linear measures, with accuracies of 200 μm being feasible, while sometimes fivefold inaccuracy levels may be reached. Diagnostic image quality may also be dramatically hampered by patient factors, such as motion and metal artefacts. Apart from radiodiagnostic possibilities, CBCT may offer a huge therapeutic potential, related to surgical guides and further prosthetic rehabilitation. Those additional opportunities may surely clarify part of the success of using CBCT for presurgical implant planning and its transfer to surgery and prosthetic solutions. Hence, dental CBCT could be justified for presurgical diagnosis, preoperative planning and peroperative transfer for oral implant rehabilitation, whilst striving for optimisation of CBCT based machine-dependent, patient-specific and indication-oriented variables.

Worldwide interest in artificial intelligence (AI) applications is growing rapidly. In medicine, devices based on machine/deep learning have proliferated, especially for image analysis, presaging new significant challenges for the utility of AI in healthcare. This inevitably raises numerous legal and ethical questions. In this paper we analyse the state of AI regulation in the context of medical device development, and strategies to make AI applications safe and useful in the future. We analyse the legal framework regulating medical devices and data protection in Europe and in the United States, assessing developments that are currently taking place. The European Union (EU) is reforming these fields with new legislation (General Data Protection Regulation [GDPR], Cybersecurity Directive, Medical Devices Regulation, In Vitro Diagnostic Medical Device Regulation). This reform is gradual, but it has now made its first impact, with the GDPR and the Cybersecurity Directive having taken effect in May, 2018. As regards the United States (U.S.), the regulatory scene is predominantly controlled by the Food and Drug Administration. This paper considers issues of accountability, both legal and ethical. The processes of medical device decision-making are largely unpredictable, therefore holding the creators accountable for it clearly raises concerns. There is a lot that can be done in order to regulate AI applications. If this is done properly and timely, the potentiality of AI based technology, in radiology as well as in other fields, will be invaluable. • AI applications are medical devices supporting detection/diagnosis, work-flow, cost-effectiveness. • Regulations for safety, privacy protection, and ethical use of sensitive information are needed. • EU and U.S. have different approaches for approving and regulating new medical devices. • EU laws consider cyberattacks, incidents (notification and minimisation), and service continuity. • U.S. laws ask for opt-in data processing and use as well as for clear consumer consent.

Epicardial adipose tissue (EAT) is a metabolically activated beige adipose tissue, non-homogeneously surrounding the myocardium. Physiologically, EAT regulates toxic fatty acids, protects the coronary arteries against mechanical strain, regulates proinflammatory cytokines, stimulates the production of nitric oxide, reduces oxidative stress, and works as a thermogenic source against hypothermia. Conversely, EAT has pathologic paracrine interactions with the surrounded vessels, and might favour the onset of atrial fibrillation. In addition, initial atherosclerotic lesions can promote inflammation and trigger the EAT production of cytokines increasing vascular inflammation, which, in turn, may help the development of collateral vessels but also of self-stimulating, dysregulated inflammatory process, increasing coronary artery disease severity. Variations in EAT were also linked to metabolic syndrome. Echocardiography first estimated EAT measuring its thickness on the free wall of the right ventricle but does not allow accurate volumetric EAT estimates. Cardiac CT (CCT) and cardiac MR (CMR) allow for three-dimensional EAT estimates, the former showing higher spatial resolution and reproducibility but being limited by radiation exposure and long segmentation times, the latter being radiation-free but limited by lower spatial resolution and reproducibility, higher cost, and difficulties for obese patients. EAT radiodensity at CCT could to be related to underlying metabolic processes. The correlation between EAT and response to certain pharmacological therapies has also been investigated, showing promising results. In the future, semi-automatic or fully automatic techniques, machine/deep-learning methods, if validated, will facilitate research for various EAT measures and may find a place in CCT/CMR reporting.