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Artificial Intelligence (AI) holds promise in improving diagnostics and treatment. Likewise, AI is anticipated to mitigate the impacts of staff shortages in the healthcare sector. However, realising the expectations placed on AI requires a substantial effort involving patients and clinical domain experts.

Artificial intelligence in healthcare: why not apply the medico-legal method starting with the Collingridge dilemma?

Technology has greatly influenced and radically changed human life, from communication to creativity and from productivity to entertainment. The authors, starting from considerations concerning the implementation of new technologies with a strong impact on people's everyday lives, take up Collingridge's dilemma and relate it to the application of AI in healthcare. Collingridge's dilemma is an ethical and epistemological problem concerning the relationship between technology and society which involves two approaches.

Value assessment of artificial intelligence in medical imaging: a scoping review.

Background: Artificial intelligence (AI) is seen as one of the major disrupting forces in the future healthcare system. However, the assessment of the value of these new technologies is still unclear, and no agreed international health technology assessment-based guideline exists. This study provides an overview of the available literature in the value assessment of AI in the field of medical imaging.

Cognitive behavioral therapy with adaptive virtual reality exposure vs. cognitive behavioral therapy with exposure in the treatment of social anxiety disorder: A study protocol for a randomized controlled trial.

Background: Social anxiety disorder (SAD) has a high prevalence and an early onset with recovery taking decades to occur. Current evidence supports the efficacy of cognitive behavioral therapy (CBT) with virtual reality (VR) exposure. However, the evidence is based on a sparse number of studies with predominantly small sample sizes.

Model for ASsessing the value of Artificial Intelligence in medical imaging (MAS-AI).

Objectives: Artificial intelligence (AI) is seen as a major disrupting force in the future healthcare system. However, the assessment of the value of AI technologies is still unclear. Therefore, a multidisciplinary group of experts and patients developed a Model for ASsessing the value of AI (MAS-AI) in medical imaging.

A qualitative exploration of early assessment of innovative medical technologies.

Background: Hospitals increasingly make decisions about early development of and investment in innovative medical technologies (IMTs), but at present often without an early assessment of their potential to ensure selection of the most promising candidates for further development. This paper explores how early assessment is carried out in different health organisations and then discusses relevant learning points for hospitals.

Methods: A qualitative study design with a structured interview guide covering four themes was used.

Review of early assessment models of innovative medical technologies.

Introduction: Hospitals increasingly make decisions regarding the early development of and investment in technologies, but a formal evaluation model for assisting hospitals early on in assessing the potential of innovative medical technologies is lacking. This article provides an overview of models for early assessment in different health organisations and discusses which models hold most promise for hospital decision makers.

Methods: A scoping review of published studies between 1996 and 2015 was performed using nine databases.

Cost-effectiveness of telemonitoring of diabetic foot ulcer patients.

This study compared the cost-effectiveness of telemonitoring with standard monitoring for patients with diabetic foot ulcers. The economic evaluation was nested within a pragmatic randomised controlled trial. A total of 374 patients were randomised to either telemonitoring or standard monitoring.

[A guide to obtain validity and reproducibility in systematic reviews].

Validity and reproducibility are key concepts in the execution and reporting of the literature searches underlying a systematic review as it enables the reader to assess the quality of the performed searches. However, often the reporting of searches is lacking crucial information. This article provides guidelines for the process from development of a search protocol to quality assessment of the retrieved literature in order to obtain validity and reproducibility.