Suchergebnisse

The application of artificial intelligence (AI) may revolutionize the healthcare system, leading to enhance efficiency by automatizing routine tasks and decreasing health-related costs, broadening access to healthcare delivery, targeting more precisely patient needs, and assisting clinicians in their decision-making. For these benefits to materialize, governments and health authorities must regulate AI, and conduct appropriate health technology assessment (HTA). Many authors have highlighted that AI health technologies (AIHT) challenge traditional evaluation and regulatory processes. To inform and support HTA organizations and regulators in adapting their processes to AIHTs, we conducted a systematic review of the literature on the challenges posed by AIHTs in HTA and health regulation. Our research question was: What makes artificial intelligence exceptional in HTA? The current body of literature appears to portray AIHTs as being exceptional to HTA. This exceptionalism is expressed along 5 dimensions: 1) AIHT's distinctive features; 2) their systemic impacts on health care and the health sector; 3) the increased expectations towards AI in health; 4) the new ethical, social and legal challenges that arise from deploying AI in the health sector; and 5) the new evaluative constraints that AI poses to HTA. Thus, AIHTs are perceived as exceptional because of their technological characteristics and potential impacts on society at large. As AI implementation by governments and health organizations carries risks of generating new, and amplifying existing, challenges, there are strong arguments for taking into consideration the exceptional aspects of AIHTs, especially as their impacts on the healthcare system will be far greater than that of drugs and medical devices. As AIHTs begin to be increasingly introduced into the health care sector, there is a window of opportunity for HTA agencies and scholars to consider AIHTs' exceptionalism and to work towards only deploying clinically, economically, socially acceptable ...

Since the beginning of the COVID-19 pandemic, numerous studies have been conducted to identify interventions that could contribute to alleviating the burden it has caused. The Institut national d'excellence en santé et en services sociaux (INESSS) has played a key role in informing the government of Québec regarding the evaluation of specific pandemic-related interventions. This process took place in a context characterized by a sense of urgency to assess and recommend potential interventions that could save lives and reduce the effects of the disease on populations and healthcare systems, which increased the pressure on the regulatory agencies leading these evaluations. While some of the interventions examined were considered promising, results from COVID-19 studies often led to uncertainty regarding their efficacy or safety. Regulatory agencies evaluating the value of promising interventions thus face challenges in deciding whether these should be made available to the population, particularly when assessing their benefit-risk balance. To shed light on these challenges, we identified underlying ethical considerations that can influence such an assessment. A rapid literature review was conducted in February 2021, to identify the main challenges associated with the benefit-risk balance assessment of promising interventions. To reinforce our understanding of the underlying ethical considerations, we initiated a discussion among various social actors involved in critical thinking surrounding the evaluation of promising interventions, including ethicists, clinicians and researchers involved in clinical or public health practice, as well as patients and citizens. This discussion allowed us to create a space for exchange and mutual understanding among these various actors who contributed equally to the identification of ethical considerations. The knowledge and perspectives stemming from the scientific literature and those consulted were integrated in a common reflection on these ethical considerations. This allowed ...