Health technology assessment framework for artificial intelligence-based technologies.

Objectives: Artificial intelligence (AI)-based health technologies (AIHTs) have already been applied in clinical practice. However, there is currently no standardized framework for evaluating them based on the principles of health technology assessment (HTA).

Methods: A two-round Delphi survey was distributed to a panel of experts to determine the significance of incorporating topics outlined in the EUnetHTA Core Model and twenty additional ones identified through literature reviews.

Advancing EEG prediction with deep learning and uncertainty estimation.

Deep Learning (DL) has the potential to enhance patient outcomes in healthcare by implementing proficient systems for disease detection and diagnosis. However, the complexity and lack of interpretability impede their widespread adoption in critical high-stakes predictions in healthcare. Incorporating uncertainty estimations in DL systems can increase trustworthiness, providing valuable insights into the model's confidence and improving the explanation of predictions.

Artificial intelligence in neurology: opportunities, challenges, and policy implications.

Neurological conditions are the leading cause of disability and mortality combined, demanding innovative, scalable, and sustainable solutions. Brain health has become a global priority with adoption of the World Health Organization's Intersectoral Global Action Plan in 2022. Simultaneously, rapid advancements in artificial intelligence (AI) are revolutionizing neurological research and practice.

Intelligent digital tools for screening of brain connectivity and dementia risk estimation in people affected by mild cognitive impairment: the AI-Mind clinical study protocol.

More than 10 million Europeans show signs of mild cognitive impairment (MCI), a transitional stage between normal brain aging and dementia stage memory disorder. The path MCI takes can be divergent; while some maintain stability or even revert to cognitive norms, alarmingly, up to half of the cases progress to dementia within 5 years. Current diagnostic practice lacks the necessary screening tools to identify those at risk of progression.

Reliable evaluation of functional connectivity and graph theory measures in source-level EEG: How many electrodes are enough?

Objective: Using EEG to characterise functional brain networks through graph theory has gained significant interest in clinical and basic research. However, the minimal requirements for reliable measures remain largely unaddressed. Here, we examined functional connectivity estimates and graph theory metrics obtained from EEG with varying electrode densities.