Artificial Intelligence Medical Compendium

BACKGROUND: Personality traits are central to understanding human behavior and life outcomes, yet their assessment remains challenging due to biases and limited expressiveness in traditional self-report inventories. The emergence of large language mo... read more 

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Missed genetic deletions can prevent cancer patients from receiving effective targeted therapy. Standard pipelines, such as TSO500, use bin count averaging for copy number variation detection but often overlook focal events. To address this, we devel... read more 

BACKGROUND: Artificial intelligence (AI) is being introduced into every stage of the health economics and outcomes research (HEOR) production line, from literature screening to causal estimation. The field lacks a defensible count of what has been do... read more 

Challenging macrotrends in recent decades have characterised population mental health across the globe, with apparent deterioration in adolescent mental health, as well as underfunded, inequitable, and inefficient psychiatric care systems [1]. Unnece... read more 

Recent advances in nuclear medicine are reshaping the evaluation of cardiac amyloidosis, as highlighted by the recent collection published in EJNMMI (collection link: https://link.springer.com/collections/igfdhcddjg). This commentary summarizes key i... read more 

Complex forensic DNA mixtures remain challenging to interpret, especially when contributor proportions are highly imbalanced or when the mixture involves the same cell type. Single-cell isolation strategies have provided a novel methodological approa... read more 

BACKGROUND: The growing application of artificial intelligence in dentistry has opened new opportunities for predicting peri-implantitis (PI) using convolutional neural networks (CNNs). Transfer learning, where knowledge from a model pre-trained on a... read more 

Nanowire networks have emerged as a promising platform for physical reservoir computing. However, networks based on memristive switching exhibit inherent limitations in dynamical richness and short-term memory capacity. Memcapacitors are expected to ... read more 

Deep learning models for protein structure prediction achieve near-experimental accuracy on individual folds, yet their default inference samples concentrate around a single dominant conformation. We introduce ConforFlux, an inference-time procedure ... read more