AI Medical Compendium Topic

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Clinical Decision-Making

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Machine learning to predict distal caries in mandibular second molars associated with impacted third molars.

Scientific reports
Impacted mandibular third molars (M3M) are associated with the occurrence of distal caries on the adjacent mandibular second molars (DCM2M). In this study, we aimed to develop and validate five machine learning (ML) models designed to predict the occ...

Future of machine learning in paediatrics.

Archives of disease in childhood
Machine learning (ML) is a branch of artificial intelligence (AI) that enables computers to learn without being explicitly programmed, through a combination of statistics and computer science. It encompasses a variety of techniques used to analyse an...

Emerging role of artificial intelligence in stroke imaging.

Expert review of neurotherapeutics
: The recognition and therapy of patients with stroke is becoming progressively intricate as additional treatment choices become accessible and new associations between disease characteristics and treatment response are incessantly uncovered. Therefo...

A machine learning algorithm can optimize the day of trigger to improve in vitro fertilization outcomes.

Fertility and sterility
OBJECTIVE: To determine whether a machine learning causal inference model can optimize trigger injection timing to maximize the yield of fertilized oocytes (2PNs) and total usable blastocysts for a given cohort of stimulated follicles.

The Impact of Artificial Intelligence on Traditional Chinese Medicine.

The American journal of Chinese medicine
Traditional Chinese Medicine (TCM) is a well-established medical system with a long history. Currently, artificial intelligence (AI) is rapidly expanding in many fields including TCM. AI will significantly improve the reliability and accuracy of diag...

A clinical deep learning framework for continually learning from cardiac signals across diseases, time, modalities, and institutions.

Nature communications
Deep learning algorithms trained on instances that violate the assumption of being independent and identically distributed (i.i.d.) are known to experience destructive interference, a phenomenon characterized by a degradation in performance. Such a v...

Understanding, explaining, and utilizing medical artificial intelligence.

Nature human behaviour
Medical artificial intelligence is cost-effective and scalable and often outperforms human providers, yet people are reluctant to use it. We show that resistance to the utilization of medical artificial intelligence is driven by both the subjective d...

Tissue outcome prediction in hyperacute ischemic stroke: Comparison of machine learning models.

Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism
Machine Learning (ML) has been proposed for tissue fate prediction after acute ischemic stroke (AIS), with the aim to help treatment decision and patient management. We compared three different ML models to the clinical method based on diffusion-perf...

Augmenting the Transplant Team With Artificial Intelligence: Toward Meaningful AI Use in Solid Organ Transplant.

Frontiers in immunology
Advances in systems immunology, such as new biomarkers, offer the potential for highly personalized immunosuppression regimens that could improve patient outcomes. In the future, integrating all of this information with other patient history data wil...