AIMC Topic: Machine Learning

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Artificial intelligence in functional urology: how it may shape the future.

Current opinion in urology
PURPOSE OF REVIEW: The aim of the present manuscript is to provide an overview on the current state of artificial intelligence (AI) tools in either decision making, diagnosis, treatment options, or outcome prediction in functional urology.

How the use of the artificial intelligence could improve surgical skills in urology: state of the art and future perspectives.

Current opinion in urology
PURPOSE OF REVIEW: As technology advances, surgical training has evolved in parallel over the previous decade. Training is commonly seen as a way to prepare surgeons for their day-to-day work; however, more importantly, it allows for certification of...

Future artificial intelligence tools and perspectives in medicine.

Current opinion in urology
PURPOSE OF REVIEW: Artificial intelligence has become popular in medical applications, specifically as a clinical support tool for computer-aided diagnosis. These tools are typically employed on medical data (i.e., image, molecular data, clinical var...

Kidney cancer management 3.0: can artificial intelligence make us better?

Current opinion in urology
PURPOSE OF REVIEW: Artificial intelligence holds tremendous potential for disrupting clinical medicine. Here we review the current role of artificial intelligence in the kidney cancer space.

Machine Scoring of Medical Students' Written Clinical Reasoning: Initial Validity Evidence.

Academic medicine : journal of the Association of American Medical Colleges
PURPOSE: Developing medical students' clinical reasoning requires a structured longitudinal curriculum with frequent targeted assessment and feedback. Performance-based assessments, which have the strongest validity evidence, are currently not feasib...

Joint Associations of Multiple Dietary Components With Cardiovascular Disease Risk: A Machine-Learning Approach.

American journal of epidemiology
The human diet consists of a complex mixture of components. To realistically assess dietary impacts on health, new statistical tools that can better address nonlinear, collinear, and interactive relationships are necessary. Using data from 1,928 heal...

Machine Learning: The Next Paradigm Shift in Medical Education.

Academic medicine : journal of the Association of American Medical Colleges
Machine learning (ML) algorithms are powerful prediction tools with immense potential in the clinical setting. There are a number of existing clinical tools that use ML, and many more are in development. Physicians are important stakeholders in the h...

An ultrafast and flexible liquid chromatography/tandem mass spectrometry system paves the way for machine learning driven in vivo sample processing in early drug discovery.

Rapid communications in mass spectrometry : RCM
RATIONALE: The low speed and low flexibility of most liquid chromatography/tandem mass spectrometry (LC/MS/MS) approaches in early drug discovery delay sample analysis from routine in vivo studies within the same day. A high-throughput platform for t...

AI-based spectroscopic monitoring of real-time interactions between SARS-CoV-2 and human ACE2.

Proceedings of the National Academy of Sciences of the United States of America
The novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), invades a human cell via human angiotensin-converting enzyme 2 (hACE2) as the entry, causing the severe coronavirus disease (COVID-19). The interactions between hACE...