AIMC Topic: Machine Learning

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Opportunities and Challenges with Artificial Intelligence in Genomics.

Clinics in laboratory medicine
The development of artificial intelligence and machine learning algorithms may allow for advances in patient care. There are existing and potential applications in cancer diagnosis and monitoring, identification of at-risk groups of individuals, clas...

Clinical Artificial Intelligence: Design Principles and Fallacies.

Clinics in laboratory medicine
Clinical artificial intelligence (AI)/machine learning (ML) is anticipated to offer new abilities in clinical decision support, diagnostic reasoning, precision medicine, clinical operational support, and clinical research, but careful concern is need...

Artificial Intelligence in the Clinical Laboratory: An Overview with Frequently Asked Questions.

Clinics in laboratory medicine
This article provides an overview of machine learning fundamentals and some applications of machine learning to clinical laboratory diagnostics and patient management. A key goal of this article is to provide a basic foundation in clinical machine le...

Predicting Antigenic Distance from Genetic Data for PRRSV-Type 1: Applications of Machine Learning.

Microbiology spectrum
The control of porcine reproductive and respiratory syndrome (PRRS) remains a significant challenge due to the genetic and antigenic variability of the causative virus (PRRSV). Predominantly, PRRSV management includes using vaccines and live virus in...

A rubric for human-like agents and NeuroAI.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences
Researchers across cognitive, neuro- and computer sciences increasingly reference 'human-like' artificial intelligence and 'neuroAI'. However, the scope and use of the terms are often inconsistent. Contributed research ranges widely from mimicking , ...

Learning robotic navigation from experience: principles, methods and recent results.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences
Navigation is one of the most heavily studied problems in robotics and is conventionally approached as a geometric mapping and planning problem. However, real-world navigation presents a complex set of physical challenges that defies simple geometric...

Artificial Intelligence in Pediatric Nephrology-A Call for Action.

Advances in kidney disease and health
Artificial intelligence is playing an increasingly important role in many fields of clinical care to assist health care providers in patient management. In adult-focused nephrology, artificial intelligence is beginning to be used to improve clinical ...

Artificial intelligence-driven wearable technologies for neonatal cardiorespiratory monitoring. Part 2: artificial intelligence.

Pediatric research
BACKGROUND: With the development of Artificial Intelligence (AI) techniques, smart health monitoring, particularly neonatal cardiorespiratory monitoring with wearable devices, is becoming more popular. To this end, it is crucial to investigate the tr...

Predicting RNA secondary structure by a neural network: what features may be learned?

PeerJ
Deep learning is a class of machine learning techniques capable of creating internal representation of data without explicit preprogramming. Hence, in addition to practical applications, it is of interest to analyze what features of biological data m...

Applications of Deep Learning in Endocrine Neoplasms.

Surgical pathology clinics
Machine learning methods have been growing in prominence across all areas of medicine. In pathology, recent advances in deep learning (DL) have enabled computational analysis of histological samples, aiding in diagnosis and characterization in multip...