Artificial intelligence has transformed many aspects of health care from image analysis to clinical decision making. Its evolution in medicine has been gradual and deliberate with several unanswered questions regarding efficiency, privacy, and bias. These artificial intelligence-based tools have relevance to assisted reproductive technologies with opportunities to impact informed consent, day-to-day management of ovarian stimulation, oocyte and embryo selection, and workflow. However, implementation must be an informed, cautious, and circumspect process to maximize outcomes and improve the clinical experience for patients and providers alike.
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| http://dx.doi.org/10.1016/j.fertnstert.2023.05.146 | DOI Listing |
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Diagnostic Decision-Making Variability Between Novice and Expert Optometrists for Glaucoma: Comparative Analysis to Inform AI System Design.
JMIR Med Inform
January 2025
Department of Systems Design Engineering, Faculty of Engineering, University of Waterloo, Waterloo, ON, Canada.
Background: While expert optometrists tend to rely on a deep understanding of the disease and intuitive pattern recognition, those with less experience may depend more on extensive data, comparisons, and external guidance. Understanding these variations is important for developing artificial intelligence (AI) systems that can effectively support optometrists with varying degrees of experience and minimize decision inconsistencies.
Objective: The main objective of this study is to identify and analyze the variations in diagnostic decision-making approaches between novice and expert optometrists.
Spiking Flip-Flop Memory in Resonant Tunneling Diode Neurons.
Phys Rev Lett
December 2024
University of Strathclyde, Institute of Photonics, SUPA Dept of Physics, Glasgow, United Kingdom.
We report a spiking flip-flop memory mechanism that allows controllably switching between neural-like excitable spike-firing and quiescent dynamics in a resonant tunneling diode (RTD) neuron under low-amplitude (<150 mV pulses) and high-speed (ns rate) inputs pulses. We also show that the timing of the set-reset input pulses is critical to elicit switching responses between spiking and quiescent regimes in the system. The demonstrated flip-flop spiking memory, in which spiking regimes can be controllably excited, stored, and inhibited in RTD neurons via specific low-amplitude, high-speed signals (delivered at proper time instants) offers high promise for RTD-based spiking neural networks, with the potential to be extended further to optoelectronic implementations where RTD neurons and RTD memory elements are deployed alongside for fast and efficient photonic-electronic neuromorphic computing and artificial intelligence hardware.
View Article and Find Full Text PDFPrebiotics Beyond the Gut: Omics Insights, Artificial Intelligence, and Clinical Trials in Organ-Specific Applications.
Probiotics Antimicrob Proteins
January 2025
Faculty of Pharmacy and Medical Sciences, University of Petra, Amman, 11196, Jordan.
Prebiotics, traditionally linked to gut health, are increasingly recognized for their systemic benefits, influencing multiple organ systems through interactions with the gut microbiota. Compounds like inulin, fructooligosaccharides (FOS), and galactooligosaccharides (GOS) enhance short-chain fatty acid (SCFA) production, benefiting neurocognitive health, cardiovascular function, immune modulation, and skin integrity. Advances in biotechnology, including deep eutectic solvents (DES) for extraction and machine learning (ML) for personalized formulations, have expanded prebiotic applications.
View Article and Find Full Text PDFscHNTL: single-cell RNA-seq data clustering augmented by high-order neighbors and triplet loss.
Bioinformatics
January 2025
School of Computing and Artificial Intelligence, Southwest Jiaotong University, Sichuan 611756, China.
Motivation: The rapid development of single-cell RNA sequencing (scRNA-seq) has significantly advanced biomedical research. Clustering analysis, crucial for scRNA-seq data, faces challenges including data sparsity, high dimensionality, and variable gene expressions. Better low-dimensional embeddings for these complex data should maintain intrinsic information while making similar data close and dissimilar data distant.
View Article and Find Full Text PDFGenerative artificial intelligence enables the generation of bone scintigraphy images and improves generalization of deep learning models in data-constrained environments.
Eur J Nucl Med Mol Imaging
January 2025
Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Spitalgasse 23, Vienna, 1090, Austria.
Purpose: Advancements of deep learning in medical imaging are often constrained by the limited availability of large, annotated datasets, resulting in underperforming models when deployed under real-world conditions. This study investigated a generative artificial intelligence (AI) approach to create synthetic medical images taking the example of bone scintigraphy scans, to increase the data diversity of small-scale datasets for more effective model training and improved generalization.
Methods: We trained a generative model on Tc-bone scintigraphy scans from 9,170 patients in one center to generate high-quality and fully anonymized annotated scans of patients representing two distinct disease patterns: abnormal uptake indicative of (i) bone metastases and (ii) cardiac uptake indicative of cardiac amyloidosis.
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