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Benchmarking

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Benchmarking Vision Capabilities of Large Language Models in Surgical Examination Questions.

Journal of surgical education
OBJECTIVE: Recent studies investigated the potential of large language models (LLMs) for clinical decision making and answering exam questions based on text input. Recent developments of LLMs have extended these models with vision capabilities. These...

Benchmarking Automatic Speech Recognition Technology for Natural Language Samples of Children With and Without Developmental Delays.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Natural language sampling (NLS) offers rich insights into real-world speech and language usage across diverse groups; yet, human transcription is time-consuming and costly. Automatic speech recognition (ASR) technology has the potential to revolution...

Simulation of adaptive immune receptors and repertoires with complex immune information to guide the development and benchmarking of AIRR machine learning.

Nucleic acids research
Machine learning (ML) has shown great potential in the adaptive immune receptor repertoire (AIRR) field. However, there is a lack of large-scale ground-truth experimental AIRR data suitable for AIRR-ML-based disease diagnostics and therapeutics disco...

Multi-institutional Knowledge-Based (KB) plan prediction benchmark models for whole breast irradiation.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: To train and validate KB prediction models by merging a large multi-institutional cohort of whole breast irradiation (WBI) plans using tangential fields.

A benchmark of deep learning approaches to predict lung cancer risk using national lung screening trial cohort.

Scientific reports
Deep learning (DL) methods have demonstrated remarkable effectiveness in assisting with lung cancer risk prediction tasks using computed tomography (CT) scans. However, the lack of comprehensive comparison and validation of state-of-the-art (SOTA) mo...

Deep learning methods for clinical workflow phase-based prediction of procedure duration: a benchmark study.

Computer assisted surgery (Abingdon, England)
This study evaluates the performance of deep learning models in the prediction of the end time of procedures performed in the cardiac catheterization laboratory (cath lab). We employed only the clinical phases derived from video analysis as input to ...

A-Eval: A benchmark for cross-dataset and cross-modality evaluation of abdominal multi-organ segmentation.

Medical image analysis
Although deep learning has revolutionized abdominal multi-organ segmentation, its models often struggle with generalization due to training on small-scale, specific datasets and modalities. The recent emergence of large-scale datasets may mitigate th...

Initializing a Public Repository for Hosting Benchmark Datasets to Facilitate Machine Learning Model Development in Food Safety.

Journal of food protection
While there is clear potential for artificial intelligence (AI) and machine learning (ML) models to help improve food safety, the development and deployment of these models in the food safety domain are by and large lacking. The absence of publicly a...

Meta-MolNet: A Cross-Domain Benchmark for Few Examples Drug Discovery.

IEEE transactions on neural networks and learning systems
Predicting the pharmacological activity, toxicity, and pharmacokinetic properties of molecules is a central task in drug discovery. Existing machine learning methods are transferred from one resource rich molecular property to another data scarce pro...

A dataset and benchmark for hospital course summarization with adapted large language models.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Brief hospital course (BHC) summaries are clinical documents that summarize a patient's hospital stay. While large language models (LLMs) depict remarkable capabilities in automating real-world tasks, their capabilities for healthcare appl...