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Capacity of Generative AI to Interpret Human Emotions From Visual and Textual Data: Pilot Evaluation Study.

JMIR mental health
BACKGROUND: Mentalization, which is integral to human cognitive processes, pertains to the interpretation of one's own and others' mental states, including emotions, beliefs, and intentions. With the advent of artificial intelligence (AI) and the pro...

Identifying Diabetic Retinopathy in the Human Eye: A Hybrid Approach Based on a Computer-Aided Diagnosis System Combined with Deep Learning.

Tomography (Ann Arbor, Mich.)
Diagnosing and screening for diabetic retinopathy is a well-known issue in the biomedical field. A component of computer-aided diagnosis that has advanced significantly over the past few years as a result of the development and effectiveness of deep ...

A Novel Multi-Scale Graph Neural Network for Metabolic Pathway Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
Predicting the metabolic pathway classes of compounds in the human body is an important problem in drug research and development. For this purpose, we propose a Multi-Scale Graph Neural Network framework, named MSGNN. The framework includes a subgrap...

Improving preliminary clinical diagnosis accuracy through knowledge filtering techniques in consultation dialogues.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Symptom descriptions by ordinary people are often inaccurate or vague when seeking medical advice, which often leads to inaccurate preliminary clinical diagnoses. To address this issue, we propose a deep learning model named...

DEPICTER: Deep representation clustering for histology annotation.

Computers in biology and medicine
Automatic segmentation of histopathology whole-slide images (WSI) usually involves supervised training of deep learning models with pixel-level labels to classify each pixel of the WSI into tissue regions such as benign or cancerous. However, fully s...

Evidence-based uncertainty-aware semi-supervised medical image segmentation.

Computers in biology and medicine
Semi-Supervised Learning (SSL) has demonstrated great potential to reduce the dependence on a large set of annotated data, which is challenging to collect in clinical practice. One of the most important SSL methods is to generate pseudo labels from t...

Robustness and reproducibility for AI learning in biomedical sciences: RENOIR.

Scientific reports
Artificial intelligence (AI) techniques are increasingly applied across various domains, favoured by the growing acquisition and public availability of large, complex datasets. Despite this trend, AI publications often suffer from lack of reproducibi...

Evaluating capabilities of large language models: Performance of GPT-4 on surgical knowledge assessments.

Surgery
BACKGROUND: Artificial intelligence has the potential to dramatically alter health care by enhancing how we diagnose and treat disease. One promising artificial intelligence model is ChatGPT, a general-purpose large language model trained by OpenAI. ...

Explainable deep learning diagnostic system for prediction of lung disease from medical images.

Computers in biology and medicine
Around the globe, respiratory lung diseases pose a severe threat to human survival. Based on a central goal to reduce contiguous transmission from infected to healthy persons, several technologies have evolved for diagnosing lung pathologies. One of ...

Automated Prediction of Photographic Wound Assessment Tool in Chronic Wound Images.

Journal of medical systems
Many automated approaches have been proposed in literature to quantify clinically relevant wound features based on image processing analysis, aiming at removing human subjectivity and accelerate clinical practice. In this work we present a fully auto...