AI Medical Compendium Journal:
Digital health

Showing 1 to 10 of 22 articles

Performance analysis of an emergency triage system in ophthalmology using a customized CHATBOT.

Digital health
PURPOSE: To evaluate the performance of a custom ChatGPT-based chatbot in triaging ophthalmic emergencies compared to trained ophthalmologists.

Predicting neoadjuvant chemotherapy response in locally advanced gastric cancer using a machine learning model combining radiomics and clinical biomarkers.

Digital health
RATIONALE AND OBJECTIVES: Neoadjuvant chemotherapy (NAC) is a promising therapeutic strategy for managing locally advanced gastric cancer (LAGC), aiming to reduce tumor burden, enhance resection rates, and improve clinical outcomes. Due to variabilit...

How digital therapeutic alliances influence the perceived helpfulness of online mental health Q&A: An explainable machine learning approach.

Digital health
OBJECTIVE: This study investigates the role of digital therapeutic alliance (DTA) in predicting and explaining the perceived helpfulness of responses on online mental health Q&A platforms.

Exploring large language models for summarizing and interpreting an online brain tumor support forum.

Digital health
OBJECTIVE: This study explored the capabilities of large language models (LLMs) GPT-3.5, GPT-4, and Llama 3 to summarize qualitative data from an online brain tumor support forum, assessing the differences between these methods and traditional themat...

Enhancing medical AI with retrieval-augmented generation: A mini narrative review.

Digital health
Retrieval-augmented generation (RAG) is a powerful technique in artificial intelligence (AI) and machine learning that enhances the capabilities of large language models (LLMs) by integrating external data sources, allowing for more accurate, context...

Machine learning-driven prediction of hospital admissions using gradient boosting and GPT-2.

Digital health
BACKGROUND: Accurately predicting hospital admissions from the emergency department (ED) is essential for improving patient care and resource allocation. This study aimed to predict hospital admissions by integrating both structured clinical data and...

Digital transformation of nephrology POCUS education-Integrating a multiagent, artificial intelligence, and human collaboration-enhanced curriculum with expert feedback.

Digital health
BACKGROUND: The digital transformation in medical education is reshaping how clinical skills, such as point-of-care ultrasound (POCUS), are taught. In nephrology fellowship programs, POCUS is essential for enhancing diagnostic accuracy, guiding proce...

Exploring factors affecting patient satisfaction in online healthcare: A machine learning approach grounded in empathy theory.

Digital health
OBJECTIVE: Empathy between doctors and patients is crucial in enhancing patient satisfaction with medical consultations. This study, grounded in empathy theory, employs natural language processing and machine learning algorithms to explore the factor...

Deep learning-based automated tool for diagnosing diabetic peripheral neuropathy.

Digital health
BACKGROUND: Diabetic peripheral neuropathy (DPN) is a common complication of diabetes, and its early identification is crucial for improving patient outcomes. Corneal confocal microscopy (CCM) can non-invasively detect changes in corneal nerve fibers...

A mixed methods crossover randomized controlled trial exploring the experiences, perceptions, and usability of artificial intelligence (ChatGPT) in health sciences education.

Digital health
BACKGROUND: Generative artificial intelligence (AI) integrated programs such as Chat Generative Pre-trained Transformers (ChatGPT) are becoming more widespread in educational settings, with mounting ethical and reliability concerns regarding its usag...