AIMC Topic: Artificial Intelligence

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Unlocking new frontiers in epilepsy through AI: From seizure prediction to personalized medicine.

Epilepsy & behavior : E&B
Artificial intelligence (AI) is revolutionizing epilepsy care by advancing seizure detection, enhancing diagnostic precision, and enabling personalized treatment. Machine learning and deep learning technologies improve seizure monitoring, automate EE...

Analysis of AI foundation model features decodes the histopathologic landscape of HPV-positive head and neck squamous cell carcinomas.

Oral oncology
OBJECTIVES: Human papillomavirus (HPV) influences the pathobiology of Head and Neck Squamous Cell Carcinomas (HSNCCs). While deep learning shows promise in detecting HPV from hematoxylin and eosin (H&E) stained slides, the histologic features utilize...

Healthcare leaders' perceptions of the contribution of artificial intelligence to person-centred care: An interview study.

Scandinavian journal of public health
AIMS: The aim of this study was to explore healthcare leaders' perceptions of the contribution of artificial intelligence (AI) to person-centred care (PCC).

Assessments of lung nodules by an artificial intelligence chatbot using longitudinal CT images.

Cell reports. Medicine
Large language models have shown efficacy across multiple medical tasks. However, their value in the assessment of longitudinal follow-up computed tomography (CT) images of patients with lung nodules is unclear. In this study, we evaluate the ability...

Use of Artificial Intelligence, Internet of Things, and Edge Intelligence in Long-Term Care for Older People: Comprehensive Analysis Through Bibliometric, Google Trends, and Content Analysis.

Journal of medical Internet research
BACKGROUND: The global aging population poses critical challenges for long-term care (LTC), including workforce shortages, escalating health care costs, and increasing demand for high-quality care. Integrating artificial intelligence (AI), the Intern...

The role of explainable artificial intelligence in disease prediction: a systematic literature review and future research directions.

BMC medical informatics and decision making
Explainable Artificial Intelligence (XAI) enhances transparency and interpretability in AI models, which is crucial for trust and accountability in healthcare. A potential application of XAI is disease prediction using various data modalities. This s...

GPT-4 generated answer rationales to multiple choice assessment questions in undergraduate medical education.

BMC medical education
BACKGROUND: Pre-clerkship medical students benefit from practice questions that provide rationales for answer choices. Creating these rationales is a time-intensive endeavor. Therefore, not all practice multiple choice questions (MCQ) have correspond...

Evolution of AI enabled healthcare systems using textual data with a pretrained BERT deep learning model.

Scientific reports
In the rapidly evolving field of healthcare, Artificial Intelligence (AI) is increasingly driving the promotion of the transformation of traditional healthcare and improving medical diagnostic decisions. The overall goal is to uncover emerging trends...

Vision-language large learning model, GPT4V, accurately classifies the Boston Bowel Preparation Scale score.

BMJ open gastroenterology
INTRODUCTION: Large learning models (LLMs) such as GPT are advanced artificial intelligence (AI) models. Originally developed for natural language processing, they have been adapted for multi-modal tasks with vision-language input. One clinically rel...

An AI-Based Clinical Decision Support System for Antibiotic Therapy in Sepsis (KINBIOTICS): Use Case Analysis.

JMIR human factors
BACKGROUND: Antimicrobial resistances pose significant challenges in health care systems. Clinical decision support systems (CDSSs) represent a potential strategy for promoting a more targeted and guideline-based use of antibiotics. The integration o...