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Decision Support Systems, Clinical

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Accuracy and consistency of publicly available Large Language Models as clinical decision support tools for the management of colon cancer.

Journal of surgical oncology
BACKGROUND: Large Language Models (LLM; e.g., ChatGPT) may be used to assist clinicians and form the basis of future clinical decision support (CDS) for colon cancer. The objectives of this study were to (1) evaluate the response accuracy of two LLM-...

Artificial intelligence and prescription of antibiotic therapy: present and future.

Expert review of anti-infective therapy
INTRODUCTION: In the past few years, the use of artificial intelligence in healthcare has grown exponentially. Prescription of antibiotics is not exempt from its rapid diffusion, and various machine learning (ML) techniques, from logistic regression ...

Effectiveness of Artificial Intelligence (AI) in Clinical Decision Support Systems and Care Delivery.

Journal of medical systems
This review aims to assess the effectiveness of AI-driven CDSSs on patient outcomes and clinical practices. A comprehensive search was conducted across PubMed, MEDLINE, and Scopus. Studies published from January 2018 to November 2023 were eligible fo...

Predictors of Health Care Practitioners' Intention to Use AI-Enabled Clinical Decision Support Systems: Meta-Analysis Based on the Unified Theory of Acceptance and Use of Technology.

Journal of medical Internet research
BACKGROUND: Artificial intelligence-enabled clinical decision support systems (AI-CDSSs) offer potential for improving health care outcomes, but their adoption among health care practitioners remains limited.

A pre-trained language model for emergency department intervention prediction using routine physiological data and clinical narratives.

International journal of medical informatics
INTRODUCTION: The urgency and complexity of emergency room (ER) settings require precise and swift decision-making processes for patient care. Ensuring the timely execution of critical examinations and interventions is vital for reducing diagnostic e...

The Impact of Information Relevancy and Interactivity on Intensivists' Trust in a Machine Learning-Based Bacteremia Prediction System: Simulation Study.

JMIR human factors
BACKGROUND: The exponential growth in computing power and the increasing digitization of information have substantially advanced the machine learning (ML) research field. However, ML algorithms are often considered "black boxes," and this fosters dis...

Making Co-Design More Responsible: Case Study on the Development of an AI-Based Decision Support System in Dementia Care.

JMIR human factors
BACKGROUND: Emerging technologies such as artificial intelligence (AI) require an early-stage assessment of potential societal and ethical implications to increase their acceptability, desirability, and sustainability. This paper explores and compare...

Rare disease diagnosis using knowledge guided retrieval augmentation for ChatGPT.

Journal of biomedical informatics
Although rare diseases individually have a low prevalence, they collectively affect nearly 400 million individuals around the world. On average, it takes five years for an accurate rare disease diagnosis, but many patients remain undiagnosed or misdi...

A decision support system for the detection of cutaneous fungal infections using artificial intelligence.

Pathology, research and practice
Cutaneous fungal infections are one of the most common skin conditions, hence, the burden of determining fungal elements upon microscopic examination with periodic acid-Schiff (PAS) and Gomori methenamine silver (GMS) stains, is very time consuming. ...

MoCab: A framework for the deployment of machine learning models across health information systems.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Machine learning models are vital for enhancing healthcare services. However, integrating them into health information systems (HISs) introduces challenges beyond clinical decision making, such as interoperability and divers...