AIMC Topic: Decision Support Systems, Clinical

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User-Centered Methods in Explainable AI Development for Hospital Clinical Decision Support: A Scoping Review.

Studies in health technology and informatics
Explainable Artificial Intelligence (XAI) offers promising advancements in enhancing transparency and usability of AI-based Clinical Decision Support Systems (CDSS) in healthcare settings. These tools aim to improve clinical outcomes by assisting wit...

Real-Time AI-Assisted Insulin Titration System for Glucose Control in Patients With Type 2 Diabetes: A Randomized Clinical Trial.

JAMA network open
IMPORTANCE: Type 2 diabetes (T2D) is one of the most prevalent chronic diseases in the world. Insulin titration for glycemic control in T2D is crucial but limited by the lack of personalized and real-time tools.

Clinical and Social Characterization of Patients Hospitalized for COPD Exacerbation Using Machine Learning Tools.

Archivos de bronconeumologia
OBJECTIVE: This study aims to employ machine learning (ML) tools to cluster patients hospitalized for acute exacerbations of chronic obstructive pulmonary disease (COPD) based on their diverse social and clinical characteristics. This clustering is i...

Knowledge Uncertainty Estimation for Reliable Clinical Decision Support: A Delirium Risk Prognosis Case Study.

Studies in health technology and informatics
INTRODUCTION: Predictive models hold significant potential in healthcare, but their adoption in clinical settings is hampered by limited trust due to their inability to recognize when presented with unfamiliar data. Estimating knowledge uncertainty (...

Integrating Artificial Intelligence (AI) With Workforce Solutions for Sustainable Care: A Follow Up to Artificial Intelligence and Machine Learning (ML) Based Decision Support Systems in Mental Health.

International journal of mental health nursing
This integrative literature review examines the evolving role of artificial intelligence (AI) and machine learning (ML) based clinical decision support systems (CDSS) in mental health (MH) care, expanding on findings from a prior review (Higgins et a...

Do explainable AI (XAI) methods improve the acceptance of AI in clinical practice? An evaluation of XAI methods on Gleason grading.

The journal of pathology. Clinical research
This work aimed to evaluate both the usefulness and user acceptance of five gradient-based explainable artificial intelligence (XAI) methods in the use case of a prostate carcinoma clinical decision support system environment. In addition, we aimed t...

A Guide for Implementing an A.I-Driven Initiative in Rural Northern Ontario.

Studies in health technology and informatics
Diagnosing pulmonary embolism (PE) often requires specialized expertise in interpreting x-rays and radiographic images, resources that are mostly limited in rural settings. This paper explores the development of an electronic health record (EHR) syst...

Using human factors methods to mitigate bias in artificial intelligence-based clinical decision support.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: To highlight the often overlooked role of user interface (UI) design in mitigating bias in artificial intelligence (AI)-based clinical decision support (CDS).

The impact of AI feedback on the accuracy of diagnosis, decision switching and trust in radiography.

PloS one
Artificial intelligence decision support systems have been proposed to assist a struggling National Health Service (NHS) workforce in the United Kingdom. Its implementation in UK healthcare systems has been identified as a priority for deployment. Fe...

Advancements of AI in healthcare: a comprehensive review of ChatGPT's applications and challenges.

JPMA. The Journal of the Pakistan Medical Association
The rapid integration of artificial intelligence into healthcare has introduced transformative possibilities and challenges. The current narrative review was planned to explore diverse applications of Chat Generative Pre-Trained Transformer (ChatGPT)...