AIMC Topic: Delivery of Health Care

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Synthetic Data in Healthcare and Drug Development: Definitions, Regulatory Frameworks, Issues.

CPT: pharmacometrics & systems pharmacology
With the recent and evolving regulatory frameworks regarding the usage of Artificial Intelligence (AI) in both drug and medical device development, the differentiation between data derived from observed ('true' or 'real') sources and artificial data ...

Transforming Healthcare: Intelligent Wearable Sensors Empowered by Smart Materials and Artificial Intelligence.

Advanced materials (Deerfield Beach, Fla.)
Intelligent wearable sensors, empowered by machine learning and innovative smart materials, enable rapid, accurate disease diagnosis, personalized therapy, and continuous health monitoring without disrupting daily life. This integration facilitates a...

Optimizing skin cancer screening with convolutional neural networks in smart healthcare systems.

PloS one
Skin cancer is among the most prevalent types of malignancy all over the global and is strongly associated with the patient's prognosis and the accuracy of the initial diagnosis. Clinical examination of skin lesions is a key aspect that is important ...

Artificial Intelligence (AI) and the future of Iran's Primary Health Care (PHC) system.

BMC primary care
OBJECTIVE: The rapid adoption of Artificial Intelligence (AI) in health service delivery underscores the need for awareness, preparedness, and strategic utilization of AI's potential to optimize Primary Health Care (PHC) systems. This study aims to e...

Public Disclosure of Results From Artificial Intelligence/Machine Learning Research in Health Care: Comprehensive Analysis of ClinicalTrials.gov, PubMed, and Scopus Data (2010-2023).

Journal of medical Internet research
BACKGROUND: Despite the rapid growth of research in artificial intelligence/machine learning (AI/ML), little is known about how often study results are disclosed years after study completion.

Trust and Acceptance Challenges in the Adoption of AI Applications in Health Care: Quantitative Survey Analysis.

Journal of medical Internet research
BACKGROUND: Artificial intelligence (AI) has potential to transform health care, but its successful implementation depends on the trust and acceptance of consumers and patients. Understanding the factors that influence attitudes toward AI is crucial ...

Understanding healthcare efficiency-an AI-supported narrative review of diverse terminologies used.

BMC medical education
BACKGROUND: Physicians have become more responsible for pursuing healthcare efficiency. However, contemporary literature uses multiple terminologies to describe healthcare efficiency. To identify which term is best suitable for medical education to e...

Protocol for human evaluation of generative artificial intelligence chatbots in clinical consultations.

PloS one
BACKGROUND: Generative artificial intelligence (GenAI) has the potential to revolutionise healthcare delivery. The nuances of real-life clinical practice and complex clinical environments demand a rigorous, evidence-based approach to ensure safe and ...

Using artificial intelligence to improve healthcare delivery in select allied health disciplines: a scoping review protocol.

BMJ open
INTRODUCTION: Methods to adopt artificial intelligence (AI) in healthcare clinical practice remain unclear. The potential for rapid integration of AI-enabled technologies across healthcare settings coupled with the growing digital divide in the healt...

A novel data-driven approach for Personas validation in healthcare using self-supervised machine learning.

Journal of biomedical informatics
OBJECTIVE: Persona validation is a challenging task, often relying on costly external validation methods. The aim of this study was the development of a novel method for Personas validation based on data already available during their creation.