AI Medical Compendium Topic

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Qualitative Research

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How to enhance employee engagement in the AI era: An fsQCA-based study.

Work (Reading, Mass.)
BackgroundThe ongoing fourth industrial revolution, characterized by the integration of intelligent machines, presents a transformative shift in the nature of work. Unlike past industrial revolutions that reshaped job dynamics, the current reliance o...

Is artificial intelligence an opportunity or a threat in nursing care?: An in-depth phenomenological study.

Archives of psychiatric nursing
INTRODUCTION: In the contemporary landscape, the use of artificial intelligence (AI) in nursing care has sparked ongoing debates regarding its merits and drawbacks.

User and Developer Views on Using AI Technologies to Facilitate the Early Detection of Skin Cancers in Primary Care Settings: Qualitative Semistructured Interview Study.

JMIR cancer
BACKGROUND: Skin cancers, including melanoma and keratinocyte cancers, are among the most common cancers worldwide, and their incidence is rising in most populations. Earlier detection of skin cancer leads to better outcomes for patients. Artificial ...

A Machine Learning Approach Using Topic Modeling to Identify and Assess Experiences of Patients With Colorectal Cancer: Explorative Study.

JMIR cancer
BACKGROUND: The rising number of cancer survivors and the shortage of health care professionals challenge the accessibility of cancer care. Health technologies are necessary for sustaining optimal patient journeys. To understand individuals' daily li...

Health Care Professionals and Data Scientists' Perspectives on a Machine Learning System to Anticipate and Manage the Risk of Decompensation From Patients With Heart Failure: Qualitative Interview Study.

Journal of medical Internet research
BACKGROUND: Heart failure (HF) is a significant global health problem, affecting approximately 64.34 million people worldwide. The worsening of HF, also known as HF decompensation, is a major factor behind hospitalizations, contributing to substantia...

Learning to Teach AI: Understanding the Needs of Healthcare Professionals.

Studies in health technology and informatics
As Artificial Intelligence (AI) technologies become more integrated into clinical settings to optimize care, healthcare professionals (HCPs) will need to become more adept in responsibly using these novel technologies to augment patient care. A quali...

An Explainable AI Application (AF'fective) to Support Monitoring of Patients With Atrial Fibrillation After Catheter Ablation: Qualitative Focus Group, Design Session, and Interview Study.

JMIR human factors
BACKGROUND: The opaque nature of artificial intelligence (AI) algorithms has led to distrust in medical contexts, particularly in the treatment and monitoring of atrial fibrillation. Although previous studies in explainable AI have demonstrated poten...

Exploring Older Adults' Perspectives and Acceptance of AI-Driven Health Technologies: Qualitative Study.

JMIR aging
BACKGROUND: Artificial intelligence (AI) is increasingly being applied in various health care services due to its enhanced efficiency and accuracy. As the population ages, AI-based health technologies could be a potent tool in older adults' health ca...

Physician Perspectives on the Potential Benefits and Risks of Applying Artificial Intelligence in Psychiatric Medicine: Qualitative Study.

JMIR mental health
BACKGROUND: As artificial intelligence (AI) tools are integrated more widely in psychiatric medicine, it is important to consider the impact these tools will have on clinical practice.

Exploring Suitability of Low-Severity Rating Hospital Incident Reports for Machine Learning.

Computers, informatics, nursing : CIN
Electronic incident reporting is a key quality and a safety process for healthcare organizations that assists in evaluating performance and informing quality improvement initiatives. Although it is mandatory for high-severity incident reports to be i...