Cardiac amyloidosis (CA) is an underdiagnosed form of infiltrative cardiomyopathy caused by abnormal amyloid fibrils deposited extracellularly in the myocardium and cardiac structures. There can be high variability in its clinical manifestations, and diagnosing CA requires expertise and often thorough evaluation; as such, the diagnosis of CA can be challenging and is often delayed. The application of artificial intelligence (AI) to different diagnostic modalities is rapidly expanding and transforming cardiovascular medicine. Advanced AI methods such as deep-learning convolutional neural networks (CNNs) may enhance the diagnostic process for CA by identifying patients at higher risk and potentially expediting the diagnosis of CA. In this review, we summarize the current state of AI applications to different diagnostic modalities used for the evaluation of CA, including their diagnostic and prognostic potential, and current challenges and limitations.
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http://dx.doi.org/10.3390/jcdd11040118 | DOI Listing |
Health Serv Insights
December 2024
Department of Surgery, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia.
One of the main challenges in breast cancer management is health system literacy to provide optimal and timely diagnosis and treatments within complex and multidisciplinary health system environments. Digitalised patient navigation programs have been developed and found to be helpful in high- and low-resource settings, but gaps remain in finding cost-effective navigation in the public sector in Malaysia, where resources are scarce and unstable. Hence, we set out to develop a virtual patient navigation application for breast cancer patients to enhance knowledge about cancer diagnosis and treatments and provide a tracking mechanism to ensure quality care.
View Article and Find Full Text PDFCureus
December 2024
Colorectal Surgery, Torbay and South Devon NHS Foundation Trust, Torbay, GBR.
Telemedicine in surgical care has undergone rapid advancements in recent years, leveraging technologies such as telerobotics, artificial intelligence (AI) diagnostics, and wearable devices to facilitate remote evaluation and monitoring of patients. These innovations have improved access to care, reduced costs, and enhanced patient satisfaction. However, significant challenges remain, including technical barriers, limited tactile feedback in telesurgery, and inequities arising from digital literacy and infrastructure gaps.
View Article and Find Full Text PDFInt J Cardiol Congenit Heart Dis
September 2024
National Pulmonary Hypertension Centre, Royal Papworth Hospital, UK.
Chronic thromboembolic pulmonary disease (CTEPD) with or without pulmonary hypertension (PH) occurs when thromboemboli in pulmonary arteries fail to resolve completely. Pulmonary artery obstructions due to chronic thrombi and secondary microvasculopathy can increase pulmonary arterial pressure and resistance leading to chronic thromboembolic PH (CTEPH). Mechanical interventions and/or PH medications can improve cardiopulmonary haemodynamic, alleviate symptoms, and decrease mortality risk.
View Article and Find Full Text PDFDigit Health
December 2024
Clinic Chat, LLC, Denver, CO, USA.
Background: Following the US Supreme Court decision overturning Roe v. Wade, there is evidence of limitations in access to safe abortion care. Artificially intelligent (AI)-enabled conversational chatbots are becoming an appealing option to support access to care, but generative AI systems can misinform and hallucinate and risk reinforcing problematic bias and stigma related to sexual and reproductive healthcare.
View Article and Find Full Text PDFDigit Health
December 2024
Department of Nursing Management and Education, College of Nursing, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia.
Background: As artificial intelligence (AI) continues to permeate various sectors including healthcare, understanding its impact on nursing students' attitudes and intentions to use is crucial. To examine the association of AI ethical awareness, attitudes, anxiety, and intention-to-use AI technology among Jordanian nursing students.
Method: A descriptive, cross-sectional design was used.
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