Role of artificial intelligence in cardiology

Vidal-Pérez, Rafael; Vázquez‐Rodríguez, José Manuel

doi:10.4330/wjc.v15.i4.116

Cited by 5 publications

(12 citation statements)

References 15 publications

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“…The interventions included artificial intelligence (1, 2), machine learning (2), wearable devices (14), mobile health (10), telehealth (3,11), virtual reality ( 16), augmented reality (18), blockchain technology (19), Internet of Things (IoT) (20), and big data analytics (22). The outcomes assessed were blood pressure, cholesterol levels, medication adherence, cardiovascular events, and quality of life.…”

Section: Resultsmentioning

confidence: 99%

“…Second, it used a rigorous methodology to select studies and assess the risk of bias, utilizing tools like the Cochrane Risk of Bias Tool and the GRADE approach [5,6,8]. Third, it conducted a meta-analysis for the main outcomes, providing a quantitative synthesis of the data [10,11,12].…”

Section: Discussionmentioning

confidence: 99%

“…Innovative e-health technologies has improved CVD management by enhancing treatment adherence and reducing the need for in-person physician visits (10,13,15).…”

Section: Discussionmentioning

confidence: 99%

“…Interventions, particularly those involving wearable devices and mobile health technologies, showed significant reductions in both systolic and diastolic blood pressure. For instance, wearable devices demonstrated a mean difference in systolic blood pressure reduction of -5.7 mmHg (95% CI: -7.3 to -4.1) [8,9,10,11,12,13,14,28,30].…”

Section: Blood Pressurementioning

confidence: 99%

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Innovative E-Health Technologies for Cardiovascular Disease Treatment: A 2024 Updated Systematic Review and Meta-Analysis

Borges

2024

Preprint

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Background and Objectives:Cardiovascular disease (CVD) remains the leading cause of death globally, with an estimated 18.6 million deaths in 2021. E-health interventions have the potential to improve CVD management by providing remote monitoring, patient education, and support. This updated systematic review and meta-analysis aimed to synthesize the evidence on the effectiveness of innovative e-health technologies for CVD treatment, including studies published up to November 2023.Methods:A comprehensive literature search was conducted in MEDLINE, Embase, Cochrane Central Register of Controlled Trials, CINAHL, and PsycINFO from inception to November 2023. Randomized controlled trials (RCTs) comparing an innovative e-health technology to usual care or another intervention in adults with CVD or at risk of CVD were included. The risk of bias was assessed using the Cochrane Collaboration’s risk of bias tool. A meta-analysis was conducted for outcomes with sufficient studies and similar interventions and outcomes. For other outcomes, a narrative synthesis was performed. The certainty of evidence for each outcome was assessed using the GRADE approach.Findings:Thirty studies met the inclusion criteria, with a total of 10,234 participants. The interventions included artificial intelligence, machine learning, wearable devices, mobile health, telehealth, virtual reality, augmented reality, blockchain technology, Internet of Things (IoT), and big data analytics. The outcomes assessed were blood pressure, cholesterol levels, medication adherence, cardiovascular events, and quality of life. The meta-analysis showed that innovative e-health technologies were effective in improving blood pressure (mean difference: -5.7 mmHg; 95% CI: -7.3 to -4.1), cholesterol levels (mean difference: -10.8 mg/dL; 95% CI: -13.9 to -7.7), and medication adherence (odds ratio: 1.48; 95% CI: 1.31 to 1.67). The certainty of evidence for these outcomes was moderate. The narrative synthesis indicated that innovative e-health technologies were also effective in reducing cardiovascular events and improving quality of life. However, the evidence for these outcomes was limited, and more research is needed.Conclusions:The findings from this systematic review and meta-analysis provide robust evidence that innovative e-health technologies significantly enhance the management of cardiovascular disease (CVD). The included interventions, spanning artificial intelligence (AI), machine learning (ML), wearable devices, mobile health (mHealth), telehealth, virtual reality (VR), augmented reality (AR), blockchain technology, Internet of Things (IoT), and big data analytics, have demonstrated substantial improvements in key clinical outcomes. However, limitations such as variability in study design and the need for more high-quality RCTs highlight areas for further research. In clinical practice, these technologies offer promising avenues for improving patient outcomes and optimizing CVD management strategies.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

“…Innovative e-health technologies has improved CVD management by enhancing treatment adherence and reducing the need for in-person physician visits (10,13,15).…”

Section: Discussionmentioning

confidence: 99%

Section: Blood Pressurementioning

confidence: 99%

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Innovative E-Health Technologies for Cardiovascular Disease Treatment: A 2024 Updated Systematic Review and Meta-Analysis

Borges

2024

Preprint

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“…While specialized fields such as neurosurgery and cardiology are increasingly incorporating AI to improve surgical planning and robotic assistance, general surgery has been notably slower in adopting these advanced technologies [8,9].…”

Section: Introductionmentioning

confidence: 99%

What is the role of artificial intelligence in general surgery?

Baik,

Lee

2024

Ewha Med J

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The capabilities of artificial intelligence (AI) have recently surged, largely due to advancements in deep learning inspired by the structure and function of the neural networks of the human brain. In the medical field, the impact of AI spans from diagnostics and treatment recommendations to patient engagement and monitoring, considerably improving efficiency and outcomes. The clinical integration of AI has also been examined in specialties, including pathology, radiology, and oncology. General surgery primarily involves manual manipulation and includes preoperative, intraoperative, and postoperative care, all of which are critical for saving lives. Other fields have strived to utilize and adopt AI; nonetheless, general surgery appears to have retrogressed. In this review, we analyzed the published research, to understand how the application of AI in general surgery differs from that in other medical fields. Based on previous research in other fields, the application of AI in the preoperative stage is nearing feasibility. Ongoing research efforts aim to utilize AI to improve and predict operative outcomes, enhance performance, and improve patient care. However, the use of AI in the operating room remains significantly understudied. Moreover, ethical responsibilities are associated with such research, necessitating extensive work to gather evidence. By fostering interdisciplinary collaboration and leveraging lessons from AI success stories in other fields, AI tools could be specifically tailored for general surgery. Surgeons should be prepared for the integration of AI into clinical practice to achieve better outcomes; therefore, the time has come to consider ethical and legal implications.

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Artificial intelligence for emergency medical care

Rajput,

Sharma,

Malviya

2023

Health Care Science

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There is increasing research into the potential benefits of incorporating artificial intelligence (AI) and machine learning algorithms into emergency medical services. AI is finding new applications across a wide range of sectors, one of which is healthcare, where it is being used to enhance clinical diagnostics. AI solutions have enormous untapped potential to improve healthcare efficiency and quality, thus researchers have focused heavily on emergency medicine (EM). Many individuals without prior experience with any physician often receive their initial medical care in the emergency room. Two areas that could benefit from the implementation of AI are reducing waiting times and enhancing diagnostic capabilities. This study provides further explanation of how AI is used in emergency rooms. Several machine learning‐based algorithms are also addressed. In this research, we summarise recent developments in the use of AI in EM. This research tries to summarise the usefulness of AI in EM by looking at recent developments in emergency department operations and clinical patient management.

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Role of artificial intelligence in cardiology

Cited by 5 publications

References 15 publications

Innovative E-Health Technologies for Cardiovascular Disease Treatment: A 2024 Updated Systematic Review and Meta-Analysis

Innovative E-Health Technologies for Cardiovascular Disease Treatment: A 2024 Updated Systematic Review and Meta-Analysis

What is the role of artificial intelligence in general surgery?

Artificial intelligence for emergency medical care

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