Artificial intelligence–enabled tools in cardiovascular medicine: A survey of current use, perceptions, and challenges

Schepart, Alexander; Burton, Arianna; Durkin, Larry; Fuller, Allison; Charap, Ellyn; Bhambri, Rahul; Ahmad, Faraz

doi:10.1016/j.cvdhj.2023.04.003

Cited by 10 publications

(5 citation statements)

References 18 publications

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“…Additionally, the 80% of respondents who highlighted the importance of transparency in trusting AI solutions from that study indicate a larger consensus on transparency than that expressed in this study. The lack of knowledge in AI by medical professionals was also highlighted in several studies of AI in cardiovascular care and medicine as a whole indicating that these problems are not con ned to cardiovascular care (10)(11)(12). Our study extends the ndings of challenges of AI in cardiovascular care by sampling a more diverse group of healthcare professionals involved in cardiovascular care.…”

Section: Discussionmentioning

confidence: 54%

“…The ndings of this study are similar to other studies exploring the challenges of AI in cardiovascular care and AI in general. The survey by Schepart et al (10) also indicated the existence of minority skepticism toward AI from cardiologists and health IT professionals. Additionally, the 80% of respondents who highlighted the importance of transparency in trusting AI solutions from that study indicate a larger consensus on transparency than that expressed in this study.…”

Section: Discussionmentioning

confidence: 99%

“…Although cardiovascular diseases (CVDs) have a signi cant impact on healthcare, there is a scarcity of experimental studies investigating the challenges associated with adopting AI in clinical cardiovascular care. A mixed-methods research, by Schepart et al (10), identi ed the existence of 5 major challenges of AI in cardiovascular care: insu cient knowledge, limited useability, limited funding, inadequate compatibility with electronic health records, and trust issues. It is important to note that the scope of Schepart et al's study was con ned to cardiologists and health IT specialists, which may not re ect the perspectives of the broader healthcare community.…”

Section: Introductionmentioning

confidence: 99%

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From Diagnosis to Management: Unveiling the Challenges of Artificial Intelligence Solutions in Cardiovascular Healthcare

Idakwo

2024

Preprint

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Cardiovascular diseases (CVDs) are the leading cause of mortality in the world. Artificial Intelligence (AI) offers an opportunity to improve the quality of care provided to cardiovascular patients due to its ability to handle large and complex data. Despite promising results obtained in several studies, widespread adoption of AI in cardiovascular care is lacking due to the existence of some gaps. The goal of this study is to analyze the existing challenges faced by AI solutions in cardiovascular care. This study adopted a mixed-methods research approach, combining semi-structured interviews with responses from a self-administered online survey. A total of 5 interviews were conducted and 91 valid survey responses were obtained. Survey respondents included doctors, nurses, medical researchers, health I specialists, hospital administrators, and other clinically affiliated participants working with cardiovascular patients. Participants identified 8 major challenges: data-related challenges, regulatory challenges, infrastructural challenges, gaps in knowledge, transparency challenges, ethical challenges, issues with change management, and acceptance challenges. These gaps hinder the adoption of AI in cardiovascular care and taking proactive measures to address these challenges is key to fostering AI adoption.

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Section: Discussionmentioning

confidence: 54%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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From Diagnosis to Management: Unveiling the Challenges of Artificial Intelligence Solutions in Cardiovascular Healthcare

Idakwo

2024

Preprint

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“…This is because trust and acceptance of AI technology are seen as preconditions for clinical workflow integration [ 6 ]. Currently, trust has already been demonstrated by several studies as one of the main determinants in driving the adoption of AI in health care [ 7 , 8 ]. One study showed that within a general home-based health care setting—where AI is applied on the internet of things–based devices to monitor patients’ health—risk perception, acceptance, and trust are related concepts that govern the ultimate use of the developed technology [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Risk Perception, Acceptance, and Trust of Using AI in Gastroenterology Practice in the Asia-Pacific Region: Web-Based Survey Study

Goh,

Chia,

Cheung

et al. 2024

JMIR AI

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Background The use of artificial intelligence (AI) can revolutionize health care, but this raises risk concerns. It is therefore crucial to understand how clinicians trust and accept AI technology. Gastroenterology, by its nature of being an image-based and intervention-heavy specialty, is an area where AI-assisted diagnosis and management can be applied extensively. Objective This study aimed to study how gastroenterologists or gastrointestinal surgeons accept and trust the use of AI in computer-aided detection (CADe), computer-aided characterization (CADx), and computer-aided intervention (CADi) of colorectal polyps in colonoscopy. Methods We conducted a web-based questionnaire from November 2022 to January 2023, involving 5 countries or areas in the Asia-Pacific region. The questionnaire included variables such as background and demography of users; intention to use AI, perceived risk; acceptance; and trust in AI-assisted detection, characterization, and intervention. We presented participants with 3 AI scenarios related to colonoscopy and the management of colorectal polyps. These scenarios reflect existing AI applications in colonoscopy, namely the detection of polyps (CADe), characterization of polyps (CADx), and AI-assisted polypectomy (CADi). Results In total, 165 gastroenterologists and gastrointestinal surgeons responded to a web-based survey using the structured questionnaire designed by experts in medical communications. Participants had a mean age of 44 (SD 9.65) years, were mostly male (n=116, 70.3%), and mostly worked in publicly funded hospitals (n=110, 66.67%). Participants reported relatively high exposure to AI, with 111 (67.27%) reporting having used AI for clinical diagnosis or treatment of digestive diseases. Gastroenterologists are highly interested to use AI in diagnosis but show different levels of reservations in risk prediction and acceptance of AI. Most participants (n=112, 72.72%) also expressed interest to use AI in their future practice. CADe was accepted by 83.03% (n=137) of respondents, CADx was accepted by 78.79% (n=130), and CADi was accepted by 72.12% (n=119). CADe and CADx were trusted by 85.45% (n=141) of respondents and CADi was trusted by 72.12% (n=119). There were no application-specific differences in risk perceptions, but more experienced clinicians gave lesser risk ratings. Conclusions Gastroenterologists reported overall high acceptance and trust levels of using AI-assisted colonoscopy in the management of colorectal polyps. However, this level of trust depends on the application scenario. Moreover, the relationship among risk perception, acceptance, and trust in using AI in gastroenterology practice is not straightforward.

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“…Understanding these predictors is paramount in clinical practice as it would allow for risk stratification and individualized treatment plans, potentially improving patient outcomes. Recent advances in machine learning provide a robust platform to explore complex interrelations among clinical variables [ 8 – 10 ]. Cardiology has increasingly used this approach to predict outcomes and guide treatment strategies, showcasing a promising avenue for understanding and managing complex conditions like TTS [ 11 – 14 ].…”

Section: Introductionmentioning

confidence: 99%

Importance of hospital and clinical factors for early mortality in Takotsubo syndrome: Insights from the Swedish Coronary Angiography and Angioplasty Registry

Gudmundsson,

Redfors,

Råmunddal

et al. 2024

BMC Cardiovasc Disord

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Background Takotsubo syndrome (TTS) is an acute heart failure syndrome with symptoms similar to acute myocardial infarction. TTS is often triggered by acute emotional or physical stress and is a significant cause of morbidity and mortality. Predictors of mortality in patients with TS are not well understood, and there is a need to identify high-risk patients and tailor treatment accordingly. This study aimed to assess the importance of various clinical factors in predicting 30-day mortality in TTS patients using a machine learning algorithm. Methods We analyzed data from the nationwide Swedish Coronary Angiography and Angioplasty Registry (SCAAR) for all patients with TTS in Sweden between 2015 and 2022. Gradient boosting was used to assess the relative importance of variables in predicting 30-day mortality in TTS patients. Results Of 3,180 patients hospitalized with TTS, 76.0% were women. The median age was 71.0 years (interquartile range 62–77). The crude all-cause mortality rate was 3.2% at 30 days. Machine learning algorithms by gradient boosting identified treating hospitals as the most important predictor of 30-day mortality. This factor was followed in significance by the clinical indication for angiography, creatinine level, Killip class, and age. Other less important factors included weight, height, and certain medical conditions such as hyperlipidemia and smoking status. Conclusions Using machine learning with gradient boosting, we analyzed all Swedish patients diagnosed with TTS over seven years and found that the treating hospital was the most significant predictor of 30-day mortality.

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Artificial intelligence–enabled tools in cardiovascular medicine: A survey of current use, perceptions, and challenges

Cited by 10 publications

References 18 publications

From Diagnosis to Management: Unveiling the Challenges of Artificial Intelligence Solutions in Cardiovascular Healthcare

From Diagnosis to Management: Unveiling the Challenges of Artificial Intelligence Solutions in Cardiovascular Healthcare

Risk Perception, Acceptance, and Trust of Using AI in Gastroenterology Practice in the Asia-Pacific Region: Web-Based Survey Study

Importance of hospital and clinical factors for early mortality in Takotsubo syndrome: Insights from the Swedish Coronary Angiography and Angioplasty Registry

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