Prospective evaluation of smartwatch-enabled detection of left ventricular dysfunction

Attia, Zachi I.; Harmon, David; Dugan, Jennifer; Manka, Lukas; López-Jiménez, Francisco; Lerman, Amir; Siontis, Konstantinos C.; Noseworthy, Peter A.; Yao, Xiaoxi; Klavetter, Eric W.; Halamka, John; Asirvatham, Samuel J.; Khan, Rita; Carter, Rickey E.; Leibovich, Bradley C.; Friedman, Paul A.

doi:10.1038/s41591-022-02053-1

Cited by 76 publications

(47 citation statements)

References 43 publications

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“…Another possible future development is for chatbots to reach superhuman level as seen in other medical domains. 10 Respondents' trust in chatbots' functions were mildly positive. Notably, there was a lower level of trust in chatbots as the medical complexity of the task increased, with the highest acceptance being administrative tasks like scheduling appointments and the lowest acceptance being providing treatment advice.…”

Section: Discussionmentioning

confidence: 99%

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Putting ChatGPT’s Medical Advice to the (Turing) Test

Nov

Singh

Mann

2023

Preprint

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Importance: Chatbots could play a role in answering patient questions, but patients' ability to distinguish between provider and chatbot responses, and patients' trust in chatbots' functions are not well established. Objective: To assess the feasibility of using ChatGPT or a similar AI-based chatbot for patient-provider communication. Design: Survey in January 2023 Setting: Survey Participants: A US representative sample of 400 study participants aged 18 and above was recruited on Prolific, a crowdsourcing platform for academic studies. 384 participants filled out the full survey. After removing participants who spent less than 3 minutes on the survey, 360 respondents remained. 53.3% of respondents analyzed were women; their average age was 45.4. Exposure(s): Ten representative non-administrative patient-provider interactions were extracted from the EHR. Patients' questions were placed in ChatGPT with a request for the chatbot to respond using approximately the same word count as the human provider's response. In the survey, each patient's question was followed by a provider- or ChatGPT-generated response. Participants were informed that five responses were provider-generated and five were chatbot-generated. Participants were asked, and incentivized financially, to correctly identify the response source. Participants were also asked about their trust in chatbots' functions in patient-provider communication, using a Likert scale of 1-5. Main Outcome(s) and Measure(s): Main outcome: Proportion of responses correctly classified as provider- vs chatbot-generated. Secondary outcomes: Average and standard deviation of responses to trust questions. Results: The correct classification of responses ranged between 23.4% to 86.7% for different questions. On average, chatbot responses were identified correctly 60.0% of the time and provider responses were identified correctly 62.3% of the time. On average, responses toward patients' trust in chatbots' functions were weakly positive (mean Likert score: 3.4), with lower trust as the health-related complexity of the task in questions increased. Conclusions and Relevance: ChatGPT responses to patient questions were close to indistinguishable from provider responses. Laypeople appear to trust the use of chatbots to answer lower risk health questions. It is important to continue studying patient-chatbot interaction as chatbots move from administrative to more clinical roles in healthcare.

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Section: Discussionmentioning

confidence: 99%

“…Another possible future development is for chatbots to reach superhuman level as seen in other medical domains. 10…”

Section: Discussionmentioning

confidence: 99%

Putting ChatGPT’s Medical Advice to the (Turing) Test

Nov

Singh

Mann

2023

Preprint

View full text Add to dashboard Cite

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“…Consumer-facing wearable devices have provided the potential for data from wider cohorts to be assessed, as well as for longitudinal analyses to be performed. Whilst not applied to AF as of yet, the Mayo group have demonstrated the utility of AI assessment of smart watch data to predict left ventricular dysfunction [ 135 ]. Algorithms that can work across the different modalities of consumer-facing devices will be of particular use, given the growing number of devices that are available to both consumers and physicians.…”

Section: Discussionmentioning

confidence: 99%

Electrocardiographic Predictors of Atrial Fibrillation

et al. 2023

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Background: Atrial fibrillation (AF) is the most common pathological arrhythmia, and its complications lead to significant morbidity and mortality. However, patients with AF can often go undetected, especially if they are asymptomatic or have a low burden of paroxysms. Identification of those at high risk of AF development may help refine screening and management strategies. Methods: PubMed and Embase databases were systematically searched for studies looking at electrocardiographic predictors of AF from inception to August 2021. Results: A total of 115 studies were reported which examined a combination of atrial and ventricular parameters that could be electrocardiographic predictors of AF. Atrial predictors include conduction parameters, such as the PR interval, p-wave index and dispersion, and partial interatrial or advanced interatrial block, or morphological parameters, such as p-wave axis, amplitude and terminal force. Ventricular predictors include abnormalities in QRS amplitude, morphology or duration, QT interval duration, r-wave progression and ST segment, i.e., t-wave abnormalities. Conclusions: There has been significant interest in electrocardiographic prediction of AF, especially in populations at high risk of atrial AF, such as those with an embolic stroke of undetermined source. This review highlights the breadth of possible predictive parameters, and possible pathological bases for the predictive role of each parameter are proposed.

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“…In addition to patches that have been specifically designed for this purpose, smartwatch and smartphone applications have also been designed for arrhythmia detection. Artificial intelligence algorithms applied to commercially available smartwatches have been shown to successfully detect left ventricular dysfunction [ 15 ]; which, in patients with CKD, may help to guide therapy or detect acute cardiac events in this high-risk population.…”

Section: Wearable Devices For Diagnosis and Monitoring In Ckdmentioning

confidence: 99%

Wearables in Nephrology: Fanciful Gadgetry or Prêt-à-Porter?

Stauss

Htay

Kooman

et al. 2023

Sensors

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Telemedicine and digitalised healthcare have recently seen exponential growth, led, in part, by increasing efforts to improve patient flexibility and autonomy, as well as drivers from financial austerity and concerns over climate change. Nephrology is no exception, and daily innovations are underway to provide digitalised alternatives to current models of healthcare provision. Wearable technology already exists commercially, and advances in nanotechnology and miniaturisation mean interest is also garnering clinically. Here, we outline the current existing wearable technology pertaining to the diagnosis and monitoring of patients with a spectrum of kidney disease, give an overview of wearable dialysis technology, and explore wearables that do not yet exist but would be of great interest. Finally, we discuss challenges and potential pitfalls with utilising wearable technology and the factors associated with successful implementation.

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Prospective evaluation of smartwatch-enabled detection of left ventricular dysfunction

Cited by 76 publications

References 43 publications

Putting ChatGPT’s Medical Advice to the (Turing) Test

Putting ChatGPT’s Medical Advice to the (Turing) Test

Electrocardiographic Predictors of Atrial Fibrillation

Wearables in Nephrology: Fanciful Gadgetry or Prêt-à-Porter?

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