Pub Date : 2024-06-15DOI: 10.1007/s10916-024-02080-0
Nikola Kirilov, E Bischoff
The rapid development of the digital healthcare and the electronic health records (EHR) requires smooth networking infrastructure to access data using Hypertext Transfer Protocol (HTTP)-based applications. The new HTTP/3 standard should provide performance and security improvements over HTTP/2. The goal of our work was to test the performance of HTTP/2 and HTTP/3 in the context of the EHRs. We used 45,000 test FHIR Patient resources downloaded and uploaded using 20, 50, 100 and 200 resources per Bundle, which resulted in 2251, 901, 451 and 226 HTTP GET and POST requests respectively. The first test downloading 20 resources per Bundle showed that HTTP/3 outperformed HTTP/2 in the local (mean request time 16.57 ms ± 7.2 standard deviation [SD]) and in the remote network (71.45 ms ± 43.5 SD) which is almost 3 times faster. In the 50 and 100 resources per Bundle test the HTTP/3 protocol demonstrated again more than two times gain in downloading performance for remote requests with mean request time 91.13 ms ± 34.54 SD and 88.09 ms ± 21.66 SD respectively. Furthermore, HTTP/3 outperformed HTTP/2 in the constructed clinical dataset remote transfer. In the upload tests HTTP/3 showed only a slight gain in performance merely in the remote network. The HTTP/3 protocol is a relatively new development and a major improvement for the worldwide web. This new technology is still missing in the digital health and EHRs. Its use could offer a major performance gain in situations where data is gathered from multiple remote locations.
{"title":"Networking Aspects of the Electronic Health Records: Hypertext Transfer Protocol Version 2 (HTTP/2) vs HTTP/3.","authors":"Nikola Kirilov, E Bischoff","doi":"10.1007/s10916-024-02080-0","DOIUrl":"https://doi.org/10.1007/s10916-024-02080-0","url":null,"abstract":"<p><p>The rapid development of the digital healthcare and the electronic health records (EHR) requires smooth networking infrastructure to access data using Hypertext Transfer Protocol (HTTP)-based applications. The new HTTP/3 standard should provide performance and security improvements over HTTP/2. The goal of our work was to test the performance of HTTP/2 and HTTP/3 in the context of the EHRs. We used 45,000 test FHIR Patient resources downloaded and uploaded using 20, 50, 100 and 200 resources per Bundle, which resulted in 2251, 901, 451 and 226 HTTP GET and POST requests respectively. The first test downloading 20 resources per Bundle showed that HTTP/3 outperformed HTTP/2 in the local (mean request time 16.57 ms ± 7.2 standard deviation [SD]) and in the remote network (71.45 ms ± 43.5 SD) which is almost 3 times faster. In the 50 and 100 resources per Bundle test the HTTP/3 protocol demonstrated again more than two times gain in downloading performance for remote requests with mean request time 91.13 ms ± 34.54 SD and 88.09 ms ± 21.66 SD respectively. Furthermore, HTTP/3 outperformed HTTP/2 in the constructed clinical dataset remote transfer. In the upload tests HTTP/3 showed only a slight gain in performance merely in the remote network. The HTTP/3 protocol is a relatively new development and a major improvement for the worldwide web. This new technology is still missing in the digital health and EHRs. Its use could offer a major performance gain in situations where data is gathered from multiple remote locations.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141327590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-10DOI: 10.1007/s10916-024-02079-7
Jacob C Clifton, Holly B Ende, Chandramouli Rathnam, Robert E Freundlich, Warren S Sandberg, Jonathan P Wanderer
Transition to the postanesthesia care unit (PACU) requires timely order placement by anesthesia providers. Computerized ordering enables automated order reminder systems, but their value is not fully understood. We performed a single-center, retrospective cohort study to estimate the association between automated PACU order reminders and primary outcomes (1) on-time order placement and (2) the degree of delay in placement. As a secondary post-hoc analysis, we studied the association between late order placement and PACU outcomes. We included patients with a qualifying postprocedure order from January 1, 2019, to May 31, 2023. We excluded cases transferred directly to the ICU, whose anesthesia provider was involved in the pilot testing of the reminder system, or those with missing covariate data. Order reminder system usage was defined by the primary attending anesthesiologist's receipt of a push notification reminder on the day of surgery. We estimated the association between reminder system usage and timely order placement using a logistic regression. For patients with late orders, we performed a survival analysis of order placement. The significance level was 0.05. Patient (e.g., age, race), procedural (e.g., anesthesia duration), and provider-based (e.g., ordering privileges) variables were used as covariates within the analyses. Reminders were associated with 51% increased odds of order placement prior to PACU admission (Odds Ratio: 1.51; 95% Confidence Interval: 1.43, 1.58; p ≤ 0.001), reducing the incidence of late PACU orders from 17.5% to 12.6% (p ≤ 0.001). In patients with late orders, the reminders were associated with 10% quicker placement (Hazard Ratio: 1.10; 95% CI 1.05, 1.15; p < 0.001). On-time order placement was associated with decreased PACU duration (p < 0.001), decreased odds of peak PACU pain score (p < 0.001), and decreased odds of multiple administration of antiemetics (p = 0.02). An order reminder system was associated with an increase in order placement prior to PACU arrival and a reduction in delay in order placement after arrival.
{"title":"A Mobile Post Anesthesia Care Unit Order Reminder System Improves Timely Order Entry.","authors":"Jacob C Clifton, Holly B Ende, Chandramouli Rathnam, Robert E Freundlich, Warren S Sandberg, Jonathan P Wanderer","doi":"10.1007/s10916-024-02079-7","DOIUrl":"10.1007/s10916-024-02079-7","url":null,"abstract":"<p><p>Transition to the postanesthesia care unit (PACU) requires timely order placement by anesthesia providers. Computerized ordering enables automated order reminder systems, but their value is not fully understood. We performed a single-center, retrospective cohort study to estimate the association between automated PACU order reminders and primary outcomes (1) on-time order placement and (2) the degree of delay in placement. As a secondary post-hoc analysis, we studied the association between late order placement and PACU outcomes. We included patients with a qualifying postprocedure order from January 1, 2019, to May 31, 2023. We excluded cases transferred directly to the ICU, whose anesthesia provider was involved in the pilot testing of the reminder system, or those with missing covariate data. Order reminder system usage was defined by the primary attending anesthesiologist's receipt of a push notification reminder on the day of surgery. We estimated the association between reminder system usage and timely order placement using a logistic regression. For patients with late orders, we performed a survival analysis of order placement. The significance level was 0.05. Patient (e.g., age, race), procedural (e.g., anesthesia duration), and provider-based (e.g., ordering privileges) variables were used as covariates within the analyses. Reminders were associated with 51% increased odds of order placement prior to PACU admission (Odds Ratio: 1.51; 95% Confidence Interval: 1.43, 1.58; p ≤ 0.001), reducing the incidence of late PACU orders from 17.5% to 12.6% (p ≤ 0.001). In patients with late orders, the reminders were associated with 10% quicker placement (Hazard Ratio: 1.10; 95% CI 1.05, 1.15; p < 0.001). On-time order placement was associated with decreased PACU duration (p < 0.001), decreased odds of peak PACU pain score (p < 0.001), and decreased odds of multiple administration of antiemetics (p = 0.02). An order reminder system was associated with an increase in order placement prior to PACU arrival and a reduction in delay in order placement after arrival.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11164754/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141296265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-05DOI: 10.1007/s10916-024-02075-x
Shiavax J Rao, Ameesh Isath, Parvathy Krishnan, Jonathan A Tangsrivimol, Hafeez Ul Hassan Virk, Zhen Wang, Benjamin S Glicksberg, Chayakrit Krittanawong
Artificial Intelligence, specifically advanced language models such as ChatGPT, have the potential to revolutionize various aspects of healthcare, medical education, and research. In this narrative review, we evaluate the myriad applications of ChatGPT in diverse healthcare domains. We discuss its potential role in clinical decision-making, exploring how it can assist physicians by providing rapid, data-driven insights for diagnosis and treatment. We review the benefits of ChatGPT in personalized patient care, particularly in geriatric care, medication management, weight loss and nutrition, and physical activity guidance. We further delve into its potential to enhance medical research, through the analysis of large datasets, and the development of novel methodologies. In the realm of medical education, we investigate the utility of ChatGPT as an information retrieval tool and personalized learning resource for medical students and professionals. There are numerous promising applications of ChatGPT that will likely induce paradigm shifts in healthcare practice, education, and research. The use of ChatGPT may come with several benefits in areas such as clinical decision making, geriatric care, medication management, weight loss and nutrition, physical fitness, scientific research, and medical education. Nevertheless, it is important to note that issues surrounding ethics, data privacy, transparency, inaccuracy, and inadequacy persist. Prior to widespread use in medicine, it is imperative to objectively evaluate the impact of ChatGPT in a real-world setting using a risk-based approach.
{"title":"ChatGPT: A Conceptual Review of Applications and Utility in the Field of Medicine.","authors":"Shiavax J Rao, Ameesh Isath, Parvathy Krishnan, Jonathan A Tangsrivimol, Hafeez Ul Hassan Virk, Zhen Wang, Benjamin S Glicksberg, Chayakrit Krittanawong","doi":"10.1007/s10916-024-02075-x","DOIUrl":"10.1007/s10916-024-02075-x","url":null,"abstract":"<p><p>Artificial Intelligence, specifically advanced language models such as ChatGPT, have the potential to revolutionize various aspects of healthcare, medical education, and research. In this narrative review, we evaluate the myriad applications of ChatGPT in diverse healthcare domains. We discuss its potential role in clinical decision-making, exploring how it can assist physicians by providing rapid, data-driven insights for diagnosis and treatment. We review the benefits of ChatGPT in personalized patient care, particularly in geriatric care, medication management, weight loss and nutrition, and physical activity guidance. We further delve into its potential to enhance medical research, through the analysis of large datasets, and the development of novel methodologies. In the realm of medical education, we investigate the utility of ChatGPT as an information retrieval tool and personalized learning resource for medical students and professionals. There are numerous promising applications of ChatGPT that will likely induce paradigm shifts in healthcare practice, education, and research. The use of ChatGPT may come with several benefits in areas such as clinical decision making, geriatric care, medication management, weight loss and nutrition, physical fitness, scientific research, and medical education. Nevertheless, it is important to note that issues surrounding ethics, data privacy, transparency, inaccuracy, and inadequacy persist. Prior to widespread use in medicine, it is imperative to objectively evaluate the impact of ChatGPT in a real-world setting using a risk-based approach.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141247716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.1007/s10916-024-02073-z
Pei-Fu Chen, Franklin Dexter
Modern anesthetic drugs ensure the efficacy of general anesthesia. Goals include reducing variability in surgical, tracheal extubation, post-anesthesia care unit, or intraoperative response recovery times. Generalized confidence intervals based on the log-normal distribution compare variability between groups, specifically ratios of standard deviations. The alternative statistical approaches, performing robust variance comparison tests, give P-values, not point estimates nor confidence intervals for the ratios of the standard deviations. We performed Monte-Carlo simulations to learn what happens to confidence intervals for ratios of standard deviations of anesthesia-associated times when analyses are based on the log-normal, but the true distributions are Weibull. We used simulation conditions comparable to meta-analyses of most randomized trials in anesthesia, and coefficients of variation . The estimates of the ratios of standard deviations were positively biased, but slightly, the ratios being 0.11% to 0.33% greater than nominal. In contrast, the 95% confidence intervals were very wide (i.e., > 95% of P ≥ 0.05). Although substantive inferentially, the differences in the confidence limits were small from a clinical or managerial perspective, with a maximum absolute difference in ratios of 0.016. Thus, P < 0.05 is reliable, but investigators should plan for Type II errors at greater than nominal rates.
现代麻醉药物可确保全身麻醉的疗效。目标包括减少手术、气管拔管、麻醉后护理病房或术中反应恢复时间的变异性。基于对数正态分布的广义置信区间可比较组间变异性,特别是标准偏差比。另一种统计方法是进行稳健方差比较测试,给出的是 P 值,而不是标准差比率的点估计值或置信区间。我们进行了蒙特卡罗模拟,以了解当分析以对数正态分布为基础,而真实分布为Weibull时,麻醉相关时间标准差比率的置信区间会发生什么变化。我们使用的模拟条件与大多数麻醉随机试验的荟萃分析相当,即 n ≈ 25,变异系数≈ 0.30。标准偏差比率的估计值呈正偏差,但偏差较小,比率比标称值大 0.11% 至 0.33%。相反,95% 置信区间非常宽(即 P≥0.05 的 >95%)。从临床或管理的角度来看,置信区间的差异虽然是实质性的,但却很小,比率的最大绝对差异为 0.016。因此,P
{"title":"Generalized Confidence Intervals for Ratios of Standard Deviations Based on Log-Normal Distribution when Times Follow Weibull Distributions.","authors":"Pei-Fu Chen, Franklin Dexter","doi":"10.1007/s10916-024-02073-z","DOIUrl":"https://doi.org/10.1007/s10916-024-02073-z","url":null,"abstract":"<p><p>Modern anesthetic drugs ensure the efficacy of general anesthesia. Goals include reducing variability in surgical, tracheal extubation, post-anesthesia care unit, or intraoperative response recovery times. Generalized confidence intervals based on the log-normal distribution compare variability between groups, specifically ratios of standard deviations. The alternative statistical approaches, performing robust variance comparison tests, give P-values, not point estimates nor confidence intervals for the ratios of the standard deviations. We performed Monte-Carlo simulations to learn what happens to confidence intervals for ratios of standard deviations of anesthesia-associated times when analyses are based on the log-normal, but the true distributions are Weibull. We used simulation conditions comparable to meta-analyses of most randomized trials in anesthesia, <math><mrow><mi>n</mi> <mo>≈</mo> <mn>25</mn></mrow> </math> and coefficients of variation <math><mrow><mo>≈</mo> <mn>0.30</mn></mrow> </math> . The estimates of the ratios of standard deviations were positively biased, but slightly, the ratios being 0.11% to 0.33% greater than nominal. In contrast, the 95% confidence intervals were very wide (i.e., > 95% of P ≥ 0.05). Although substantive inferentially, the differences in the confidence limits were small from a clinical or managerial perspective, with a maximum absolute difference in ratios of 0.016. Thus, P < 0.05 is reliable, but investigators should plan for Type II errors at greater than nominal rates.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141186208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-27DOI: 10.1007/s10916-024-02077-9
Luca Neri, Ivan Corazza, Matt T Oberdier, Jessica Lago, Ilaria Gallelli, Arrigo F G Cicero, Igor Diemberger, Alessandro Orro, Amir Beker, Nazareno Paolocci, Henry R Halperin, Claudio Borghi
Wearable electronics are increasingly common and useful as health monitoring devices, many of which feature the ability to record a single-lead electrocardiogram (ECG). However, recording the ECG commonly requires the user to touch the device to complete the lead circuit, which prevents continuous data acquisition. An alternative approach to enable continuous monitoring without user initiation is to embed the leads in a garment. This study assessed ECG data obtained from the YouCare device (a novel sensorized garment) via comparison with a conventional Holter monitor. A cohort of thirty patients (age range: 20-82 years; 16 females and 14 males) were enrolled and monitored for twenty-four hours with both the YouCare device and a Holter monitor. ECG data from both devices were qualitatively assessed by a panel of three expert cardiologists and quantitatively analyzed using specialized software. Patients also responded to a survey about the comfort of the YouCare device as compared to the Holter monitor. The YouCare device was assessed to have 70% of its ECG signals as "Good", 12% as "Acceptable", and 18% as "Not Readable". The R-wave, independently recorded by the YouCare device and Holter monitor, were synchronized within measurement error during 99.4% of cardiac cycles. In addition, patients found the YouCare device more comfortable than the Holter monitor (comfortable 22 vs. 5 and uncomfortable 1 vs. 18, respectively). Therefore, the quality of ECG data collected from the garment-based device was comparable to a Holter monitor when the signal was sufficiently acquired, and the garment was also comfortable.
{"title":"Comparison Between a Single-Lead ECG Garment Device and a Holter Monitor: A Signal Quality Assessment.","authors":"Luca Neri, Ivan Corazza, Matt T Oberdier, Jessica Lago, Ilaria Gallelli, Arrigo F G Cicero, Igor Diemberger, Alessandro Orro, Amir Beker, Nazareno Paolocci, Henry R Halperin, Claudio Borghi","doi":"10.1007/s10916-024-02077-9","DOIUrl":"10.1007/s10916-024-02077-9","url":null,"abstract":"<p><p>Wearable electronics are increasingly common and useful as health monitoring devices, many of which feature the ability to record a single-lead electrocardiogram (ECG). However, recording the ECG commonly requires the user to touch the device to complete the lead circuit, which prevents continuous data acquisition. An alternative approach to enable continuous monitoring without user initiation is to embed the leads in a garment. This study assessed ECG data obtained from the YouCare device (a novel sensorized garment) via comparison with a conventional Holter monitor. A cohort of thirty patients (age range: 20-82 years; 16 females and 14 males) were enrolled and monitored for twenty-four hours with both the YouCare device and a Holter monitor. ECG data from both devices were qualitatively assessed by a panel of three expert cardiologists and quantitatively analyzed using specialized software. Patients also responded to a survey about the comfort of the YouCare device as compared to the Holter monitor. The YouCare device was assessed to have 70% of its ECG signals as \"Good\", 12% as \"Acceptable\", and 18% as \"Not Readable\". The R-wave, independently recorded by the YouCare device and Holter monitor, were synchronized within measurement error during 99.4% of cardiac cycles. In addition, patients found the YouCare device more comfortable than the Holter monitor (comfortable 22 vs. 5 and uncomfortable 1 vs. 18, respectively). Therefore, the quality of ECG data collected from the garment-based device was comparable to a Holter monitor when the signal was sufficiently acquired, and the garment was also comfortable.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11129969/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141155219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-27DOI: 10.1007/s10916-024-02078-8
Yang Cao, Guochao Zhang, You Wu, Hang Yi
The rapid growth of internet users in China presents opportunities for advancing the "Healthy China 2030" initiative through online health education. Platforms like "Shanghai Health Cloud" and "National Health Information Platform" improve health literacy and management, enhancing overall public health. However, challenges such as the digital divide and the spread of unverified health information hinder progress. Addressing these issues requires enhancing digital infrastructure, employing advanced technologies for information validation, and setting high standards for online health services. Integrated efforts from various sectors are essential to maximize the benefits of online health education in China.
{"title":"Navigating Challenges and Seizing Opportunities in China's Era of Online Health Education.","authors":"Yang Cao, Guochao Zhang, You Wu, Hang Yi","doi":"10.1007/s10916-024-02078-8","DOIUrl":"https://doi.org/10.1007/s10916-024-02078-8","url":null,"abstract":"<p><p>The rapid growth of internet users in China presents opportunities for advancing the \"Healthy China 2030\" initiative through online health education. Platforms like \"Shanghai Health Cloud\" and \"National Health Information Platform\" improve health literacy and management, enhancing overall public health. However, challenges such as the digital divide and the spread of unverified health information hinder progress. Addressing these issues requires enhancing digital infrastructure, employing advanced technologies for information validation, and setting high standards for online health services. Integrated efforts from various sectors are essential to maximize the benefits of online health education in China.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141155225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-23DOI: 10.1007/s10916-024-02072-0
Mohamad-Hani Temsah, Abdullah N Alhuzaimi, Mohammed Almansour, Fadi Aljamaan, Khalid Alhasan, Munirah A Batarfi, Ibraheem Altamimi, Amani Alharbi, Adel Abdulaziz Alsuhaibani, Leena Alwakeel, Abdulrahman Abdulkhaliq Alzahrani, Khaled B Alsulaim, Amr Jamal, Afnan Khayat, Mohammed Hussien Alghamdi, Rabih Halwani, Muhammad Khurram Khan, Ayman Al-Eyadhy, Rakan Nazer
Artificial Intelligence (AI), particularly AI-Generated Imagery, has the potential to impact medical and patient education. This research explores the use of AI-generated imagery, from text-to-images, in medical education, focusing on congenital heart diseases (CHD). Utilizing ChatGPT's DALL·E 3, the research aims to assess the accuracy and educational value of AI-created images for 20 common CHDs. In this study, we utilized DALL·E 3 to generate a comprehensive set of 110 images, comprising ten images depicting the normal human heart and five images for each of the 20 common CHDs. The generated images were evaluated by a diverse group of 33 healthcare professionals. This cohort included cardiology experts, pediatricians, non-pediatric faculty members, trainees (medical students, interns, pediatric residents), and pediatric nurses. Utilizing a structured framework, these professionals assessed each image for anatomical accuracy, the usefulness of in-picture text, its appeal to medical professionals, and the image's potential applicability in medical presentations. Each item was assessed on a Likert scale of three. The assessments produced a total of 3630 images' assessments. Most AI-generated cardiac images were rated poorly as follows: 80.8% of images were rated as anatomically incorrect or fabricated, 85.2% rated to have incorrect text labels, 78.1% rated as not usable for medical education. The nurses and medical interns were found to have a more positive perception about the AI-generated cardiac images compared to the faculty members, pediatricians, and cardiology experts. Complex congenital anomalies were found to be significantly more predicted to anatomical fabrication compared to simple cardiac anomalies. There were significant challenges identified in image generation. Based on our findings, we recommend a vigilant approach towards the use of AI-generated imagery in medical education at present, underscoring the imperative for thorough validation and the importance of collaboration across disciplines. While we advise against its immediate integration until further validations are conducted, the study advocates for future AI-models to be fine-tuned with accurate medical data, enhancing their reliability and educational utility.
{"title":"Art or Artifact: Evaluating the Accuracy, Appeal, and Educational Value of AI-Generated Imagery in DALL·E 3 for Illustrating Congenital Heart Diseases.","authors":"Mohamad-Hani Temsah, Abdullah N Alhuzaimi, Mohammed Almansour, Fadi Aljamaan, Khalid Alhasan, Munirah A Batarfi, Ibraheem Altamimi, Amani Alharbi, Adel Abdulaziz Alsuhaibani, Leena Alwakeel, Abdulrahman Abdulkhaliq Alzahrani, Khaled B Alsulaim, Amr Jamal, Afnan Khayat, Mohammed Hussien Alghamdi, Rabih Halwani, Muhammad Khurram Khan, Ayman Al-Eyadhy, Rakan Nazer","doi":"10.1007/s10916-024-02072-0","DOIUrl":"https://doi.org/10.1007/s10916-024-02072-0","url":null,"abstract":"<p><p>Artificial Intelligence (AI), particularly AI-Generated Imagery, has the potential to impact medical and patient education. This research explores the use of AI-generated imagery, from text-to-images, in medical education, focusing on congenital heart diseases (CHD). Utilizing ChatGPT's DALL·E 3, the research aims to assess the accuracy and educational value of AI-created images for 20 common CHDs. In this study, we utilized DALL·E 3 to generate a comprehensive set of 110 images, comprising ten images depicting the normal human heart and five images for each of the 20 common CHDs. The generated images were evaluated by a diverse group of 33 healthcare professionals. This cohort included cardiology experts, pediatricians, non-pediatric faculty members, trainees (medical students, interns, pediatric residents), and pediatric nurses. Utilizing a structured framework, these professionals assessed each image for anatomical accuracy, the usefulness of in-picture text, its appeal to medical professionals, and the image's potential applicability in medical presentations. Each item was assessed on a Likert scale of three. The assessments produced a total of 3630 images' assessments. Most AI-generated cardiac images were rated poorly as follows: 80.8% of images were rated as anatomically incorrect or fabricated, 85.2% rated to have incorrect text labels, 78.1% rated as not usable for medical education. The nurses and medical interns were found to have a more positive perception about the AI-generated cardiac images compared to the faculty members, pediatricians, and cardiology experts. Complex congenital anomalies were found to be significantly more predicted to anatomical fabrication compared to simple cardiac anomalies. There were significant challenges identified in image generation. Based on our findings, we recommend a vigilant approach towards the use of AI-generated imagery in medical education at present, underscoring the imperative for thorough validation and the importance of collaboration across disciplines. While we advise against its immediate integration until further validations are conducted, the study advocates for future AI-models to be fine-tuned with accurate medical data, enhancing their reliability and educational utility.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141081372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-23DOI: 10.1007/s10916-024-02066-y
Jianning Li, David G Ellis, Antonio Pepe, Christina Gsaxner, Michele R Aizenberg, Jens Kleesiek, Jan Egger
Designing implants for large and complex cranial defects is a challenging task, even for professional designers. Current efforts on automating the design process focused mainly on convolutional neural networks (CNN), which have produced state-of-the-art results on reconstructing synthetic defects. However, existing CNN-based methods have been difficult to translate to clinical practice in cranioplasty, as their performance on large and complex cranial defects remains unsatisfactory. In this paper, we present a statistical shape model (SSM) built directly on the segmentation masks of the skulls represented as binary voxel occupancy grids and evaluate it on several cranial implant design datasets. Results show that, while CNN-based approaches outperform the SSM on synthetic defects, they are inferior to SSM when it comes to large, complex and real-world defects. Experienced neurosurgeons evaluate the implants generated by the SSM to be feasible for clinical use after minor manual corrections. Datasets and the SSM model are publicly available at https://github.com/Jianningli/ssm .
{"title":"Back to the Roots: Reconstructing Large and Complex Cranial Defects using an Image-based Statistical Shape Model.","authors":"Jianning Li, David G Ellis, Antonio Pepe, Christina Gsaxner, Michele R Aizenberg, Jens Kleesiek, Jan Egger","doi":"10.1007/s10916-024-02066-y","DOIUrl":"10.1007/s10916-024-02066-y","url":null,"abstract":"<p><p>Designing implants for large and complex cranial defects is a challenging task, even for professional designers. Current efforts on automating the design process focused mainly on convolutional neural networks (CNN), which have produced state-of-the-art results on reconstructing synthetic defects. However, existing CNN-based methods have been difficult to translate to clinical practice in cranioplasty, as their performance on large and complex cranial defects remains unsatisfactory. In this paper, we present a statistical shape model (SSM) built directly on the segmentation masks of the skulls represented as binary voxel occupancy grids and evaluate it on several cranial implant design datasets. Results show that, while CNN-based approaches outperform the SSM on synthetic defects, they are inferior to SSM when it comes to large, complex and real-world defects. Experienced neurosurgeons evaluate the implants generated by the SSM to be feasible for clinical use after minor manual corrections. Datasets and the SSM model are publicly available at https://github.com/Jianningli/ssm .</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11116219/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141081376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-22DOI: 10.1007/s10916-024-02076-w
Mohammad Saiduzzaman Sayed, Mohammad Abu Tareq Rony, Mohammad Shariful Islam, Ali Raza, Sawsan Tabassum, Mohammad Sh Daoud, Hazem Migdady, Laith Abualigah
Myocardial Infarction (MI) commonly referred to as a heart attack, results from the abrupt obstruction of blood supply to a section of the heart muscle, leading to the deterioration or death of the affected tissue due to a lack of oxygen. MI, poses a significant public health concern worldwide, particularly affecting the citizens of the Chittagong Metropolitan Area. The challenges lie in both prevention and treatment, as the emergence of MI has inflicted considerable suffering among residents. Early warning systems are crucial for managing epidemics promptly, especially given the escalating disease burden in older populations and the complexities of assessing present and future demands. The primary objective of this study is to forecast MI incidence early using a deep learning model, predicting the prevalence of heart attacks in patients. Our approach involves a novel dataset collected from daily heart attack incidence Time Series Patient Data spanning January 1, 2020, to December 31, 2021, in the Chittagong Metropolitan Area. Initially, we applied various advanced models, including Autoregressive Integrated Moving Average (ARIMA), Error-Trend-Seasonal (ETS), Trigonometric seasonality, Box-Cox transformation, ARMA errors, Trend and Seasonal (TBATS), and Long Short Time Memory (LSTM). To enhance prediction accuracy, we propose a novel Myocardial Sequence Classification (MSC)-LSTM method tailored to forecast heart attack occurrences in patients using the newly collected data from the Chittagong Metropolitan Area. Comprehensive results comparisons reveal that the novel MSC-LSTM model outperforms other applied models in terms of performance, achieving a minimum Mean Percentage Error (MPE) score of 1.6477. This research aids in predicting the likely future course of heart attack occurrences, facilitating the development of thorough plans for future preventive measures. The forecasting of MI occurrences contributes to effective resource allocation, capacity planning, policy creation, budgeting, public awareness, research identification, quality improvement, and disaster preparedness.
{"title":"A Novel Deep Learning Approach for Forecasting Myocardial Infarction Occurrences with Time Series Patient Data.","authors":"Mohammad Saiduzzaman Sayed, Mohammad Abu Tareq Rony, Mohammad Shariful Islam, Ali Raza, Sawsan Tabassum, Mohammad Sh Daoud, Hazem Migdady, Laith Abualigah","doi":"10.1007/s10916-024-02076-w","DOIUrl":"https://doi.org/10.1007/s10916-024-02076-w","url":null,"abstract":"<p><p>Myocardial Infarction (MI) commonly referred to as a heart attack, results from the abrupt obstruction of blood supply to a section of the heart muscle, leading to the deterioration or death of the affected tissue due to a lack of oxygen. MI, poses a significant public health concern worldwide, particularly affecting the citizens of the Chittagong Metropolitan Area. The challenges lie in both prevention and treatment, as the emergence of MI has inflicted considerable suffering among residents. Early warning systems are crucial for managing epidemics promptly, especially given the escalating disease burden in older populations and the complexities of assessing present and future demands. The primary objective of this study is to forecast MI incidence early using a deep learning model, predicting the prevalence of heart attacks in patients. Our approach involves a novel dataset collected from daily heart attack incidence Time Series Patient Data spanning January 1, 2020, to December 31, 2021, in the Chittagong Metropolitan Area. Initially, we applied various advanced models, including Autoregressive Integrated Moving Average (ARIMA), Error-Trend-Seasonal (ETS), Trigonometric seasonality, Box-Cox transformation, ARMA errors, Trend and Seasonal (TBATS), and Long Short Time Memory (LSTM). To enhance prediction accuracy, we propose a novel Myocardial Sequence Classification (MSC)-LSTM method tailored to forecast heart attack occurrences in patients using the newly collected data from the Chittagong Metropolitan Area. Comprehensive results comparisons reveal that the novel MSC-LSTM model outperforms other applied models in terms of performance, achieving a minimum Mean Percentage Error (MPE) score of 1.6477. This research aids in predicting the likely future course of heart attack occurrences, facilitating the development of thorough plans for future preventive measures. The forecasting of MI occurrences contributes to effective resource allocation, capacity planning, policy creation, budgeting, public awareness, research identification, quality improvement, and disaster preparedness.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141076079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-18DOI: 10.1007/s10916-024-02074-y
Zhi-Qiang Li, Xue-Feng Wang, Jian-Ping Liu
This study aimed to analyze the current landscape of ChatGPT application in the medical field, assessing the current collaboration patterns and research topic hotspots to understand the impact and trends. By conducting a search in the Web of Science, we collected literature related to the applications of ChatGPT in medicine, covering the period from January 1, 2000 up to January 16, 2024. Bibliometric analyses were performed using CiteSpace (V6.2., Drexel University, PA, USA) and Microsoft Excel (Microsoft Corp.,WA, USA) to map the collaboration among countries/regions, the distribution of institutions and authors, and clustering of keywords. A total of 574 eligible articles were included, with 97.74% published in 2023. These articles span various disciplines, particularly in Health Care Sciences Services, with extensive international collaboration involving 73 countries. In terms of countries/regions studied, USA, India, and China led in the number of publications. USA ot only published nearly half of the total number of papers but also exhibits a highest collaborative capability. Regarding the co-occurrence of institutions and scholars, the National University of Singapore and Harvard University held significant influence in the cooperation network, with the top three authors in terms of publications being Wiwanitkit V (10 articles), Seth I (9 articles), Klang E (7 articles), and Kleebayoon A (7 articles). Through keyword clustering, the study identified 9 research theme clusters, among which "digital health"was not only the largest in scale but also had the most citations. The study highlights ChatGPT's cross-disciplinary nature and collaborative research in medicine, showcasing its growth potential, particularly in digital health and clinical decision support. Future exploration should examine the socio-economic and cultural impacts of this trend, along with ChatGPT's specific technical uses in medical practice.
{"title":"Publication Trends and Hot Spots of ChatGPT's Application in the Medicine.","authors":"Zhi-Qiang Li, Xue-Feng Wang, Jian-Ping Liu","doi":"10.1007/s10916-024-02074-y","DOIUrl":"10.1007/s10916-024-02074-y","url":null,"abstract":"<p><p>This study aimed to analyze the current landscape of ChatGPT application in the medical field, assessing the current collaboration patterns and research topic hotspots to understand the impact and trends. By conducting a search in the Web of Science, we collected literature related to the applications of ChatGPT in medicine, covering the period from January 1, 2000 up to January 16, 2024. Bibliometric analyses were performed using CiteSpace (V6.2., Drexel University, PA, USA) and Microsoft Excel (Microsoft Corp.,WA, USA) to map the collaboration among countries/regions, the distribution of institutions and authors, and clustering of keywords. A total of 574 eligible articles were included, with 97.74% published in 2023. These articles span various disciplines, particularly in Health Care Sciences Services, with extensive international collaboration involving 73 countries. In terms of countries/regions studied, USA, India, and China led in the number of publications. USA ot only published nearly half of the total number of papers but also exhibits a highest collaborative capability. Regarding the co-occurrence of institutions and scholars, the National University of Singapore and Harvard University held significant influence in the cooperation network, with the top three authors in terms of publications being Wiwanitkit V (10 articles), Seth I (9 articles), Klang E (7 articles), and Kleebayoon A (7 articles). Through keyword clustering, the study identified 9 research theme clusters, among which \"digital health\"was not only the largest in scale but also had the most citations. The study highlights ChatGPT's cross-disciplinary nature and collaborative research in medicine, showcasing its growth potential, particularly in digital health and clinical decision support. Future exploration should examine the socio-economic and cultural impacts of this trend, along with ChatGPT's specific technical uses in medical practice.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11102365/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140957826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}