Pub Date : 2024-09-06DOI: 10.1038/s41746-024-01216-3
Cyrus Tanade, Nusrat Sadia Khan, Emily Rakestraw, William D. Ladd, Erik W. Draeger, Amanda Randles
Understanding the evolving nature of coronary hemodynamics is crucial for early disease detection and monitoring progression. We require digital twins that mimic a patient’s circulatory system by integrating continuous physiological data and computing hemodynamic patterns over months. Current models match clinical flow measurements but are limited to single heartbeats. To this end, we introduced the longitudinal hemodynamic mapping framework (LHMF), designed to tackle critical challenges: (1) computational intractability of explicit methods; (2) boundary conditions reflecting varying activity states; and (3) accessible computing resources for clinical translation. We show negligible error (0.0002–0.004%) between LHMF and explicit data of 750 heartbeats. We deployed LHMF across traditional and cloud-based platforms, demonstrating high-throughput simulations on heterogeneous systems. Additionally, we established LHMFC, where hemodynamically similar heartbeats are clustered to avoid redundant simulations, accurately reconstructing longitudinal hemodynamic maps (LHMs). This study captured 3D hemodynamics over 4.5 million heartbeats, paving the way for cardiovascular digital twins.
{"title":"Establishing the longitudinal hemodynamic mapping framework for wearable-driven coronary digital twins","authors":"Cyrus Tanade, Nusrat Sadia Khan, Emily Rakestraw, William D. Ladd, Erik W. Draeger, Amanda Randles","doi":"10.1038/s41746-024-01216-3","DOIUrl":"10.1038/s41746-024-01216-3","url":null,"abstract":"Understanding the evolving nature of coronary hemodynamics is crucial for early disease detection and monitoring progression. We require digital twins that mimic a patient’s circulatory system by integrating continuous physiological data and computing hemodynamic patterns over months. Current models match clinical flow measurements but are limited to single heartbeats. To this end, we introduced the longitudinal hemodynamic mapping framework (LHMF), designed to tackle critical challenges: (1) computational intractability of explicit methods; (2) boundary conditions reflecting varying activity states; and (3) accessible computing resources for clinical translation. We show negligible error (0.0002–0.004%) between LHMF and explicit data of 750 heartbeats. We deployed LHMF across traditional and cloud-based platforms, demonstrating high-throughput simulations on heterogeneous systems. Additionally, we established LHMFC, where hemodynamically similar heartbeats are clustered to avoid redundant simulations, accurately reconstructing longitudinal hemodynamic maps (LHMs). This study captured 3D hemodynamics over 4.5 million heartbeats, paving the way for cardiovascular digital twins.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01216-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142142605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-06DOI: 10.1038/s41746-024-01236-z
Cyril Brzenczek, Quentin Klopfenstein, Tom Hähnel, Holger Fröhlich, Enrico Glaab, On behalf of the NCER-PD Consortium
Parkinson’s disease (PD) presents diverse symptoms and comorbidities, complicating its diagnosis and management. The primary objective of this cross-sectional, monocentric study was to assess digital gait sensor data’s utility for monitoring and diagnosis of motor and gait impairment in PD. As a secondary objective, for the more challenging tasks of detecting comorbidities, non-motor outcomes, and disease progression subgroups, we evaluated for the first time the integration of digital markers with metabolomics and clinical data. Using shoe-attached digital sensors, we collected gait measurements from 162 patients and 129 controls in a single visit. Machine learning models showed significant diagnostic power, with AUC scores of 83–92% for PD vs. control and up to 75% for motor severity classification. Integrating gait data with metabolomics and clinical data improved predictions for challenging-to-detect comorbidities such as hallucinations. Overall, this approach using digital biomarkers and multimodal data integration can assist in objective disease monitoring, diagnosis, and comorbidity detection.
{"title":"Integrating digital gait data with metabolomics and clinical data to predict outcomes in Parkinson’s disease","authors":"Cyril Brzenczek, Quentin Klopfenstein, Tom Hähnel, Holger Fröhlich, Enrico Glaab, On behalf of the NCER-PD Consortium","doi":"10.1038/s41746-024-01236-z","DOIUrl":"10.1038/s41746-024-01236-z","url":null,"abstract":"Parkinson’s disease (PD) presents diverse symptoms and comorbidities, complicating its diagnosis and management. The primary objective of this cross-sectional, monocentric study was to assess digital gait sensor data’s utility for monitoring and diagnosis of motor and gait impairment in PD. As a secondary objective, for the more challenging tasks of detecting comorbidities, non-motor outcomes, and disease progression subgroups, we evaluated for the first time the integration of digital markers with metabolomics and clinical data. Using shoe-attached digital sensors, we collected gait measurements from 162 patients and 129 controls in a single visit. Machine learning models showed significant diagnostic power, with AUC scores of 83–92% for PD vs. control and up to 75% for motor severity classification. Integrating gait data with metabolomics and clinical data improved predictions for challenging-to-detect comorbidities such as hallucinations. Overall, this approach using digital biomarkers and multimodal data integration can assist in objective disease monitoring, diagnosis, and comorbidity detection.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01236-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142142606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-06DOI: 10.1038/s41746-024-01232-3
Mateo Aboy, Timo Minssen, Effy Vayena
The newly adopted EU AI Act represents a pivotal milestone that heralds a new era of AI regulation across industries. With its broad territorial scope and applicability, this comprehensive legislation establishes stringent requirements for AI systems. In this article, we analyze the AI Act’s impact on digital medical products, such as medical devices: How does the AI Act apply to AI/ML-enabled medical devices? How are they classified? What are the compliance requirements? And, what are the obligations of ‘providers’ of these AI systems? After addressing these foundational questions, we discuss the AI Act’s broader implications for the future of regulated digital medical products.
{"title":"Navigating the EU AI Act: implications for regulated digital medical products","authors":"Mateo Aboy, Timo Minssen, Effy Vayena","doi":"10.1038/s41746-024-01232-3","DOIUrl":"10.1038/s41746-024-01232-3","url":null,"abstract":"The newly adopted EU AI Act represents a pivotal milestone that heralds a new era of AI regulation across industries. With its broad territorial scope and applicability, this comprehensive legislation establishes stringent requirements for AI systems. In this article, we analyze the AI Act’s impact on digital medical products, such as medical devices: How does the AI Act apply to AI/ML-enabled medical devices? How are they classified? What are the compliance requirements? And, what are the obligations of ‘providers’ of these AI systems? After addressing these foundational questions, we discuss the AI Act’s broader implications for the future of regulated digital medical products.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01232-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142142713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-05DOI: 10.1038/s41746-024-01234-1
Hanjin Park, Oh-Seok Kwon, Jaemin Shim, Daehoon Kim, Je-Wook Park, Yun-Gi Kim, Hee Tae Yu, Tae-Hoon Kim, Jae-Sun Uhm, Jong-Il Choi, Boyoung Joung, Moon-Hyoung Lee, Hui-Nam Pak
The application of artificial intelligence (AI) algorithms to 12-lead electrocardiogram (ECG) provides promising age prediction models. We explored whether the gap between the pre-procedural AI-ECG age and chronological age can predict atrial fibrillation (AF) recurrence after catheter ablation. We validated a pre-trained residual network-based model for age prediction on four multinational datasets. Then we estimated AI-ECG age using a pre-procedural sinus rhythm ECG among individuals on anti-arrhythmic drugs who underwent de-novo AF catheter ablation from two independent AF ablation cohorts. We categorized the AI-ECG age gap based on the mean absolute error of the AI-ECG age gap obtained from four model validation datasets; aged-ECG (≥10 years) and normal ECG age (<10 years) groups. In the two AF ablation cohorts, aged-ECG was associated with a significantly increased risk of AF recurrence compared to the normal ECG age group. These associations were independent of chronological age or left atrial diameter. In summary, a pre-procedural AI-ECG age has a prognostic value for AF recurrence after catheter ablation.
{"title":"Artificial intelligence estimated electrocardiographic age as a recurrence predictor after atrial fibrillation catheter ablation","authors":"Hanjin Park, Oh-Seok Kwon, Jaemin Shim, Daehoon Kim, Je-Wook Park, Yun-Gi Kim, Hee Tae Yu, Tae-Hoon Kim, Jae-Sun Uhm, Jong-Il Choi, Boyoung Joung, Moon-Hyoung Lee, Hui-Nam Pak","doi":"10.1038/s41746-024-01234-1","DOIUrl":"10.1038/s41746-024-01234-1","url":null,"abstract":"The application of artificial intelligence (AI) algorithms to 12-lead electrocardiogram (ECG) provides promising age prediction models. We explored whether the gap between the pre-procedural AI-ECG age and chronological age can predict atrial fibrillation (AF) recurrence after catheter ablation. We validated a pre-trained residual network-based model for age prediction on four multinational datasets. Then we estimated AI-ECG age using a pre-procedural sinus rhythm ECG among individuals on anti-arrhythmic drugs who underwent de-novo AF catheter ablation from two independent AF ablation cohorts. We categorized the AI-ECG age gap based on the mean absolute error of the AI-ECG age gap obtained from four model validation datasets; aged-ECG (≥10 years) and normal ECG age (<10 years) groups. In the two AF ablation cohorts, aged-ECG was associated with a significantly increased risk of AF recurrence compared to the normal ECG age group. These associations were independent of chronological age or left atrial diameter. In summary, a pre-procedural AI-ECG age has a prognostic value for AF recurrence after catheter ablation.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01234-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142137895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-05DOI: 10.1038/s41746-024-01227-0
Faris Gulamali, Pushkala Jayaraman, Ashwin S. Sawant, Jacob Desman, Benjamin Fox, Annette Chang, Brian Y. Soong, Naveen Arivazagan, Alexandra S. Reynolds, Son Q. Duong, Akhil Vaid, Patricia Kovatch, Robert Freeman, Ira S. Hofer, Ankit Sakhuja, Neha S. Dangayach, David S. Reich, Alexander W. Charney, Girish N. Nadkarni
Increased intracranial pressure (ICP) ≥15 mmHg is associated with adverse neurological outcomes, but needs invasive intracranial monitoring. Using the publicly available MIMIC-III Waveform Database (2000–2013) from Boston, we developed an artificial intelligence-derived biomarker for elevated ICP (aICP) for adult patients. aICP uses routinely collected extracranial waveform data as input, reducing the need for invasive monitoring. We externally validated aICP with an independent dataset from the Mount Sinai Hospital (2020–2022) in New York City. The AUROC, accuracy, sensitivity, and specificity on the external validation dataset were 0.80 (95% CI, 0.80–0.80), 73.8% (95% CI, 72.0–75.6%), 73.5% (95% CI 72.5–74.5%), and 73.0% (95% CI, 72.0–74.0%), respectively. We also present an exploratory analysis showing aICP predictions are associated with clinical phenotypes. A ten-percentile increment was associated with brain malignancy (OR = 1.68; 95% CI, 1.09-2.60), intracerebral hemorrhage (OR = 1.18; 95% CI, 1.07–1.32), and craniotomy (OR = 1.43; 95% CI, 1.12–1.84; P < 0.05 for all).
{"title":"Derivation, external and clinical validation of a deep learning approach for detecting intracranial hypertension","authors":"Faris Gulamali, Pushkala Jayaraman, Ashwin S. Sawant, Jacob Desman, Benjamin Fox, Annette Chang, Brian Y. Soong, Naveen Arivazagan, Alexandra S. Reynolds, Son Q. Duong, Akhil Vaid, Patricia Kovatch, Robert Freeman, Ira S. Hofer, Ankit Sakhuja, Neha S. Dangayach, David S. Reich, Alexander W. Charney, Girish N. Nadkarni","doi":"10.1038/s41746-024-01227-0","DOIUrl":"10.1038/s41746-024-01227-0","url":null,"abstract":"Increased intracranial pressure (ICP) ≥15 mmHg is associated with adverse neurological outcomes, but needs invasive intracranial monitoring. Using the publicly available MIMIC-III Waveform Database (2000–2013) from Boston, we developed an artificial intelligence-derived biomarker for elevated ICP (aICP) for adult patients. aICP uses routinely collected extracranial waveform data as input, reducing the need for invasive monitoring. We externally validated aICP with an independent dataset from the Mount Sinai Hospital (2020–2022) in New York City. The AUROC, accuracy, sensitivity, and specificity on the external validation dataset were 0.80 (95% CI, 0.80–0.80), 73.8% (95% CI, 72.0–75.6%), 73.5% (95% CI 72.5–74.5%), and 73.0% (95% CI, 72.0–74.0%), respectively. We also present an exploratory analysis showing aICP predictions are associated with clinical phenotypes. A ten-percentile increment was associated with brain malignancy (OR = 1.68; 95% CI, 1.09-2.60), intracerebral hemorrhage (OR = 1.18; 95% CI, 1.07–1.32), and craniotomy (OR = 1.43; 95% CI, 1.12–1.84; P < 0.05 for all).","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01227-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142137901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-04DOI: 10.1038/s41746-024-01211-8
Gül Erdemli, Margarita Grammatikopoulou, Bertil Wagner, Srinivasan Vairavan, Jelena Curcic, Dag Aarsland, Gayle Wittenberg, Spiros Nikolopoulos, Marijn Muurling, Holger Froehlich, Casper de Boer, Niraj M. Shanbhag, Vera J. M. Nies, Neva Coello, Dianne Gove, Ana Diaz, Suzanne Foy, Wim Dartee, Anna-Katharine Brem
The Remote Assessment of Disease and Relapse – Alzheimer’s Disease (RADAR-AD) consortium evaluated remote measurement technologies (RMTs) for assessing functional status in AD. The consortium engaged with the European Medicines Agency (EMA) to obtain feedback on identification of meaningful functional domains, selection of RMTs and clinical study design to assess the feasibility of using RMTs in AD clinical studies. We summarized the feedback and the lessons learned to guide future projects.
疾病和复发远程评估--阿尔茨海默病（RADAR-AD）联盟评估了用于评估阿尔茨海默病功能状态的远程测量技术（RMT）。该联盟与欧洲药品管理局 (EMA) 合作，就有意义的功能领域的确定、RMT 的选择和临床研究设计等方面获得反馈，以评估在 AD 临床研究中使用 RMT 的可行性。我们总结了反馈意见和经验教训，以指导未来的项目。
{"title":"Regulatory considerations for developing remote measurement technologies for Alzheimer’s disease research","authors":"Gül Erdemli, Margarita Grammatikopoulou, Bertil Wagner, Srinivasan Vairavan, Jelena Curcic, Dag Aarsland, Gayle Wittenberg, Spiros Nikolopoulos, Marijn Muurling, Holger Froehlich, Casper de Boer, Niraj M. Shanbhag, Vera J. M. Nies, Neva Coello, Dianne Gove, Ana Diaz, Suzanne Foy, Wim Dartee, Anna-Katharine Brem","doi":"10.1038/s41746-024-01211-8","DOIUrl":"10.1038/s41746-024-01211-8","url":null,"abstract":"The Remote Assessment of Disease and Relapse – Alzheimer’s Disease (RADAR-AD) consortium evaluated remote measurement technologies (RMTs) for assessing functional status in AD. The consortium engaged with the European Medicines Agency (EMA) to obtain feedback on identification of meaningful functional domains, selection of RMTs and clinical study design to assess the feasibility of using RMTs in AD clinical studies. We summarized the feedback and the lessons learned to guide future projects.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01211-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142130681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Deep learning for computer vision can be leveraged for interpreting surgical scenes and providing surgeons with real-time guidance to avoid complications. However, neither generalizability nor scalability of computer-vision-based surgical guidance systems have been demonstrated, especially to geographic locations that lack hardware and infrastructure necessary for real-time inference. We propose a new equipment-agnostic framework for real-time use in operating suites. Using laparoscopic cholecystectomy and semantic segmentation models for predicting safe/dangerous (“Go”/”No-Go”) zones of dissection as an example use case, this study aimed to develop and test the performance of a novel data pipeline linked to a web-platform that enables real-time deployment from any edge device. To test this infrastructure and demonstrate its scalability and generalizability, lightweight U-Net and SegFormer models were trained on annotated frames from a large and diverse multicenter dataset from 136 institutions, and then tested on a separate prospectively collected dataset. A web-platform was created to enable real-time inference on any surgical video stream, and performance was tested on and optimized for a range of network speeds. The U-Net and SegFormer models respectively achieved mean Dice scores of 57% and 60%, precision 45% and 53%, and recall 82% and 75% for predicting the Go zone, and mean Dice scores of 76% and 76%, precision 68% and 68%, and recall 92% and 92% for predicting the No-Go zone. After optimization of the client-server interaction over the network, we deliver a prediction stream of at least 60 fps and with a maximum round-trip delay of 70 ms for speeds above 8 Mbps. Clinical deployment of machine learning models for surgical guidance is feasible and cost-effective using a generalizable, scalable and equipment-agnostic framework that lacks dependency on hardware with high computing performance or ultra-fast internet connection speed.
{"title":"Development, deployment and scaling of operating room-ready artificial intelligence for real-time surgical decision support","authors":"Sergey Protserov, Jaryd Hunter, Haochi Zhang, Pouria Mashouri, Caterina Masino, Michael Brudno, Amin Madani","doi":"10.1038/s41746-024-01225-2","DOIUrl":"10.1038/s41746-024-01225-2","url":null,"abstract":"Deep learning for computer vision can be leveraged for interpreting surgical scenes and providing surgeons with real-time guidance to avoid complications. However, neither generalizability nor scalability of computer-vision-based surgical guidance systems have been demonstrated, especially to geographic locations that lack hardware and infrastructure necessary for real-time inference. We propose a new equipment-agnostic framework for real-time use in operating suites. Using laparoscopic cholecystectomy and semantic segmentation models for predicting safe/dangerous (“Go”/”No-Go”) zones of dissection as an example use case, this study aimed to develop and test the performance of a novel data pipeline linked to a web-platform that enables real-time deployment from any edge device. To test this infrastructure and demonstrate its scalability and generalizability, lightweight U-Net and SegFormer models were trained on annotated frames from a large and diverse multicenter dataset from 136 institutions, and then tested on a separate prospectively collected dataset. A web-platform was created to enable real-time inference on any surgical video stream, and performance was tested on and optimized for a range of network speeds. The U-Net and SegFormer models respectively achieved mean Dice scores of 57% and 60%, precision 45% and 53%, and recall 82% and 75% for predicting the Go zone, and mean Dice scores of 76% and 76%, precision 68% and 68%, and recall 92% and 92% for predicting the No-Go zone. After optimization of the client-server interaction over the network, we deliver a prediction stream of at least 60 fps and with a maximum round-trip delay of 70 ms for speeds above 8 Mbps. Clinical deployment of machine learning models for surgical guidance is feasible and cost-effective using a generalizable, scalable and equipment-agnostic framework that lacks dependency on hardware with high computing performance or ultra-fast internet connection speed.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01225-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142126189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-27DOI: 10.1038/s41746-024-01224-3
Lena Christine Zuchowski, Matthias Lukas Zuchowski, Eckhard Nagel
The importance of a trust-based relationship between patients and medical professionals has been recognized as one of the most important predictors of treatment success and patients’ satisfaction. We have developed a novel legal, social and regulatory envelopment of medical AI that is explicitly based on the preservation of trust between patients and medical professionals. We require that the envelopment fosters reliance on the medical AI by both patients and medical professionals. Focusing on this triangle of desirable attitudes allows us to develop eight envelopment components that will support, strengthen and preserve these attitudes. We then demonstrate how each envelopment component can be enacted during different stages of the systems development life cycle and demonstrate that this requires the involvement of medical professionals and patients at the earliest stages of the life cycle. Therefore, this framework requires medical AI start-ups to cooperate with medical professionals and patients throughout.
{"title":"A trust based framework for the envelopment of medical AI","authors":"Lena Christine Zuchowski, Matthias Lukas Zuchowski, Eckhard Nagel","doi":"10.1038/s41746-024-01224-3","DOIUrl":"10.1038/s41746-024-01224-3","url":null,"abstract":"The importance of a trust-based relationship between patients and medical professionals has been recognized as one of the most important predictors of treatment success and patients’ satisfaction. We have developed a novel legal, social and regulatory envelopment of medical AI that is explicitly based on the preservation of trust between patients and medical professionals. We require that the envelopment fosters reliance on the medical AI by both patients and medical professionals. Focusing on this triangle of desirable attitudes allows us to develop eight envelopment components that will support, strengthen and preserve these attitudes. We then demonstrate how each envelopment component can be enacted during different stages of the systems development life cycle and demonstrate that this requires the involvement of medical professionals and patients at the earliest stages of the life cycle. Therefore, this framework requires medical AI start-ups to cooperate with medical professionals and patients throughout.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01224-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142081109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-27DOI: 10.1038/s41746-024-01195-5
Agata Blasiak, Lester W. J. Tan, Li Ming Chong, Xavier Tadeo, Anh T. L. Truong, Kirthika Senthil Kumar, Yoann Sapanel, Michelle Poon, Raghav Sundar, Sanjay de Mel, Dean Ho
The digital revolution in healthcare, amplified by the COVID-19 pandemic and artificial intelligence (AI) advances, has led to a surge in the development of digital technologies. However, integrating digital health solutions, especially AI-based ones, in rare diseases like Waldenström macroglobulinemia (WM) remains challenging due to limited data, among other factors. CURATE.AI, a clinical decision support system, offers an alternative to big data approaches by calibrating individual treatment profiles based on that individual’s data alone. We present a case study from the PRECISE CURATE.AI trial with a WM patient, where, over two years, CURATE.AI provided dynamic Ibrutinib dose recommendations to clinicians (users) aimed at achieving optimal IgM levels. An 80-year-old male with newly diagnosed WM requiring treatment due to anemia was recruited to the trial for CURATE.AI-based dosing of the Bruton tyrosine kinase inhibitor Ibrutinib. The primary and secondary outcome measures were focused on scientific and logistical feasibility. Preliminary results underscore the platform’s potential in enhancing user and patient engagement, in addition to clinical efficacy. Based on a two-year-long patient enrollment into the CURATE.AI-augmented treatment, this study showcases how AI-enabled tools can support the management of rare diseases, emphasizing the integration of AI to enhance personalized therapy.
{"title":"Personalized dose selection for the first Waldenström macroglobulinemia patient on the PRECISE CURATE.AI trial","authors":"Agata Blasiak, Lester W. J. Tan, Li Ming Chong, Xavier Tadeo, Anh T. L. Truong, Kirthika Senthil Kumar, Yoann Sapanel, Michelle Poon, Raghav Sundar, Sanjay de Mel, Dean Ho","doi":"10.1038/s41746-024-01195-5","DOIUrl":"10.1038/s41746-024-01195-5","url":null,"abstract":"The digital revolution in healthcare, amplified by the COVID-19 pandemic and artificial intelligence (AI) advances, has led to a surge in the development of digital technologies. However, integrating digital health solutions, especially AI-based ones, in rare diseases like Waldenström macroglobulinemia (WM) remains challenging due to limited data, among other factors. CURATE.AI, a clinical decision support system, offers an alternative to big data approaches by calibrating individual treatment profiles based on that individual’s data alone. We present a case study from the PRECISE CURATE.AI trial with a WM patient, where, over two years, CURATE.AI provided dynamic Ibrutinib dose recommendations to clinicians (users) aimed at achieving optimal IgM levels. An 80-year-old male with newly diagnosed WM requiring treatment due to anemia was recruited to the trial for CURATE.AI-based dosing of the Bruton tyrosine kinase inhibitor Ibrutinib. The primary and secondary outcome measures were focused on scientific and logistical feasibility. Preliminary results underscore the platform’s potential in enhancing user and patient engagement, in addition to clinical efficacy. Based on a two-year-long patient enrollment into the CURATE.AI-augmented treatment, this study showcases how AI-enabled tools can support the management of rare diseases, emphasizing the integration of AI to enhance personalized therapy.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01195-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142081111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-27DOI: 10.1038/s41746-024-01221-6
Jelena Schmidt, Nienke M. Schutte, Stefan Buttigieg, David Novillo-Ortiz, Eric Sutherland, Michael Anderson, Bart de Witte, Michael Peolsson, Brigid Unim, Milena Pavlova, Ariel Dora Stern, Elias Mossialos, Robin van Kessel
Regulatory frameworks for artificial intelligence (AI) are needed to mitigate risks while ensuring the ethical, secure, and effective implementation of AI technology in healthcare and population health. In this article, we present a synthesis of 141 binding policies applicable to AI in healthcare and population health in the EU and 10 European countries. The EU AI Act sets the overall regulatory framework for AI, while other legislations set social, health, and human rights standards, address the safety of technologies and the implementation of innovation, and ensure the protection and safe use of data. Regulation specifically pertaining to AI is still nascent and scarce, though a combination of data, technology, innovation, and health and human rights policy has already formed a baseline regulatory framework for AI in health. Future work should explore specific regulatory challenges, especially with respect to AI medical devices, data protection, and data enablement.
{"title":"Mapping the regulatory landscape for artificial intelligence in health within the European Union","authors":"Jelena Schmidt, Nienke M. Schutte, Stefan Buttigieg, David Novillo-Ortiz, Eric Sutherland, Michael Anderson, Bart de Witte, Michael Peolsson, Brigid Unim, Milena Pavlova, Ariel Dora Stern, Elias Mossialos, Robin van Kessel","doi":"10.1038/s41746-024-01221-6","DOIUrl":"10.1038/s41746-024-01221-6","url":null,"abstract":"Regulatory frameworks for artificial intelligence (AI) are needed to mitigate risks while ensuring the ethical, secure, and effective implementation of AI technology in healthcare and population health. In this article, we present a synthesis of 141 binding policies applicable to AI in healthcare and population health in the EU and 10 European countries. The EU AI Act sets the overall regulatory framework for AI, while other legislations set social, health, and human rights standards, address the safety of technologies and the implementation of innovation, and ensure the protection and safe use of data. Regulation specifically pertaining to AI is still nascent and scarce, though a combination of data, technology, innovation, and health and human rights policy has already formed a baseline regulatory framework for AI in health. Future work should explore specific regulatory challenges, especially with respect to AI medical devices, data protection, and data enablement.","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":null,"pages":null},"PeriodicalIF":12.4,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s41746-024-01221-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142081110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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