Artificial Intelligence-Assisted Auscultation of Heart Murmurs: Validation by Virtual Clinical Trial

2018 ◽  
Vol 40 (3) ◽  
pp. 623-629 ◽  
Author(s):  
W. Reid Thompson ◽  
Andreas J. Reinisch ◽  
Michael J. Unterberger ◽  
Andreas J. Schriefl
Keyword(s):  
Artificial Intelligence ◽  
Clinical Trial ◽  
Heart Murmurs

scholarly journals Guidance for Acupuncture Robot with Potentially Utilizing Medical Robotic Technologies

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Tiancheng Xu ◽  
Youbing Xia
Keyword(s):  
Artificial Intelligence ◽  
Clinical Trial ◽  
Binocular Vision ◽  
Future Development ◽  
Force Feedback ◽  
Development Trends ◽  
Real Acupuncture ◽  
The World ◽  
Coordination Framework ◽  
Robotic Technologies

Acupuncture is gaining increasing attention and recognition all over the world. However, a lot of physical labor is paid by acupuncturists. It is natural to resort to a robot which can improve the accuracy as well as the efficacy of therapy. Several teams have separately developed real acupuncture robots or related technologies and even went to the stage of clinical trial and then achieved success commercially. A completed clinical practical acupuncture robot is not far from reach with the combination of existing mature medical robotic technologies. A hand-eye-brain coordination framework is proposed in this review to integrate the potential utilizing technologies including force feedback, binocular vision, and automatic prescription. We should take acupuncture prescription with artificial intelligence and future development trends into account and make a feasible choice in development of modern acupuncture.

scholarly journals Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension

2020 ◽  
Vol 2 (10) ◽  
pp. e537-e548 ◽  
Author(s):  
Xiaoxuan Liu ◽  
Samantha Cruz Rivera ◽  
David Moher ◽  
Melanie J Calvert ◽  
Alastair K Denniston ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Clinical Trial ◽  
Reporting Guidelines

scholarly journals Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI Extension

BMJ ◽  
2020 ◽  
pp. m3164 ◽  
Author(s):  
Xiaoxuan Liu ◽  
Samantha Cruz Rivera ◽  
David Moher ◽  
Melanie J Calvert ◽  
Alastair K Denniston
Keyword(s):  
Artificial Intelligence ◽  
Clinical Trial ◽  
Clinical Trials ◽  
Clinical Trial Design ◽  
Trial Protocols ◽  
General Readership ◽  
Peer Reviewers ◽  
Multi Stakeholder ◽  
Analysis Of Error

Abstract The CONSORT 2010 (Consolidated Standards of Reporting Trials) statement provides minimum guidelines for reporting randomised trials. Its widespread use has been instrumental in ensuring transparency when evaluating new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate impact on health outcomes. The CONSORT-AI extension is a new reporting guideline for clinical trials evaluating interventions with an AI component. It was developed in parallel with its companion statement for clinical trial protocols: SPIRIT-AI. Both guidelines were developed through a staged consensus process, involving a literature review and expert consultation to generate 29 candidate items, which were assessed by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed on in a two-day consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The CONSORT-AI extension includes 14 new items, which were considered sufficiently important for AI interventions, that they should be routinely reported in addition to the core CONSORT 2010 items. CONSORT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention is integrated, the handling of inputs and outputs of the AI intervention, the human-AI interaction and providing analysis of error cases. CONSORT-AI will help promote transparency and completeness in reporting clinical trials for AI interventions. It will assist editors and peer-reviewers, as well as the general readership, to understand, interpret and critically appraise the quality of clinical trial design and risk of bias in the reported outcomes.

SYNERGY-AI: Artificial intelligence based precision oncology clinical trial matching and registry.

2019 ◽  
Vol 37 (4_suppl) ◽  
pp. TPS717-TPS717
Author(s):  
Selin Kurnaz ◽  
Arturo Loaiza-Bonilla ◽  
Jason Lawrence Freedman ◽  
Belisario Augusto Arango ◽  
Kristin Johnston ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Clinical Trial ◽  
Hematological Malignancies ◽  
Machine Learning Algorithms ◽  
Precision Oncology ◽  
Level Of Evidence ◽  
Dynamic Matching ◽  
Oncology Clinical Trial ◽  
Tumor Boards ◽  
The Impact

TPS717 Background: Precision oncology encompasses the implementation of high level of evidence disease-specific and biomarker-driven diagnostic and treatment recommendations for optimized cancer care. Artificial Intelligence (AI), telemedicine and value-based care may optimize clinical trial enrollment (CTE) and overall cost-benefit. This ongoing, international registry for cancer pts evaluates the feasibility and clinical utility of an AI-based precision oncology clinical trial matching tool, powered by a virtual tumor boards (VTB) program, and its clinical impact on pts with advanced cancer to facilitate CTE, as well as the financial impact, and potential outcomes of the intervention. Methods: The SYNERGY-AI Registry is an international prospective, observational cohort study of eligible adult and pediatric pts with advanced solid and hematological malignancies, for whom the decision to consider CTE has already been made by their primary providers (PP). Using a proprietary application programming interface (API) linked to existing electronic health records (EHR) platforms, individual clinical data is extracted, analyzed and matched to a parametric database of existing institutional and non-institutional CTs. Machine learning algorithms allow for dynamic matching based on CT allocation and availability for optimized matching. Patients voluntarily enroll into registry, which is non-interventional with no protocol-mandated tests/procedures—all treatment decisions are made at the discretion of PP in consultation with their pts, based on the AI CT matching report, and VTB support. CTE will be assessed on variables including biomarkers, barriers to enrollment. Study duration anticipated as ~36 mo (~24-mo enrollment followed by 12 mo of data collection, to occur every 3 mo). The primary analysis will be performed 12 mo after last pt enrolled. The impact time to initiation of CTE on PFS and OS will be estimated by Kaplan-Meier and Cox multivariable survival analysis. Enrollment is ongoing, with a target of ≥ 1500 patients. Key inclusion criteria: Pts with solid and hematological malignancies; cancer-related biomarkers. Key exclusion criteria: ECOG PS > 2; abnormal organ function; hospice enrollment Clinical trial information: NCT03452774.

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