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.

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

2020 ◽  
Vol 26 (9) ◽  
pp. 1364-1374 ◽  
Author(s):  
Xiaoxuan Liu ◽  
◽  
Samantha Cruz Rivera ◽  
David Moher ◽  
Melanie J. Calvert ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Clinical Trial ◽  
Clinical Trials ◽  
Clinical Trial Design ◽  
Trial Protocols ◽  
General Readership ◽  
Peer Reviewers ◽  
Multi Stakeholder ◽  
Analysis Of Error

AbstractThe CONSORT 2010 statement provides minimum guidelines for reporting randomized trials. Its widespread use has been instrumental in ensuring transparency in the evaluation of 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 (Consolidated Standards of Reporting Trials–Artificial Intelligence) 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 (Standard Protocol Items: Recommendations for Interventional Trials–Artificial Intelligence). Both guidelines were developed through a staged consensus process involving 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 upon in a two-day consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The CONSORT-AI extension includes 14 new items that 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 provision of an 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.

scholarly journals Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension

2020 ◽  
Vol 26 (9) ◽  
pp. 1351-1363 ◽  
Author(s):  
Samantha Cruz Rivera ◽  
◽  
Xiaoxuan Liu ◽  
An-Wen Chan ◽  
Alastair K. Denniston ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Clinical Trial ◽  
Expert Consultation ◽  
Consensus Process ◽  
Output Data ◽  
Trial Protocols ◽  
General Readership ◽  
Peer Reviewers ◽  
Multi Stakeholder ◽  
Analysis Of Error

AbstractThe SPIRIT 2013 statement aims to improve the completeness of clinical trial protocol reporting by providing evidence-based recommendations for the minimum set of items to be addressed. This guidance has been instrumental in promoting transparent evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate their impact on health outcomes. The SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials–Artificial Intelligence) extension is a new reporting guideline for clinical trial protocols evaluating interventions with an AI component. It was developed in parallel with its companion statement for trial reports: CONSORT-AI (Consolidated Standards of Reporting Trials–Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 26 candidate items, which were consulted upon by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The SPIRIT-AI extension includes 15 new items that were considered sufficiently important for clinical trial protocols of AI interventions. These new items should be routinely reported in addition to the core SPIRIT 2013 items. SPIRIT-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 will be integrated, considerations for the handling of input and output data, the human–AI interaction and analysis of error cases. SPIRIT-AI will help promote transparency and completeness for clinical trial protocols for AI interventions. Its use will assist editors and peer reviewers, as well as the general readership, to understand, interpret and critically appraise the design and risk of bias for a planned clinical trial.

scholarly journals Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI Extension

BMJ ◽  
2020 ◽  
pp. m3210 ◽  
Author(s):  
Samantha Cruz Rivera ◽  
Xiaoxuan Liu ◽  
An-Wen Chan ◽  
Alastair K Denniston ◽  
Melanie J Calvert
Keyword(s):  
Artificial Intelligence ◽  
Clinical Trial ◽  
Expert Consultation ◽  
Consensus Process ◽  
Output Data ◽  
Trial Protocols ◽  
General Readership ◽  
Peer Reviewers ◽  
Multi Stakeholder ◽  
Analysis Of Error

Abstract The SPIRIT 2013 (The Standard Protocol Items: Recommendations for Interventional Trials) statement aims to improve the completeness of clinical trial protocol reporting, by providing evidence-based recommendations for the minimum set of items to be addressed. This guidance has been instrumental in promoting transparent evaluation of new interventions. More recently, there is a growing recognition that interventions involving artificial intelligence need to undergo rigorous, prospective evaluation to demonstrate their impact on health outcomes. The SPIRIT-AI extension is a new reporting guideline for clinical trials protocols evaluating interventions with an AI component. It was developed in parallel with its companion statement for trial reports: CONSORT-AI. Both guidelines were developed using a staged consensus process, involving a literature review and expert consultation to generate 26 candidate items, which were consulted on by an international multi-stakeholder group in a 2-stage Delphi survey (103 stakeholders), agreed on in a consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The SPIRIT-AI extension includes 15 new items, which were considered sufficiently important for clinical trial protocols of AI interventions. These new items should be routinely reported in addition to the core SPIRIT 2013 items. SPIRIT-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 will be integrated, considerations around the handling of input and output data, the human-AI interaction and analysis of error cases. SPIRIT-AI will help promote transparency and completeness for clinical trial protocols for AI interventions. Its use will assist editors and peer-reviewers, as well as the general readership, to understand, interpret and critically appraise the design and risk of bias for a planned clinical trial.

scholarly journals Reporting guidelines for clinical trials of artificial intelligence interventions: the SPIRIT-AI and CONSORT-AI guidelines

Trials ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Hussein Ibrahim ◽  
Xiaoxuan Liu ◽  
Samantha Cruz Rivera ◽  
David Moher ◽  
An-Wen Chan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Clinical Trial ◽  
Clinical Trials ◽  
International Standards ◽  
Health Interventions ◽  
Reporting Guidelines ◽  
Traditional Health ◽  
Trial Protocols ◽  
International Experts

Abstract Background The application of artificial intelligence (AI) in healthcare is an area of immense interest. The high profile of ‘AI in health’ means that there are unusually strong drivers to accelerate the introduction and implementation of innovative AI interventions, which may not be supported by the available evidence, and for which the usual systems of appraisal may not yet be sufficient. Main text We are beginning to see the emergence of randomised clinical trials evaluating AI interventions in real-world settings. It is imperative that these studies are conducted and reported to the highest standards to enable effective evaluation because they will potentially be a key part of the evidence that is used when deciding whether an AI intervention is sufficiently safe and effective to be approved and commissioned. Minimum reporting guidelines for clinical trial protocols and reports have been instrumental in improving the quality of clinical trials and promoting completeness and transparency of reporting for the evaluation of new health interventions. The current guidelines—SPIRIT and CONSORT—are suited to traditional health interventions but research has revealed that they do not adequately address potential sources of bias specific to AI systems. Examples of elements that require specific reporting include algorithm version and the procedure for acquiring input data. In response, the SPIRIT-AI and CONSORT-AI guidelines were developed by a multidisciplinary group of international experts using a consensus building methodological process. The extensions include a number of new items that should be reported in addition to the core items. Each item, where possible, was informed by challenges identified in existing studies of AI systems in health settings. Conclusion The SPIRIT-AI and CONSORT-AI guidelines provide the first international standards for clinical trials of AI systems. The guidelines are designed to ensure complete and transparent reporting of clinical trial protocols and reports involving AI interventions and have the potential to improve the quality of these clinical trials through improvements in their design and delivery. Their use will help to efficiently identify the safest and most effective AI interventions and commission them with confidence for the benefit of patients and the public.

Cancer

2021 ◽  
pp. 627-638
Author(s):  
Rashi Rai ◽  
Prudhvilal Bhukya ◽  
Muneesh Kumar Barman ◽  
Meenakshi Singh ◽  
Kailash Chand ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Clinical Trial ◽  
Clinical Trials ◽  
Trial Design ◽  
Clinical Trial Design ◽  
Success Rates ◽  
Design Strategies ◽  
Safety Level ◽  
Key Steps ◽  
The Impact

Clinical trials are essential to govern the impact of a new possible treatment. It is utilized to determine the safety level and efficacy of a certain treatment. Clinical trial studies in cancer have provided successful treatment leading to longer survival span in the patients. The design of clinical trials for cancer has been done to find new ways to prevent, diagnose, treat, and manage symptoms of the disease. This chapter will provide detailed information on different aspects of clinical trials in cancer research. Protocols outlining the design and method to conduct a clinical trial in each phase will be discussed. The process and the conditions applied in each phase (I, II, and III) will be described precisely. The design of trials done in every aspect such as prevention, immunochemotherapy, diagnosis, and treatment to combat cancer will be illustrated. Also, recent innovations in clinical design strategies and principles behind it as well as the use of recent advances in artificial intelligence in reshaping key steps of clinical trial design to increase trial success rates.

Health economic evaluations alongside clinical trials: A review of study protocols at the Swedish Medical Products Agency

2007 ◽  
Vol 23 (3) ◽  
pp. 392-396 ◽  
Author(s):  
Anne Lindfors ◽  
Nils Feltelius ◽  
Jonas Lundkvist
Keyword(s):  
Clinical Trial ◽  
Health Economic ◽  
Clinical Trials ◽  
Economic Evaluation ◽  
Economic Evaluations ◽  
Limited Information ◽  
Medical Products ◽  
Study Protocols ◽  
Trial Protocols

Objectives: Conducting economic evaluation in the context of clinical studies is common but has been the subject of extensive discussion due to its limitations. Various standard approaches and guidelines how such studies should be conducted have been proposed, but there is very limited information available about how common these studies are, what type of data that is collected, and how the quality of the protocols compares to the suggested standards. This study examines the prevalence and study design of health economic evaluations conducted alongside clinical trials in Sweden between 1995 and 2005.Methods: A systematic assessment of clinical trial protocols that had arrived as applications to the Medical Products Agency between 1995 and 2005 was performed. Only protocols arriving during the first half of odd years within the time period were included.Results: A total number of 680 protocols from 1995 to 2005 were examined, and among them, 14.4 percent included a health economic part. With the exception of year 2001, a trend toward an increased prevalence of economic evaluations next to clinical trials can be seen.Conclusions: This study shows that economic evaluations alongside clinical trials are becoming more common, although most trials still lack a health economic part of the protocol. The information about the economic evaluation provided in the protocols is in many cases scarce, possibly due to the fact that there currently are no generally accepted and applied guidelines for economic evaluations in clinical trial protocols. Introducing requirements for detailed study plans also for the economic evaluation should improve the quality of economic evaluations alongside clinical trials.

Digital Technology in Clinical Trials for Multiple Sclerosis: a systematic review. (Preprint)

2020 ◽  
Author(s):  
Marcello De Angelis ◽  
Luigi Lavorgna ◽  
Antonio Carotenuto ◽  
Martina Petruzzo ◽  
Roberta Lanzillo ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Clinical Trial ◽  
Clinical Trials ◽  
Outcome Measures ◽  
Digital Technology ◽  
Clinical Trial Design ◽  
Motor Rehabilitation ◽  
Robot Assisted ◽  
Future Directions ◽  
Treatment Side Effects

BACKGROUND Clinical trials in multiple sclerosis (MS) have leveraged the use of digital technology to overcome limitations in treatment and disease monitoring. OBJECTIVE To review the use of digital technology in concluded and ongoing MS clinical trials. METHODS In March 2020, we searched for “multiple sclerosis” and “trial” on pubmed.gov and clinicaltrials.gov using “app”, “digital”, “electronic”, “internet” and “mobile” as additional search words, separately. Overall, we included thirty-five studies. RESULTS Digital technology is part of clinical trial interventions to deliver psychotherapy and motor rehabilitation, with exergames, e-training, and robot-assisted exercises. Also, digital technology has become increasingly used to standardise previously existing outcome measures, with automatic acquisitions, reduced inconsistencies, and improved detection of symptoms. Some trials have been developing new patient-centred outcome measures for the detection of symptoms and of treatment side effects and adherence. CONCLUSIONS We will discuss how digital technology has been changing MS clinical trial design, and possible future directions for MS and neurology research.

scholarly journals Calling for improved quality in the registration of traditional Chinese medicine during the public health emergency: a survey of trial registries for COVID-19, H1N1, and SARS

Trials ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Zhuoran Kuang ◽  
◽  
Xiaoyan Li ◽  
Jianxiong Cai ◽  
Yaolong Chen ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Secondary Outcome ◽  
Specific Information ◽  
Clinical Trial Registry ◽  
Data Set ◽  
Diagnosis Criteria

Abstract Objective To assess the registration quality of traditional Chinese medicine (TCM) clinical trials for COVID-19, H1N1, and SARS. Method We searched for clinical trial registrations of TCM in the WHO International Clinical Trials Registry Platform (ICTRP) and Chinese Clinical Trial Registry (ChiCTR) on April 30, 2020. The registration quality assessment is based on the WHO Trial Registration Data Set (Version 1.3.1) and extra items for TCM information, including TCM background, theoretical origin, specific diagnosis criteria, description of intervention, and outcomes. Results A total of 136 records were examined, including 129 severe acute respiratory syndrome coronavirus 2 (COVID-19) and 7 H1N1 influenza (H1N1) patients. The deficiencies in the registration of TCM clinical trials (CTs) mainly focus on a low percentage reporting detailed information about interventions (46.6%), primary outcome(s) (37.7%), and key secondary outcome(s) (18.4%) and a lack of summary result (0%). For the TCM items, none of the clinical trial registrations reported the TCM background and rationale; only 6.6% provided the TCM diagnosis criteria or a description of the TCM intervention; and 27.9% provided TCM outcome(s). Conclusion Overall, although the number of registrations of TCM CTs increased, the registration quality was low. The registration quality of TCM CTs should be improved by more detailed reporting of interventions and outcomes, TCM-specific information, and sharing of the result data.

scholarly journals Conceptual Framework to Support Clinical Trial Optimization and End-to-End Enrollment Workflow

2019 ◽  
pp. 1-10 ◽  
Author(s):  
Neha M. Jain ◽  
Alison Culley ◽  
Teresa Knoop ◽  
Christine Micheel ◽  
Travis Osterman ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Knowledge Representation ◽  
Conceptual Framework ◽  
Trial Design ◽  
Clinical Trial Design ◽  
Prospective Clinical Trial ◽  
Future Trends ◽  
Current State ◽  
Evaluation Strategies

In this work, we present a conceptual framework to support clinical trial optimization and enrollment workflows and review the current state, limitations, and future trends in this space. This framework includes knowledge representation of clinical trials, clinical trial optimization, clinical trial design, enrollment workflows for prospective clinical trial matching, waitlist management, and, finally, evaluation strategies for assessing improvement.

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