The 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.
A clinical trial protocol is an essential document produced by study investigators detailing a priori the rationale, proposed methods and plans for how a clinical trial will be conducted1,2. This key document is used by external reviewers (funding agencies, regulatory bodies, research ethics committees, journal editors, peer reviewers, institutional review boards and, increasingly, the wider public) to understand and interpret the rationale, methodological rigor and ethical considerations of the trial. Additionally, trial protocols provide a shared reference point to support the research team in conducting a high-quality study.
Despite their importance, the quality and completeness of published trial protocols are variable1,2. The SPIRIT statement was published in 2013 to provide guidance for the minimum reporting content of a clinical-trial protocol and has been widely endorsed as an international standard3,4,5. The SPIRIT statement published in 2013 provides minimum guidance applicable for all clinical trial interventions but recognizes that certain interventions may require extension or elaboration of these items1,2. AI is an area of enormous interest, with strong drivers to accelerate new interventions through to publication, implementation and market6. While AI systems have been researched for some time, recent advances in deep learning and neural networks have gained considerable interest for their potential in health applications. Examples of such applications of these are wide ranging and include AI systems for screening and triage7,8, diagnosis9,10,11,12, prognostication13,14, decision support15 and treatment recommendation16. However, in most recent cases, the majority of published evidence has consisted of in silico, early-phase validation. It has been recognized that most recent AI studies are inadequately reported and existing reporting guidelines do not fully cover potential sources of bias specific to AI systems17. The welcome emergence of randomized controlled trials seeking to evaluate the clinical efficacy of newer interventions based on, or including, an AI component (called ‘AI interventions’ here)15,18,19,20,21,22,23 has similarly been met with concerns about design and reporting17,24,25,26. This has highlighted the need to provide reporting guidance that is ‘fit for purpose’ in this domain.
SPIRIT-AI (as part of the SPIRIT-AI and CONSORT-AI initiative) is an international initiative supported by SPIRIT and the EQUATOR (Enhancing the Quality and Transparency of Health Research) Network to extend or elaborate on the existing SPIRIT 2013 statement where necessary, to develop consensus-based AI-specific protocol guidance27,28. It is complementary to the CONSORT-AI statement, which aims to promote high-quality reporting of AI trials. This Consensus Statement describes the methods used to identify and evaluate candidate items and gain consensus. In addition, it also provides the full SPIRIT-AI checklist, including new items and their accompanying explanations.
The SPIRIT-AI and CONSORT-AI extensions were simultaneously developed for clinical trial protocols and trial reports. An announcement for the SPIRIT-AI and CONSORT-AI initiative was published in October 2019 (ref. 27), and the two guidelines were registered as reporting guidelines under development on the EQUATOR library of reporting guidelines in May 2019. Both guidelines were developed in accordance with the EQUATOR Network’s methodological framework29. The SPIRIT-AI and CONSORT-AI Steering Group, consisting of 15 international experts, was formed to oversee the conduct and methodology of the study. Definitions of key terms are provided in the glossary (Box 1).
This study was approved by the ethical review committee at the University of Birmingham, UK (ERN_19-1100). Participant information was provided to Delphi participants electronically before survey completion and before the consensus meeting. Delphi participants provided electronic informed consent, and written consent was obtained from consensus meeting participants.
Literature review and candidate item generation
An initial list of candidate items for the SPIRIT-AI and CONSORT-AI checklists was generated through review of the published literature and consultation with the Steering Group and known international experts. A search was performed on 13 May 2019 using the terms ‘artificial intelligence’, ‘machine learning’ and ‘deep learning’ to identify existing clinical trials for AI interventions listed within the US National Library of Medicine’s clinical trial registry (ClinicalTrials.gov). There were 316 registered trials, of which 62 were completed and 7 had published results22,30,31,32,33,34,35. Two studies were reported with reference to the CONSORT statement22,34, and one study provided an unpublished trial protocol34. The Operations Team (X.L., S.C.R., M.J.C. and A.K.D.) identified AI-specific considerations from these studies and reframed them as candidate reporting items. The candidate items were also informed by findings from a previous systematic review that evaluated the diagnostic accuracy of deep-learning systems for medical imaging17. After consultation with the Steering Group and additional international experts (n = 19), 29 candidate items were generated, 26 of which were relevant for both SPIRIT-AI and CONSORT-AI and 3 of which were relevant only for CONSORT-AI. The Operations Team mapped these items to the corresponding SPIRIT and CONSORT items, revising the wording and providing explanatory text as required to contextualize the items. These items were included in subsequent Delphi surveys.
Delphi consensus process
In September 2019, 169 key international experts were invited to participate in the online Delphi survey to vote upon the candidate items and suggest additional items. Experts were identified and contacted via the Steering Group and were allowed one round of ‘snowball’ recruitment in which contacted experts could suggest additional experts. In addition, individuals who made contact following publication of the announcement were included27. The Steering Group agreed that individuals with expertise in clinical trials and AI and machine learning (ML), as well as key users of the technology, should be well represented in the consultation. Stakeholders included healthcare professionals, methodologists, statisticians, computer scientists, industry representatives, journal editors, policy makers, health ‘informaticists’, experts in law and ethics, regulators, patients and funders. Participant characteristics are described in Supplementary Table 1. Two online Delphi surveys were conducted. DelphiManager software (version 4.0), developed and maintained by the COMET (Core Outcome Measures in Effectiveness Trials) initiative, was used to undertake the e-Delphi surveys. Participants were given written information about the study and were asked to provide their level of expertise within the fields of (i) AI/ML, and (ii) clinical trials. Each item was presented for consideration (26 for SPIRIT-AI and 29 for CONSORT-AI). Participants were asked to vote on each item using a 9-point scale, as follows: 1–3, not important; 4–6, important but not critical; and 7–9, important and critical. Respondents provided separate ratings for SPIRIT-AI and CONSORT-AI. There was an option to opt out of voting for each item, and each item included space for free text comments. At the end of the Delphi survey, participants had the opportunity to suggest new items. 103 responses were received for the first Delphi round, and 91 responses (88% of participants from round one) were received for the second round. The results of the Delphi surveys informed the subsequent international consensus meeting. 12 new items were proposed by the Delphi study participants and were added for discussion at the consensus meeting. Data collected during the Delphi survey were anonymized, and item-level results were presented at the consensus meeting for discussion and voting.
The two-day consensus meeting took place in January 2020 and was hosted by the University of Birmingham, UK, to seek consensus on the content of SPIRIT-AI and CONSORT-AI. 31 international stakeholders from among the Delphi survey participants were invited to discuss the items and vote on their inclusion. Participants were selected to achieve adequate representation from all the stakeholder groups. 38 items were discussed in turn, comprising the 26 items generated in the initial literature review and item-generation phase (these 26 items were relevant to both SPIRIT-AI and CONSORT-AI; 3 extra items relevant only to CONSORT-AI were also discussed) and the 12 new items proposed by participants during the Delphi surveys. Each item was presented to the consensus group, alongside its score from the Delphi exercise (median and interquartile ranges) and any comments made by Delphi participants related to that item. Consensus meeting participants were invited to comment on the importance of each item and whether the item should be included in the AI extension. In addition, participants were invited to comment on the wording of the explanatory text accompanying each item and the position of each item relative to the SPIRIT 2013 and CONSORT 2010 checklists. After open discussion of each item and the option to adjust wording, an electronic vote took place, with the option to include or exclude the item. An 80% threshold for inclusion was pre-specified and deemed reasonable by the Steering Group to demonstrate majority consensus. Each stakeholder voted anonymously using Turning Point voting pads (Turning Technologies, version 22.214.171.124).
Following the consensus meeting, attendees were given the opportunity to make final comments on the wording and agree that the updated SPIRIT-AI and CONSORT-AI items reflected discussions from the meeting. The Operations Team assigned each item as an extension or elaboration item on the basis of a decision tree and produced a penultimate draft of the SPIRIT-AI and CONSORT-AI checklists (Supplementary Fig. 1). A pilot of the penultimate checklists was conducted with 34 participants to ensure clarity of wording. Experts participating in the pilot included the following: (a) Delphi participants who did not attend the consensus meeting, and (b) external experts who had not taken part in the development process but who had reached out to the Steering Group after the Delphi study commenced. Final changes were made on wording only to improve clarity for readers, by the Operations Team (Supplementary Fig. 2).
SPIRIT-AI checklist items and explanation
The SPIRIT-AI extension recommends that, in conjunction with existing SPIRIT 2013 items, 15 items (12 extensions and 3 elaborations) should be addressed for trial protocols of AI interventions. These items were considered sufficiently important for clinical-trial protocols for AI interventions that they should be routinely reported in addition to the core SPIRIT 2013 checklist items. Table 1 lists the SPIRIT-AI items.
All 15 items included in the SPIRIT-AI Extension passed the threshold of 80% for inclusion at the consensus meeting. SPIRIT-AI 6a (i), SPIRIT-AI 11a (v) and SPIRIT-AI 22 each resulted from the merging of two items after discussion. SPIRIT-AI 11a (iii) did not fulfil the criteria for inclusion on the basis of its initial wording (73% vote to include); however, after extensive discussion and rewording, the consensus group unanimously supported a re-vote, at which point it passed the inclusion threshold (97% to include).
SPIRIT-AI 1 (i) Elaboration: Indicate that the intervention involves artificial intelligence/machine learning and specify the type of model
Indicating in the protocol title and/or abstract that the intervention involves a form of AI is encouraged, as it immediately identifies the intervention as an AI/ML intervention and also serves to facilitate indexing and searching of the trial protocol in bibliographic databases, registries and other online resources. The title should be understandable by a wide audience; therefore, a broader umbrella term such as ‘artificial intelligence’ or ‘machine learning’ is encouraged. More precise terms should be used in the abstract, rather than the title, unless they are broadly recognized as being a form of AI/ML. Specific terminology relating to the model type and architecture should be detailed in the abstract.
SPIRIT-AI 1 (ii) Elaboration: State the intended use of the AI intervention
The intended use of the AI intervention should be made clear in the protocol’s title and/or abstract. This should describe the purpose of the AI intervention and the disease context19,36. Some AI interventions may have multiple intended uses, or the intended use may evolve over time. Therefore, documenting this allows readers to understand the intended use of the algorithm at the time of the trial.
SPIRIT-AI 6a (i) Extension: Explain the intended use of the AI intervention in the context of the clinical pathway, including its purpose and its intended users (for example, healthcare professionals, patients, public)
In order to clarify how the AI intervention will fit into a clinical pathway, a detailed description of its role should be included in the protocol background. AI interventions may be designed to interact with different users, including healthcare professionals, patients and the public, and their roles can be wide-ranging (for example, the same AI intervention could theoretically be replacing, augmenting or adjudicating components of clinical decision-making). Clarifying the intended use of the AI intervention and its intended user helps readers understand the purpose for which the AI intervention will be evaluated in the trial.
SPIRIT-AI 6a (ii) Extension: Describe any pre-existing evidence for the AI intervention
Authors should describe in the protocol any pre-existing published evidence (with supporting references) or unpublished evidence relating to validation of the AI intervention or lack thereof. Consideration should be given to whether the evidence was for a use, setting and target population similar to that of the planned trial. This may include previous development of the AI model, internal and external validations and any modifications made before the trial.
Participants, interventions and outcomes
SPIRIT-AI 9 Extension: Describe the onsite and offsite requirements needed to integrate the AI intervention into the trial setting
There are limitations to the generalizability of AI algorithms, one of which is when they are used outside of their development environment37,38. AI systems are dependent on their operational environment, and the protocol should provide details of the hardware and software requirements to allow technical integration of the AI intervention at each study site. For example, it should be stated if the AI intervention requires vendor-specific devices, if there is a need for specialized computing hardware at each site, or if the sites must support cloud integration, particularly if this is vendor specific. If any changes to the algorithm are required at each study site as part of the implementation procedure (such as fine-tuning the algorithm on local data), then this process should also be clearly described.
SPIRIT-AI 10 (i) Elaboration: State the inclusion and exclusion criteria at the level of participants
The inclusion and exclusion criteria should be defined at the participant level as per usual practice in protocols of non-AI interventional trials. This is distinct from the inclusion and exclusion criteria made at the input data level, which are addressed in item 10 (ii).
SPIRIT-AI 10 (ii) Extension: State the inclusion and exclusion criteria at the level of the input data
‘Input data’ refers to the data required by the AI intervention to serve its purpose (for example, for a breast cancer diagnostic system, the input data could be the unprocessed or vendor-specific post-processing mammography scan upon which a diagnosis is being made; for an early-warning system, the input data could be physiological measurements or laboratory results from the electronic health record). The trial protocol should pre-specify if there are minimum requirements for the input data (such as image resolution, quality metrics or data format) that would determine pre-randomization eligibility. It should specify when, how and by whom this will be assessed. For example, if a participant met the eligibility criteria for lying flat for a CT scan as per item 10 (i), but the scan quality was compromised (for any given reason) to such a level that it is no longer fit for use by the AI system, this should be considered as an exclusion criterion at the input-data level. Note that where input data are acquired after randomization (addressed by SPIRIT-20c), any exclusion is considered to be from the analysis, not from enrollment (Fig. 1).
SPIRIT-AI 11a (i) Extension: State which version of the AI algorithm will be used
Similar to other forms of software as a medical device, AI systems are likely to undergo multiple iterations and updates in their lifespan. The protocol should state which version of the AI system will be used in the clinical trial and whether this is the same version that was used in previous studies that have been used to justify the study rationale. If applicable, the protocol should describe what has changed between the relevant versions and the rationale for the changes. Where available, the protocol should include a regulatory marking reference, such as an unique device identifier, that requires a new identifier for updated versions of the device39.
SPIRIT-AI 11a (ii) Extension: Specify the procedure for acquiring and selecting the input data for the AI intervention
The measured performance of any AI system may be critically dependent on the nature and quality of the input data40. The procedure for how input data will be handled, including data acquisition, selection and pre-processing before analysis by the AI system, should be provided. Completeness and transparency of this process is integral to feasibility assessment and to future replication of the intervention beyond the clinical trial. It will also help to identify whether input-data-handling procedures will be standardized across trial sites.
SPIRIT-AI 11a (iii) Extension: Specify the procedure for assessing and handling poor-quality or unavailable input data
As with SPIRIT-AI 10 (ii), ‘input data’ refers to the data required by the AI intervention to serve its purpose. As noted in item 10 (ii), the performance of AI systems may be compromised as a result of poor quality or missing input data41 (for example, excessive movement artifact on an electrocardiogram). The study protocol should specify if and how poor quality or unavailable input data will be identified and handled. The protocol should also specify a minimum standard required for the input data and the procedure for when the minimum standard is not met (including the impact on, or any changes to, the participant care pathway).
Poor quality or unavailable data can also affect non-AI interventions. For example, sub-optimal quality of a scan could affect a radiologist’s ability to interpret it and make a diagnosis. It is therefore important that this information is reported equally for the control intervention, where relevant. If this minimum quality standard is different from the inclusion criteria for input data used to assess eligibility pre-randomization, this should be stated.
SPIRIT-AI 11a (iv) Extension: Specify whether there is human–AI interaction in the handling of the input data, and what level of expertise is required for users
A description of the human–AI interface and the requirements for successful interaction when input data are handled should be provided. Examples include clinician-led selection of regions of interest from a histology slide that is then interpreted by an AI diagnostic system42, or an endoscopist’s selection of a colonoscopy video clips as input data for an algorithm designed to detect polyps21. A description of any planned user training and instructions for how users will handle the input data provides transparency and replicability of trial procedures. Poor clarity on the human–AI interface may lead to a lack of a standard approach and may carry ethical implications, particularly in the event of harm43,44. For example, it may become unclear whether an error case occurred due to human deviation from the instructed procedure, or if it was an error made by the AI system.
SPIRIT-AI 11a (v) Extension: Specify the output of the AI intervention
The output of the AI intervention should be clearly defined in the protocol. For example, an AI system may output a diagnostic classification or probability, a recommended action, an alarm alerting to an event, an instigated action in a closed-loop system (such as titration of drug infusions) or another output. The nature of the AI intervention’s output has direct implications on its usability and how it may lead to downstream actions and outcomes.
SPIRIT-AI 11a (vi) Extension: Explain the procedure for how the AI intervention’s outputs will contribute to decision-making or other elements of clinical practice
Since health outcomes may also critically depend on how humans interact with the AI intervention, the trial protocol should explain how the outputs of the AI system are used to contribute to decision-making or other elements of clinical practice. This should include adequate description of downstream interventions that can impact outcomes. As with SPIRIT-AI 11a (iv), any effects of human–AI interaction on the outputs should be described in detail, including the level of expertise required to understand the outputs and any training and/or instructions provided for this purpose. For example, a skin cancer detection system that produces a percentage likelihood as output should be accompanied by an explanation of how this output should be interpreted and acted upon by the user, specifying both the intended pathways (for example, skin lesion excision if the diagnosis is positive) and the thresholds for entry to these pathways (for example, skin lesion excision if the diagnosis is positive and the probability is >80%). The information produced by comparator interventions should be similarly described, alongside an explanation of how such information was used to arrive at clinical decisions for patient management, where relevant.
SPIRIT-AI 22 Extension: Specify any plans to identify and analyze performance errors. If there are no plans for this, explain why not
Reporting performance errors and failure case analysis is especially important for AI interventions. AI systems can make errors that may be hard to foresee but that, if allowed to be deployed at scale, could have catastrophic consequences45. Therefore, identifying cases of error and defining risk-mitigation strategies is important for informing when the intervention can be safely implemented, and for which populations. The protocol should specify whether there are any plans to analyze performance errors. If there are no plans for this, a justification should be included in the protocol.
Ethics and dissemination
SPIRIT-AI 29 Extension: State whether and how the AI intervention and/or its code can be accessed, including any restrictions to access or re-use
The protocol should make clear whether and how the AI intervention and/or its code can be accessed or re-used. This should include details about the license and any restrictions to access.
The SPIRIT-AI extension provides international consensus-based guidance on AI-specific information that should be reported in clinical trial protocols, alongside SPIRIT 2013 and other relevant SPIRIT extensions4,46. It comprises of 15 items: 3 elaborations to the existing SPIRIT 2013 guidance in the context of AI trials, and 12 new extensions. The guidance does not aim to be prescriptive about the methodological approach to AI trials; instead, it aims to promote transparency in reporting the design and methods of a clinical trial to facilitate understanding, interpretation and peer review.
A number of extension items relate to the intervention (items 11 (i)–11 (vi)), its setting (item 9) and intended role (item 6a (i)). Specific recommendations were made pertinent to AI systems related to algorithm version, input and output data, integration into trial settings, expertise of the users and protocol for acting upon the AI system’s recommendations. It was agreed that these details are critical for independent evaluation of the study protocol. Journal editors reported that despite the importance of these items, they are currently often missing from trial protocols and reports at the time of submission for publication, which provides further weight to their inclusion as specifically listed extension items.
A recurrent focus of the Delphi comments and consensus group discussion was the safety of AI systems. This is in recognition that these systems, unlike other health interventions, can unpredictably yield errors that are not easily detectable or explainable by human judgement. For example, changes to medical imaging that are invisible, or appear random, to the human eye may change the likelihood of the resultant diagnostic output entirely47,48. The concern is that given the theoretical ease with which AI systems could be deployed at scale, any unintended harmful consequences could be catastrophic. Two extension items were added to address this. SPIRIT-AI item 6a (ii) requires specification of the prior level of evidence for validation of the AI intervention. SPIRIT-AI item 22 requires specification of any plans to analyze performance errors, to emphasize the importance of anticipating systematic errors made by the algorithm and their consequences.
One topic that was raised in the Delphi survey responses and consensus meeting that is not included in the final guidelines is ‘continuously evolving’ AI systems (also known as ‘continuously adapting’ or ‘continuously learning’ AI systems). These are AI systems with the ability to continuously train on new data, which may cause changes in performance over time. The group noted that, while of interest, this field is relatively early in its development without tangible examples in healthcare applications, and that it would not be appropriate for it to be addressed by SPIRIT-AI at this stage49. This topic will be monitored and revisited in future iterations of SPIRIT-AI. It is worth noting that incremental software changes, whether continuous or iterative, intentional or unintentional, could have serious consequences on safety performance after deployment. It is therefore of vital importance that such changes are documented and identified by software version and that a robust post-deployment surveillance plan is in place.
This study is set in the current context of AI in health; therefore, several limitations should be noted. First, at the time of SPIRIT-AI development, there were only seven published trials and no published trial protocols in the field of AI for healthcare. Thus, the discussion and decisions made during the development of SPIRIT-AI are not always supported by existing real-world examples. This arises from our stated aim of addressing the issues of poor protocol development in this field as early as possible, recognizing the strong drivers in the field and the specific challenges of study design and reporting for AI. As the science and study of AI evolves, we welcome collaboration with investigators to co-evolve these reporting standards to ensure their continued relevance. Second, the literature search of AI randomized controlled trials used terminology such as ‘artificial intelligence’, ‘machine learning’ and ‘deep learning’, but not terms such as ‘clinical decision support systems’ and ‘expert systems’, which were more commonly used in the 1990s for technologies underpinned by AI systems and share risks similar to those of recent examples50. It is likely that such systems, if published today, would be indexed under ‘artificial intelligence’ or ‘machine learning’; however, clinical decision support systems were not actively discussed during this consensus process. Third, the initial candidate items list was generated by a relatively small group of experts consisting of Steering Group members and additional international experts. However, additional items from the wider Delphi group were taken forward for consideration by the consensus group, and no new items were suggested during the consensus meeting or post-meeting evaluation.
As with the SPIRIT statement, the SPIRIT-AI extension is intended as a minimum reporting guidance, and there are additional AI-specific considerations for trial protocols that may warrant consideration (Supplementary Table 2). This extension is aimed particularly at investigators planning or conducting clinical trials; however, it may also serve as useful guidance for developers of AI interventions in earlier validation stages of an AI system. Investigators seeking to report studies developing and validating the diagnostic and predictive properties of AI models should refer to TRIPOD-ML (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis–Machine Learning)24 and STARD-AI (Standards For Reporting Diagnostic Accuracy Studies–Artificial Intelligence)51, both of which are currently under development. Other potentially relevant guidelines, which are agnostic to study design, are registered with the EQUATOR network52. The SPIRIT-AI extension is expected to encourage careful early planning of AI interventions for clinical trials and this, in conjunction with CONSORT-AI, should help to improve the quality of trials for AI interventions.
There is widespread recognition that AI is a rapidly evolving field, and there will be the need to update SPIRIT-AI as the technology, and newer applications for it, develop. Currently, most applications of AI/ML involve disease detection, diagnosis and triage, and this is likely to have influenced the nature and prioritization of items within SPIRIT-AI. As wider applications that utilize ‘AI as therapy’ emerge, it will be important to re-evaluate SPIRIT-AI in the light of such studies. Additionally, advances in computational techniques and the ability to integrate them into clinical workflows will bring new opportunities for innovation that benefits patients. However, they may be accompanied by new challenges of study design and reporting to ensure transparency, minimize potential biases and ensure that the findings of such a study are trustworthy and the extent to which they may be generalizable. The SPIRIT-AI and CONSORT-AI Steering Group will continue to monitor the need for updates.
Data requests should be made to the corresponding author and release will be subject to consideration by the SPIRIT-AI and CONSORT-AI Steering Group.
Chan, A.-W. et al. SPIRIT 2013 statement: defining standard protocol items for clinical trials. Ann. Intern. Med. 158, 200–207 (2013).
Chan, A.-W. et al. SPIRIT 2013 explanation and elaboration: guidance for protocols of clinical trials. Br. Med. J. 346, e7586 (2013).
Sarkis-Onofre, R. et al. Use of guidelines to improve the quality and transparency of reporting oral health research. J. Dent. 43, 397–404 (2015).
Calvert, M. et al. Guidelines for inclusion of patient-reported outcomes in clinical trial protocols: The SPIRIT-PRO Extension. J. Am. Med. Assoc. 319, 483–494 (2018).
Dai, L. et al. Standard protocol items for clinical trials with traditional Chinese medicine 2018: recommendations, explanation and elaboration (SPIRIT-TCM Extension 2018). Chin. J. Integr. Med. 25, 71–79 (2019).
He, J. et al. The practical implementation of artificial intelligence technologies in medicine. Nat. Med. 25, 30–36 (2019).
McKinney, S. M. et al. International evaluation of an AI system for breast cancer screening. Nature 577, 89–94 (2020).
Abràmoff, M. D. et al. Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Invest. Ophthalmol. Vis. Sci. 57, 5200–5206 (2016).
De Fauw, J. et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat. Med. 24, 1342–1350 (2018).
Esteva, A. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115–118 (2017).
Rajpurkar, P. et al. Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists. PLoS Med. 15, e1002686 (2018).
Fleuren, L. M. et al. Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy. Intensive Care Med. 46, 383–400 (2020).
Yim, J. et al. Predicting conversion to wet age-related macular degeneration using deep learning. Nat. Med. 26, 892–899 (2020).
Kim, H., Goo, J. M., Lee, K. H., Kim, Y. T. & Park, C. M. Preoperative CT-based deep learning model for predicting disease-free survival in patients with lung adenocarcinomas. Radiology 296, 216–224 (2020).
Wang, P. et al. Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study. Gut 68, 1813–1819 (2019).
Tyler, N. S. et al. An artificial intelligence decision support system for the management of type 1 diabetes. Nat. Metab. 2, 612–619 (2020).
Liu, X. et al. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. Lancet Digit. Health 1, e271–e297 (2019).
Wu, L. et al. Randomised controlled trial of WISENSE, a real-time quality improving system for monitoring blind spots during esophagogastroduodenoscopy. Gut 68, 2161–2169 (2019).
Wijnberge, M. et al. Effect of a machine learning-derived early warning system for intraoperative hypotension vs standard care on depth and duration of intraoperative hypotension during elective noncardiac surgery: The HYPE randomized clinical trial. J. Am. Med. Assoc. 323, 1052–1060 (2020).
Gong, D. et al. Detection of colorectal adenomas with a real-time computer-aided system (ENDOANGEL): a randomised controlled study. Lancet Gastroenterol. Hepatol. 5, 352–361 (2020).
Wang, P. et al. Effect of a deep-learning computer-aided detection system on adenoma detection during colonoscopy (CADe-DB trial): a double-blind randomised study. Lancet Gastroenterol. Hepatol. 5, 343–351 (2020).
Lin, H. et al. Diagnostic efficacy and therapeutic decision-making capacity of an artificial intelligence platform for childhood cataracts in eye clinics: a multicentre randomized controlled trial. EClinicalMedicine 9, 52–59 (2019).
Su, J.-R. et al. Impact of a real-time automatic quality control system on colorectal polyp and adenoma detection: a prospective randomized controlled study (with videos). Gastrointest. Endosc. 91, 415–424.e4 (2020).
Collins, G. S. & Moons, K. G. M. Reporting of artificial intelligence prediction models. Lancet 393, 1577–1579 (2019).
Gregory, J., Welliver, S. & Chong, J. Top 10 reviewer critiques of radiology artificial intelligence (AI) articles: qualitative thematic analysis of reviewer critiques of machine learning/deep learning manuscripts submitted to JMRI. J. Magn. Reson. Imaging 52, 248–254 (2020).
Nagendran, M. et al. Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies. Br. Med. J. 368, m689 (2020).
CONSORT-AI and SPIRIT-AI Steering Group. Reporting guidelines for clinical trials evaluating artificial intelligence interventions are needed. Nat. Med. 25, 1467–1468 (2019).
Liu, X., Faes, L., Calvert, M. J. & Denniston, A. K. Extension of the CONSORT and SPIRIT statements. Lancet 394, 1225 (2019).
Moher, D., Schulz, K. F., Simera, I. & Altman, D. G. Guidance for developers of health research reporting guidelines. PLoS Med. 7, e1000217 (2010).
Caballero-Ruiz, E. et al. A web-based clinical decision support system for gestational diabetes: Automatic diet prescription and detection of insulin needs. Int. J. Med. Inform. 102, 35–49 (2017).
Kim, T. W. B., Gay, N., Khemka, A. & Garino, J. Internet-based exercise therapy using algorithms for conservative treatment of anterior knee pain: a pragmatic randomized controlled trial. JMIR Rehabil. Assist. Technol. 3, e12 (2016).
Labovitz, D. L., Shafner, L., Reyes Gil, M., Virmani, D. & Hanina, A. Using artificial intelligence to reduce the risk of nonadherence in patients on anticoagulation therapy. Stroke 48, 1416–1419 (2017).
Nicolae, A. et al. Evaluation of a machine-learning algorithm for treatment planning in prostate low-dose-rate brachytherapy. Int. J. Radiat. Oncol. Biol. Phys. 97, 822–829 (2017).
Voss, C. et al. Effect of wearable digital intervention for improving socialization in children with autism spectrum disorder: a randomized clinical trial. JAMA Pediatr. 173, 446–454 (2019).
Mendes-Soares, H. et al. Assessment of a personalized approach to predicting postprandial glycemic responses to food among individuals without diabetes. JAMA Netw. Open 2, e188102 (2019).
Choi, K. J. et al. Development and validation of a deep learning system for staging liver fibrosis by using contrast agent-enhanced CT images in the liver. Radiology 289, 688–697 (2018).
Kelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G. & King, D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med. 17, 195 (2019).
Pooch, E. H. P., Ballester, P. L. & Barros, R. C. Can we trust deep learning models diagnosis? The impact of domain shift in chest radiograph classification. arXiv http://arxiv.org/abs/1909.01940 (2019)
International Medical Device Regulators Forum. Unique device identification system (UDI System) application guide. http://www.imdrf.org/documents/documents.asp (2019).
Sabottke, C. F. & Spieler, B. M. The effect of image resolution on deep learning in radiography. Radiology: Artif. Intell. 2, e190015 (2020).
Heaven, D. Why deep-learning AIs are so easy to fool. Nature 574, 163–166 (2019).
Kiani, A. et al. Impact of a deep learning assistant on the histopathologic classification of liver cancer. NPJ Digit. Med. 3, 23 (2020).
Wiens, J. et al. Do no harm: a roadmap for responsible machine learning for health care. Nat. Med. 25, 1337–1340 (2019).
Habli, I., Lawton, T. & Porter, Z. Artificial intelligence in health care: accountability and safety. Bull. World Health Organ. https://www.who.int/bulletin/online_first/BLT.19.237487.pdf (2020).
Oakden-Rayner, L., Dunnmon, J., Carneiro, G. & Ré, C. Hidden stratification causes clinically meaningful failures in machine learning for medical imaging. arXiv http://arxiv.org/abs/1909.12475 (2019).
SPIRIT. Publications & Downloads. https://www.spirit-statement.org/publications-downloads/ (accessed 24 March 2020).
Zech, J. R. et al. Confounding variables can degrade generalization performance of radiological deep learning models. arXiv http://arxiv.org/abs/1807.00431 (2018).
Finlayson, S. G. et al. Adversarial attacks on medical machine learning. Science 363, 1287–1289 (2019).
Lee, C. S. & Lee, A. Y. Clinical applications of continual learning machine learning. Lancet Digit. Health 2, e279–e281 (2020).
Sutton, R. T. et al. An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ Digit. Med. 3, 17 (2020).
Sounderajah, V. et al. Developing specific reporting guidelines for diagnostic accuracy studies assessing AI interventions: The STARD-AI Steering Group. Nat. Med. 26, 807–808 (2020).
Talmon, J. et al. STARE-HI–Statement on reporting of evaluation studies in Health Informatics. Int. J. Med. Inform. 78, 1–9 (2009).
We thank the participants who were involved in the Delphi study and Pilot study (Supplementary Note); E. Marston (University of Birmingham, UK) for strategic support; and C. Radovanovic (University Hospitals Birmingham NHS Foundation Trust, UK) and A. Walker (University of Birmingham, UK) for administrative support. The views expressed in this publication are those of the authors, Delphi participants and stakeholder participants and may not represent the views of the broader stakeholder group or host institution. This work was funded by a Wellcome Trust Institutional Strategic Support Fund: Digital Health Pilot Grant Research England (part of UK Research and Innovation), Health Data Research UK and the Alan Turing Institute. The study was sponsored by the University of Birmingham, UK. The study funders and sponsors had no role in the design and conduct of the study; collection, management, analysis and interpretation of the data; preparation, review or approval of the manuscript; or decision to submit the manuscript for publication. M.J.C. is a National Institute for Health Research (NIHR) Senior Investigator and receives funding from the National Institute for Health Research (NIHR) Birmingham Biomedical Research Centre; the NIHR Surgical Reconstruction and Microbiology Research Centre and NIHR ARC West Midlands at the University of Birmingham and University Hospitals Birmingham NHS Foundation Trust; Health Data Research UK; Innovate UK (part of UK Research and Innovation); the Health Foundation; Macmillan Cancer Support; and UCB Pharma. A.D. and J.D. are also NIHR Senior Investigators. The views expressed in this article are those of the author(s) and not necessarily those of the NIHR, or the Department of Health and Social Care. S.J.V. receives funding from the Engineering and Physical Sciences Research Council, UK Research and Innovation (UKRI), Accenture, Warwick Impact Fund, Health Data Research UK and European Regional Development Fund. S.R. is an employee of the Medical Research Council (UKRI). D.M. is supported by a University of Ottawa Research Chair. A.B. is supported by a National Institutes of Health (NIH) award (7K01HL141771-02). M.K.E. is supported by the U.S. Food and Drug Administration (FDA), and D.P. is supported in part by the Office of the Director at the National Library of Medicine (NLM), US National Institutes of Health (NIH). This article may not be consistent with NIH and/or FDA’s views or policies. It reflects only the views and opinions of the authors.
M.J.C. has received personal fees from Astellas, Takeda, Merck, Daiichi Sankyo, Glaukos, GlaxoSmithKline and the Patient-Centered Outcomes Research Institute (PCORI) outside the submitted work. P.A.K. is a consultant for DeepMind Technologies, Roche, Novartis and Apellis, and has received speaker fees or travel support from Bayer, Allergan, Topcon and Heidelberg Engineering. C.J.K. is an employee of Google and owns Alphabet stock. A.E. is an employee of Salesforce CRM. R.S. is an employee of Pinpoint Science. J. Matcham was an employee of AstraZeneca at the time of this study. J. Monteiro is Chief Editor of the journal Nature Medicine; he has recused himself from any aspect of decision-making on this manuscript and played no part in the assignment of this manuscript to in-house editors or peer reviewers, and was also separated and blinded from the editorial process from submission inception to decision.
Peer review information Javier Carmona was the primary editor on this article, and managed its editorial process and peer review in collaboration with the rest of the editorial team.
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Cruz Rivera, S., Liu, X., Chan, AW. et al. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension. Nat Med 26, 1351–1363 (2020). https://doi.org/10.1038/s41591-020-1037-7
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