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Directrices para presentación de informes de ensayos clínicos sobre intervenciones con inteligencia artificial: extensión CONSORT-AI / Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension / Diretrizes para relatórios de ensaios clínicos com intervenções que utilizam inteligência artificial: a extensão CONSORT-AI
Liu, Xiaoxuan; Cruz Rivera, Samantha; Moher, David; Calvert, Melanie J.; Denniston, Alastair K.; Denniston, Alastair K.; Chan, An-Wen; Darzi, Ara; Holmes, Christopher; Yau, Christopher; Moher, David; Ashrafian, Hutan; Deeks, Jonathan J.; Ferrante di Ruffano, Lavinia; Faes, Livia; Calvert, Melanie J.; Keane, Pearse A.; Cruz Rivera, Samantha; Vollmer, Sebastian J.; Liu, Xiaoxuan; Lee, Aaron Y.; Jonas, Adrian; Esteva, Andre; Beam, Andrew L.; Chan, An-Wen; Panico, Maria Beatrice; Lee, Cecilia S.; Haug, Charlotte; Kelly, Christophe J.; Yau, Christopher; Mulrow, Cynthia; Espinoza, Cyrus; Fletcher, John; Moher, David; Paltoo, Dina; Manna, Elaine; Price, Gary; Collins, Gary S.; Harvey, Hugh; Matcham, James; Monteiro, Joao; Khair ElZarrad, M.; Ferrante di Ruffano, Lavinia; Oakden-Rayner, Luke; Calvert, Melanie J.; McCradden, Melissa; Keane, Pearse A.; Savage, Richard; Golub, Robert; Sarkar, Rupa; Rowley, Samuel; Grupo de Trabajo SPIRIT-AI y CONSORT-AI; GRUPO DE DIRECCIÓN SPIRIT-AI Y CONSORT-AI; Grupo de Consenso SPIRIT-AI y CONSORT-AI.
Afiliação
  • Liu, Xiaoxuan; Moorfields Eye Hospital NHS Foundation Trust. Londres. GB
  • Cruz Rivera, Samantha; Birmingham Health Partners Centre for Regulatory Science and Innovation. University of Birmingham. Birmingham. GB
  • Moher, David; Centre for Journalology. Clinical Epidemiology Program. Ottawa Hospital Research Institute. Ottawa. CA
  • Calvert, Melanie J.; Health Data Research Reino Unido. Londres. GB
  • Denniston, Alastair K.; Academic Unit of Ophthalmology. Institute of Inflammation and Ageing. University of Birmingham. Birmingham. GB
  • Denniston, Alastair K.; Academic Unit of Ophthalmology. Institute of Inflammation and Ageing. University of Birmingham. Birmingham. GB
  • Chan, An-Wen; Department of Medicine. Womens College Research Institute. Womens College Hospital; University of Toronto. Toronto. CA
  • Darzi, Ara; Patient Safety Translational Research Centre. Imperial College London. Londres. GB
  • Holmes, Christopher; Alan Turing Institute. GB
  • Yau, Christopher; Alan Turing Institute. GB
  • Moher, David; Centre for Journalology. Clinical Epidemiology Program. Ottawa Hospital Research Institute. Ottawa. CA
  • Ashrafian, Hutan; Patient Safety Translational Research Centre. Imperial College London. Londres. GB
  • Deeks, Jonathan J.; Institute of Applied Health Research. University of Birmingham. Birmingham. GB
  • Ferrante di Ruffano, Lavinia; Institute of Applied Health Research. University of Birmingham. Birmingham. GB
  • Faes, Livia; Department of Ophthalmology. Cantonal Hospital Lucerne. Lucerne. CH
  • Calvert, Melanie J.; Health Data Research Reino Unido. Londres. GB
  • Keane, Pearse A.; NIHR Biomedical Research Center at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology. Londres. GB
  • Cruz Rivera, Samantha; Birmingham Health Partners Centre for Regulatory Science and Innovation. University of Birmingham. Birmingham. GB
  • Vollmer, Sebastian J.; Alan Turing Institute. GB
  • Liu, Xiaoxuan; Moorfields Eye Hospital NHS Foundation Trust. Londres. GB
  • Lee, Aaron Y.; Department of Ophthalmology. University of Washington. Seattle, WA. US
  • Jonas, Adrian; The National Institute for Health and Care Excellence. Londres. GB
  • Esteva, Andre; Salesforce Research. San Francisco, CA. US
  • Beam, Andrew L.; Harvard T.H. Chan School of Public Health. Boston, MA. US
  • Chan, An-Wen; Department of Medicine. Womens College Research Institute. Womens College Hospital; University of Toronto. Toronto. CA
  • Panico, Maria Beatrice; Medicines and Healthcare products Regulatory Agency. London. GB
  • Lee, Cecilia S.; Department of Ophthalmology. University of Washington. Seattle, WA. US
  • Haug, Charlotte; New England Journal of Medicine. Waltham, MA. US
  • Kelly, Christophe J.; Google Health. London. GB
  • Yau, Christopher; Alan Turing Institute. GB
  • Mulrow, Cynthia; Annals of Internal Medicine. Filadelfia, PA. US
  • Espinoza, Cyrus; Patient Partner. Birmingham. GB
  • Fletcher, John; British Medical Journal. Londres. GB
  • Moher, David; Centre for Journalology. Clinical Epidemiology Program. Ottawa Hospital Research Institute. Ottawa. CA
  • Paltoo, Dina; National Institutes of Health. Bethesda, MD. US
  • Manna, Elaine; Patient Partner. Londres. GB
  • Price, Gary; Patient Partner. Centre for Patient Reported Outcome Research. Institute of Applied Health Research; University of Birmingham. Birmingham. GB
  • Collins, Gary S.; Centre for Statistics in Medicine. University of Oxford. Oxford. GB
  • Harvey, Hugh; Hardian Health. Londres. GB
  • Matcham, James; AstraZeneca. Cambridge. GB
  • Monteiro, Joao; Nature Research. New York, NY. US
  • Khair ElZarrad, M.; Food and Drug Administration. Silver Spring, MD. US
  • Ferrante di Ruffano, Lavinia; Institute of Applied Health Research. University of Birmingham. Birmingham. GB
  • Oakden-Rayner, Luke; Australian Institute for Machine Learning. North Terrace, Adelaide. AU
  • Calvert, Melanie J.; Health Data Research Reino Unido. Londres. GB
  • McCradden, Melissa; The Hospital for Sick Children. Toronto. CA
  • Keane, Pearse A.; NIHR Biomedical Research Center at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology. Londres. GB
  • Savage, Richard; PinPoint Data Science. Leeds. GB
  • Golub, Robert; Journal of the American Medical Association. Chicago, IL. US
  • Sarkar, Rupa; The Lancet Group. Londres. GB
  • Rowley, Samuel; Medical Research Council. Londres. GB
Rev. panam. salud pública ; 48: e13, 2024. tab, graf
Article em Es | LILACS-Express | LILACS | ID: biblio-1536672
Biblioteca responsável: BR1.1
RESUMEN
resumen está disponible en el texto completo
ABSTRACT
ABSTRACT The 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.
RESUMO
RESUMO A declaração CONSORT 2010 apresenta diretrizes mínimas para relatórios de ensaios clínicos randomizados. Seu uso generalizado tem sido fundamental para garantir a transparência na avaliação de novas intervenções. Recentemente, tem-se reconhecido cada vez mais que intervenções que incluem inteligência artificial (IA) precisam ser submetidas a uma avaliação rigorosa e prospectiva para demonstrar seus impactos sobre os resultados de saúde. A extensão CONSORT-AI (Consolidated Standards of Reporting Trials - Artificial Intelligence) é uma nova diretriz para relatórios de ensaios clínicos que avaliam intervenções com um componente de IA. Ela foi desenvolvida em paralelo à sua declaração complementar para protocolos de ensaios clínicos, a SPIRIT-AI (Standard Protocol Items Recommendations for Interventional Trials - Artificial Intelligence). Ambas as diretrizes foram desenvolvidas por meio de um processo de consenso em etapas que incluiu revisão da literatura e consultas a especialistas para gerar 29 itens candidatos. Foram feitas consultas sobre esses itens a um grupo internacional composto por 103 interessados diretos, que participaram de uma pesquisa Delphi em duas etapas. Chegou-se a um acordo sobre os itens em uma reunião de consenso que incluiu 31 interessados diretos, e os itens foram refinados por meio de uma lista de verificação piloto que envolveu 34 participantes. A extensão CONSORT-AI inclui 14 itens novos que, devido à sua importância para as intervenções de IA, devem ser informados rotineiramente juntamente com os itens básicos da CONSORT 2010. A CONSORT-AI preconiza que os pesquisadores descrevam claramente a intervenção de IA, incluindo instruções e as habilidades necessárias para seu uso, o contexto no qual a intervenção de IA está inserida, considerações sobre o manuseio dos dados de entrada e saída da intervenção de IA, a interação humano-IA e uma análise dos casos de erro. A CONSORT-AI ajudará a promover a transparência e a integralidade nos relatórios de ensaios clínicos com intervenções que utilizam IA. Seu uso ajudará editores e revisores, bem como leitores em geral, a entender, interpretar e avaliar criticamente a qualidade do desenho do ensaio clínico e o risco de viés nos resultados relatados.

Texto completo: 1 Base de dados: LILACS Idioma: Es Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: LILACS Idioma: Es Ano de publicação: 2024 Tipo de documento: Article