Alexandra Crew, Claire Reidy, Helene-Mari van der Westhuizen, Mackenzie Graham
{"title":"人工智能诊断糖尿病视网膜病变的伦理问题综述》。","authors":"Alexandra Crew, Claire Reidy, Helene-Mari van der Westhuizen, Mackenzie Graham","doi":"10.1111/jep.14237","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>Diabetic retinopathy is one of the leading causes of avoidable blindness among adults globally, and screening programmes can enable early diagnosis and prevention of progression. Artificial intelligence (AI) diagnostic solutions have been developed to diagnose diabetic retinopathy. The aim of this review is to identify ethical concerns related to AI-enabled diabetic retinopathy diagnostics and enable future research to explore these issues further.</p><p><strong>Methods: </strong>This is a narrative review that uses thematic analysis methods to develop key findings. We searched two databases, PubMed and Scopus, for papers focused on the intersection of AI, diagnostics, ethics, and diabetic retinopathy and conducted a citation search. Primary research articles published in English between 1 January 2013 and 14 June 2024 were included. From the 1878 papers that were screened, nine papers met inclusion and exclusion criteria and were selected for analysis.</p><p><strong>Results: </strong>We found that existing literature highlights ensuring patient data has appropriate protection and ownership, that bias in algorithm training data is minimised, informed patient decision-making is encouraged, and negative consequences in the context of clinical practice are mitigated.</p><p><strong>Conclusions: </strong>While the technical developments in AI-enabled diabetic retinopathy diagnostics receive the bulk of the research focus, we found that insufficient attention is paid to how this technology is accessed equitably in different settings and which safeguards are needed against exploitative practices. Such ethical issues merit additional exploration and practical problem-solving through primary research. AI-enabled diabetic retinopathy screening has the potential to enable screening at a scale that was previously not possible and could contribute to reducing preventable blindness. It will only achieve this if ethical issues are emphasised, understood, and addressed throughout the translation of this technology to clinical practice.</p>","PeriodicalId":15997,"journal":{"name":"Journal of evaluation in clinical practice","volume":" ","pages":""},"PeriodicalIF":2.1000,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Narrative Review of Ethical Issues in the Use of Artificial Intelligence Enabled Diagnostics for Diabetic Retinopathy.\",\"authors\":\"Alexandra Crew, Claire Reidy, Helene-Mari van der Westhuizen, Mackenzie Graham\",\"doi\":\"10.1111/jep.14237\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>Diabetic retinopathy is one of the leading causes of avoidable blindness among adults globally, and screening programmes can enable early diagnosis and prevention of progression. Artificial intelligence (AI) diagnostic solutions have been developed to diagnose diabetic retinopathy. The aim of this review is to identify ethical concerns related to AI-enabled diabetic retinopathy diagnostics and enable future research to explore these issues further.</p><p><strong>Methods: </strong>This is a narrative review that uses thematic analysis methods to develop key findings. We searched two databases, PubMed and Scopus, for papers focused on the intersection of AI, diagnostics, ethics, and diabetic retinopathy and conducted a citation search. Primary research articles published in English between 1 January 2013 and 14 June 2024 were included. From the 1878 papers that were screened, nine papers met inclusion and exclusion criteria and were selected for analysis.</p><p><strong>Results: </strong>We found that existing literature highlights ensuring patient data has appropriate protection and ownership, that bias in algorithm training data is minimised, informed patient decision-making is encouraged, and negative consequences in the context of clinical practice are mitigated.</p><p><strong>Conclusions: </strong>While the technical developments in AI-enabled diabetic retinopathy diagnostics receive the bulk of the research focus, we found that insufficient attention is paid to how this technology is accessed equitably in different settings and which safeguards are needed against exploitative practices. Such ethical issues merit additional exploration and practical problem-solving through primary research. AI-enabled diabetic retinopathy screening has the potential to enable screening at a scale that was previously not possible and could contribute to reducing preventable blindness. It will only achieve this if ethical issues are emphasised, understood, and addressed throughout the translation of this technology to clinical practice.</p>\",\"PeriodicalId\":15997,\"journal\":{\"name\":\"Journal of evaluation in clinical practice\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2024-11-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of evaluation in clinical practice\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1111/jep.14237\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"HEALTH CARE SCIENCES & SERVICES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of evaluation in clinical practice","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1111/jep.14237","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
A Narrative Review of Ethical Issues in the Use of Artificial Intelligence Enabled Diagnostics for Diabetic Retinopathy.
Introduction: Diabetic retinopathy is one of the leading causes of avoidable blindness among adults globally, and screening programmes can enable early diagnosis and prevention of progression. Artificial intelligence (AI) diagnostic solutions have been developed to diagnose diabetic retinopathy. The aim of this review is to identify ethical concerns related to AI-enabled diabetic retinopathy diagnostics and enable future research to explore these issues further.
Methods: This is a narrative review that uses thematic analysis methods to develop key findings. We searched two databases, PubMed and Scopus, for papers focused on the intersection of AI, diagnostics, ethics, and diabetic retinopathy and conducted a citation search. Primary research articles published in English between 1 January 2013 and 14 June 2024 were included. From the 1878 papers that were screened, nine papers met inclusion and exclusion criteria and were selected for analysis.
Results: We found that existing literature highlights ensuring patient data has appropriate protection and ownership, that bias in algorithm training data is minimised, informed patient decision-making is encouraged, and negative consequences in the context of clinical practice are mitigated.
Conclusions: While the technical developments in AI-enabled diabetic retinopathy diagnostics receive the bulk of the research focus, we found that insufficient attention is paid to how this technology is accessed equitably in different settings and which safeguards are needed against exploitative practices. Such ethical issues merit additional exploration and practical problem-solving through primary research. AI-enabled diabetic retinopathy screening has the potential to enable screening at a scale that was previously not possible and could contribute to reducing preventable blindness. It will only achieve this if ethical issues are emphasised, understood, and addressed throughout the translation of this technology to clinical practice.
期刊介绍：
The Journal of Evaluation in Clinical Practice aims to promote the evaluation and development of clinical practice across medicine, nursing and the allied health professions. All aspects of health services research and public health policy analysis and debate are of interest to the Journal whether studied from a population-based or individual patient-centred perspective. Of particular interest to the Journal are submissions on all aspects of clinical effectiveness and efficiency including evidence-based medicine, clinical practice guidelines, clinical decision making, clinical services organisation, implementation and delivery, health economic evaluation, health process and outcome measurement and new or improved methods (conceptual and statistical) for systematic inquiry into clinical practice. Papers may take a classical quantitative or qualitative approach to investigation (or may utilise both techniques) or may take the form of learned essays, structured/systematic reviews and critiques.
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