In low- and middle-income countries (LMICs), AI has been promoted as a potential means of strengthening healthcare systems by a growing number of publications. We aimed to evaluate the scope and nature of AI technologies in the specific context of LMICs. In this systematic scoping review, we used a broad variety of AI and healthcare search terms. Our literature search included records published between 1st January 2009 and 30th September 2021 from the Scopus, EMBASE, MEDLINE, Global Health and APA PsycInfo databases, and grey literature from a Google Scholar search. We included studies that reported a quantitative and/or qualitative evaluation of a real-world application of AI in an LMIC health context. A total of 10 references evaluating the application of AI in an LMIC were included. Applications varied widely, including: clinical decision support systems, treatment planning and triage assistants and health chatbots. Only half of the papers reported which algorithms and datasets were used in order to train the AI. A number of challenges of using AI tools were reported, including issues with reliability, mixed impacts on workflows, poor user friendliness and lack of adeptness with local contexts. Many barriers exists that prevent the successful development and adoption of well-performing, context-specific AI tools, such as limited data availability, trust and evidence of cost-effectiveness in LMICs. Additional evaluations of the use of AI in healthcare in LMICs are needed in order to identify their effectiveness and reliability in real-world settings and to generate understanding for best practices for future implementations. 

/pdf/artificial-intelligence-for-strengthening-healthcare-systems-2v5pt6l2.pdf

Artificial intelligence for strengthening healthcare systems in low- and middle-income countries: a systematic scoping review

This scoping review of randomised controlled trials on artificial intelligence (AI) in clinical practice reveals an expanding interest in AI across clinical specialties and locations. The USA and China are leading in the number of trials, with a focus on deep learning systems for medical imaging, particularly in gastroenterology and radiology. A majority of trials (70 [81%] of 86) report positive primary endpoints, primarily related to diagnostic yield or performance; however, the predominance of single-centre trials, little demographic reporting, and varying reports of operational efficiency raise concerns about the generalisability and practicality of these results. Despite the promising outcomes, considering the likelihood of publication bias and the need for more comprehensive research including multicentre trials, diverse outcome measures, and improved reporting standards is crucial. Future AI trials should prioritise patient-relevant outcomes to fully understand AI’s true effects and limitations in health care.

Randomised controlled trials evaluating artificial intelligence in clinical practice: a scoping review.

Global eye health is defined as the degree to which vision, ocular health, and function are maximised worldwide, thereby optimising overall wellbeing and quality of life. Improving eye health is a global priority as a key to unlocking human potential by reducing the morbidity burden of disease, increasing productivity, and supporting access to education. Although extraordinary progress fuelled by global eye health initiatives has been made over the last decade, there remain substantial challenges impeding further progress. The accelerated development of digital health and artificial intelligence (AI) applications provides an opportunity to transform eye health, from facilitating and increasing access to eye care to supporting clinical decision making with an objective, data-driven approach. Here, we explore the opportunities and challenges presented by digital health and AI in global eye health and describe how these technologies could be leveraged to improve global eye health. AI, telehealth, and emerging technologies have great potential, but require specific work to overcome barriers to implementation. We suggest that a global digital eye health task force could facilitate coordination of funding, infrastructural development, and democratisation of AI and digital health to drive progress forwards in this domain. 

Artificial intelligence and digital health in global eye health: opportunities and challenges.

Diabetic retinopathy can be prevented with screening and early detection. We hypothesized that autonomous artificial intelligence (AI) diabetic eye exams at the point-of-care would increase diabetic eye exam completion rates in a racially and ethnically diverse youth population. AI for Children's diabetiC Eye ExamS (NCT05131451) is a parallel randomized controlled trial that randomized youth (ages 8-21 years) with type 1 and type 2 diabetes to intervention (autonomous artificial intelligence diabetic eye exam at the point of care), or control (scripted eye care provider referral and education) in an academic pediatric diabetes center. The primary outcome was diabetic eye exam completion rate within 6 months. The secondary outcome was the proportion of participants who completed follow-through with an eye care provider if deemed appropriate. Diabetic eye exam completion rate was significantly higher (100%, 95%CI: 95.5%, 100%) in the intervention group (n = 81) than the control group (n = 83) (22%, 95%CI: 14.2%, 32.4%)(p < 0.001). In the intervention arm, 25/81 participants had an abnormal result, of whom 64% (16/25) completed follow-through with an eye care provider, compared to 22% in the control arm (p < 0.001). Autonomous AI increases diabetic eye exam completion rates in youth with diabetes. 

Autonomous artificial intelligence increases screening and follow-up for diabetic retinopathy in youth: the ACCESS randomized control trial

There is currently no national strategy or standardized approach to diabetic retinopathy screening in the Brazilian public health system, and multiple socioeconomic barriers prevent access to eye examination in Brazil's poorest regions.From September 2021 to March 2022 we carried out a pilot project with an artificial intelligence system for diabetic retinopathy screening, embedded in a portable retinal camera. Patients with a diagnosis of diabetes according to the municipality registry were invited to attend nearby clinics for screening on designated days. Trained health-care technicians acquired images which were automatically evaluated by the system, with instant remote evaluation by retinal specialists in selected cases.Our study was based in Sergipe State, located at a region with high illiteracy rates and no local availability of specialized retina care. The average number of laser treatments performed annually in the last 5 years is 126, for a total State population of 2.3 million.Even though screening was performed free of charge in a convenient location for patients, from a total 2052 eligible individuals, only 1083 attended for screening.Efforts to raise awareness on the condition screened and to provide health education for patients and local health-care personnel are fundamental for increased attendance. Tailoring screening systems to the local setting, such as determining the trade-off between sensitivity and specificity, is challenging in regions with no current benchmarks. Standards for retinopathy screening based on the strategies adopted by high-income countries may not be realistic in low- and middle-income countries.Le système de santé publique brésilien ne possède à ce jour ni stratégie nationale, ni approche standardisée pour le dépistage de la rétinopathie diabétique, et de nombreux obstacles socioéconomiques entravent l'accès à des examens ophtalmologiques dans les régions les plus défavorisées du Brésil.Entre septembre 2021 et mars 2022, nous avons mené un projet pilote où l'intelligence artificielle était intégrée dans une caméra rétinienne portable afin de dépister la rétinopathie diabétique. Les patients souffrant de diabète, selon le registre de la municipalité, ont été invités à se rendre dans les cliniques des environs pour un dépistage lors de journées prévues à cet effet. Les images réalisées par des techniciens de santé qualifiés ont été automatiquement évaluées par le dispositif et, dans certains cas, soumises à des spécialistes de la rétine pour examen.Notre étude s'est déroulée dans l'État de Sergipe, une région où le taux d'analphabétisme est élevé et qui ne dispose d'aucun établissement proposant une prise en charge des maladies rétiniennes. Sur une base annuelle, 126 traitements au laser ont été effectués en moyenne ces cinq dernières années, alors que l'État compte 2,3 millions d'habitants.Malgré la gratuité du dépistage et la proximité géographique avec les patients, ils n'ont été que 1083 à venir sur les 2052 individus éligibles au total.Des efforts sont nécessaires pour sensibiliser au dépistage de cette maladie et offrir une éducation sanitaire aux patients et aux soignants de la région, afin de garantir un meilleur taux de fréquentation. Adapter les systèmes de dépistage au contexte local, notamment en trouvant le juste milieu entre sensibilité et spécificité, représente un défi dans des régions actuellement sans repères. Les normes de dépistage de la rétinopathie diabétique inspirées des stratégies en vigueur dans les pays à revenu élevé pourraient se révéler irréalisables dans les pays à revenu faible et intermédiaire.En la actualidad, no existe una estrategia nacional ni un enfoque estandarizado para el cribado de la retinopatía diabética en el sistema sanitario público brasileño, y múltiples barreras socioeconómicas impiden el acceso a la exploración oftalmológica en las regiones más pobres de Brasil.De septiembre de 2021 a marzo de 2022, se llevó a cabo un proyecto piloto con un sistema de inteligencia artificial para el cribado de la retinopatía diabética, integrado en una cámara de retina portátil. Se invitó a los pacientes con diagnóstico de diabetes según el registro del municipio a acudir a las clínicas cercanas para el cribado en días determinados. Los técnicos sanitarios capacitados adquirieron imágenes que el sistema evaluó de manera automática, y los especialistas en retina realizaron una evaluación instantánea a distancia en casos seleccionados.Este estudio se realizó en el estado de Sergipe, ubicado en una región con altas tasas de analfabetismo y sin disponibilidad local de atención especializada en retina. El promedio de tratamientos con láser realizados al año en los últimos 5 años es de 126, para una población total del estado de 2,3 millones.A pesar de que el cribado se realizó de forma gratuita en un lugar conveniente para los pacientes, de un total de 2052 personas que cumplían los requisitos, solo 1083 acudieron al cribado.Para aumentar la asistencia, es fundamental realizar esfuerzos de sensibilización sobre la enfermedad que se estudia y proporcionar educación sanitaria a los pacientes y al personal sanitario local. La adaptación de los sistemas de cribado al entorno local, como la determinación del equilibrio entre la sensibilidad y la especificidad, es un reto en las regiones que carecen de puntos de referencia actuales. Los estándares para el cribado de la retinopatía a partir de las estrategias que adoptan los países de ingresos altos pueden no ser realistas en los países de ingresos bajos y medios.لا توجد حاليًا استراتيجية وطنية أو أسلوب قياسي لفحص اعتلال الشبكية السكري في نظام الصحة العامة البرازيلي، وهناك العديد من الحواجز الاجتماعية والاقتصادية التي تمنع الخضوع لفحص العين في المناطق الأكثر فقرًا في البرازيل.من سبتمبر/أيلول 2021 إلى مارس/آذار 2022، قمنا بتنفيذ مشروع تجريبي باستخدام نظام ذكاء اصطناعي لفحص اعتلال الشبكية السكري، مدمج في كاميرا شبكية محمولة. المرضى الذين تم تشخيص إصابتهم بمرض السكري وفقًا لسجل البلدية، تمت دعوتهم للحضور للعيادات القريبة للخضوع للفحص في أيام مقررة. حصل فنيو الرعاية الصحية المدربون على صور تم تقييمها أوتوماتيكيًا بواسطة النظام، مع تقييم فوري عن بُعد بواسطة متخصصين في شبكية العين في حالات مختارة.اتخذت دراستنا من ولاية سيرغيبي مركزًا لها، والواقعة في منطقة ترتفع فيها معدلات الأمية ولا تتوفر رعاية محلية متخصصة لشبكية العين. متوسط عدد العلاجات بالليزر التي تم إجراؤها سنويًا في آخر 5 سنوات هو 126، لإجمالي عدد سكان الولاية البالغ 2.3 مليون نسمة.على الرغم من إجراء الفحص مجانًا في مكان مناسب للمرضى، فمن إجمالي 2052 فردًا مؤهلاً، حضر 1083 فردًا فقط للفحص.إن الجهود المبذولة لزيادة الوعي بالحالة التي يتم فحصها، وتوفير التوعية الصحية للمرضى وموظفي الرعاية الصحية المحليين، تعتبر مطلبًا أساسيًا لزيادة الحضور. إن تصميم أنظمة الفحص وفقًا للمواقع المحلية، مثل تحديد المقايضة بين الحساسية والنوعية، يمثل تحديًا صعبًا في المناطق التي لا توجد بها معايير حالية. إن معايير فحص اعتلال الشبكية القائمة على الاستراتيجيات المعتمدة في الدول ذات الدخل المرتفع، قد لا تكون واقعية في الدول ذات الدخل المنخفض والدخل المتوسط.巴西公共卫生系统目前没有针对糖尿病视网膜病变筛查的国家战略或标准化方法，而且多种社会经济障碍阻碍了巴西最贫困地区进行眼科检查。.从 2021 年 9 月到 2022 年 3 月，我们开展了一个试点项目，将人工智能系统嵌入便携式视网膜摄像头，以便进行糖尿病视网膜病变筛查。市政登记处中诊断为糖尿病的患者被邀请在指定日期到附近的诊所参加筛查。训练有素的医疗技术人员获取系统自动评估的图像，视网膜专科医生对所选病例进行即时远程评估。.我们研究的对象是塞尔希培州，该州文盲率高，当地没有专门的视网膜护理服务。该州总人口 230 万，但在过去 5 年中，每年进行激光治疗的平均次数为 126 次。.尽管是在便于患者前往的地点免费进行筛查，但在 2052 名符合筛查条件的人中，只有 1083 人参加了筛查。.应努力提高人们对筛查疾病的认识，并为患者和当地卫生保健人员提供健康教育，这是提高筛查率的基础。结合当地状况量身制定筛查系统，例如，如何在当前没有基准的地区对敏感性和特异性进行权衡，这便是一项挑战。将高收入国家采用的视网膜筛查战略作为标准，对于中低收入国家来说可能并不实际。.В настоящее время в системе общественного здравоохранения Бразилии не существует национальной стратегии или стандартизированного подхода к скринингу на диабетическую ретинопатию, а многочисленные социально-экономические барьеры препятствуют доступу к офтальмологическому обследованию в беднейших регионах Бразилии.С сентября 2021 г. по март 2022 г. авторы реализовали экспериментальный проект с использованием системы искусственного интеллекта для скрининга на диабетическую ретинопатию, которая была встроена в портативную ретинальную камеру. Пациентов с диагнозом «диабет», чьи соответствующие данные были упомянуты в муниципальном реестре, приглашали посетить близлежащие клиники для проведения скрининга в назначенные дни. Обученные технические работники здравоохранения получали изображения, которые система автоматически оценивала, а в отдельных случаях ретинологи проводили мгновенную дистанционную оценку.Данное исследование проводилось в штате Сержипи, расположенном в регионе с высоким уровнем неграмотности и отсутствием специализированной офтальмологической помощи на местном уровне. Ежегодно за последние 5 лет здесь выполнялось в среднем 126 лазерных офтальмологических процедур при общей численности населения штата в 2,3 миллиона человек.Несмотря на то что скрининг проводился бесплатно в удобном для пациентов месте, из 2052 человек, соответствующих критериям, только 1083 человека посетили скрининг.Усилия, направленные на повышение осведомленности о подлежащем скринингу заболевании и медико-санитарное просвещение пациентов и местного медицинского персонала, имеют основополагающее значение для повышения посещаемости. Адаптация систем скрининга к местным условиям, например определение компромисса между чувствительностью и специфичностью, является сложной задачей в регионах, где нет действующих контрольных показателей. Стандарты скрининга на ретинопатию, основанные на стратегиях, принятых в странах с высоким уровнем дохода, могут оказаться нереалистичными в странах с низким и средним уровнем дохода. 

/pdf/feasibility-of-screening-for-diabetic-retinopathy-using-2ltzsava.pdf

Feasibility of screening for diabetic retinopathy using artificial intelligence, Brazil

PurposeThis trial was designed to determine if artificial intelligence (AI)-supported diabetic retinopathy (DR) screening improved referral uptake in Rwanda.DesignThe Rwanda Artificial Intelligence for Diabetic Retinopathy Screening (RAIDERS) study was an investigator-masked, parallel-group randomized controlled trial.ParticipantsPatients ≥ 18 years of age with known diabetes who required referral for DR based on AI interpretation.MethodsThe RAIDERS study screened for DR using retinal imaging with AI interpretation implemented at 4 facilities from March 2021 through July 2021. Eligible participants were assigned randomly (1:1) to immediate feedback of AI grading (intervention) or communication of referral advice after human grading was completed 3 to 5 days after the initial screening (control).Main Outcome MeasuresDifference between study groups in the rate of presentation for referral services within 30 days of being informed of the need for a referral visit.ResultsOf the 823 clinic patients who met inclusion criteria, 275 participants (33.4%) showed positive findings for referable DR based on AI screening and were randomized for inclusion in the trial. Study participants (mean age, 50.7 years; 58.2% women) were randomized to the intervention (n = 136 [49.5%]) or control (n = 139 [50.5%]) groups. No significant intergroup differences were found at baseline, and main outcome data were available for analyses for 100% of participants. Referral adherence was statistically significantly higher in the intervention group (70/136 [51.5%]) versus the control group (55/139 [39.6%]; P = 0.048), a 30.1% increase. Older age (odds ratio [OR], 1.04; 95% confidence interval [CI], 1.02–1.05; P < 0.0001), male sex (OR, 2.07; 95% CI, 1.22–3.51; P = 0.007), rural residence (OR, 1.79; 95% CI, 1.07–3.01; P = 0.027), and intervention group (OR, 1.74; 95% CI, 1.05–2.88; P = 0.031) were statistically significantly associated with acceptance of referral in multivariate analyses.ConclusionsImmediate feedback on referral status based on AI-supported screening was associated with statistically significantly higher referral adherence compared with delayed communications of results from human graders. These results provide evidence for an important benefit of AI screening in promoting adherence to prescribed treatment for diabetic eye care in sub-Saharan Africa. This trial was designed to determine if artificial intelligence (AI)-supported diabetic retinopathy (DR) screening improved referral uptake in Rwanda. The Rwanda Artificial Intelligence for Diabetic Retinopathy Screening (RAIDERS) study was an investigator-masked, parallel-group randomized controlled trial. Patients ≥ 18 years of age with known diabetes who required referral for DR based on AI interpretation. The RAIDERS study screened for DR using retinal imaging with AI interpretation implemented at 4 facilities from March 2021 through July 2021. Eligible participants were assigned randomly (1:1) to immediate feedback of AI grading (intervention) or communication of referral advice after human grading was completed 3 to 5 days after the initial screening (control). Difference between study groups in the rate of presentation for referral services within 30 days of being informed of the need for a referral visit. Of the 823 clinic patients who met inclusion criteria, 275 participants (33.4%) showed positive findings for referable DR based on AI screening and were randomized for inclusion in the trial. Study participants (mean age, 50.7 years; 58.2% women) were randomized to the intervention (n = 136 [49.5%]) or control (n = 139 [50.5%]) groups. No significant intergroup differences were found at baseline, and main outcome data were available for analyses for 100% of participants. Referral adherence was statistically significantly higher in the intervention group (70/136 [51.5%]) versus the control group (55/139 [39.6%]; P = 0.048), a 30.1% increase. Older age (odds ratio [OR], 1.04; 95% confidence interval [CI], 1.02–1.05; P < 0.0001), male sex (OR, 2.07; 95% CI, 1.22–3.51; P = 0.007), rural residence (OR, 1.79; 95% CI, 1.07–3.01; P = 0.027), and intervention group (OR, 1.74; 95% CI, 1.05–2.88; P = 0.031) were statistically significantly associated with acceptance of referral in multivariate analyses. Immediate feedback on referral status based on AI-supported screening was associated with statistically significantly higher referral adherence compared with delayed communications of results from human graders. These results provide evidence for an important benefit of AI screening in promoting adherence to prescribed treatment for diabetic eye care in sub-Saharan Africa. 

/pdf/impact-of-artificial-intelligence-assessment-of-diabetic-1vza3px5.pdf

Impact of Artificial Intelligence Assessment of Diabetic Retinopathy on Referral Service Uptake in a Low-Resource Setting

Background Evidence on the practical application of artificial intelligence (AI)-based diabetic retinopathy (DR) screening is needed. Methods Consented participants were screened for DR using retinal imaging with AI interpretation from March 2021 to June 2021 at four diabetes clinics in Rwanda. Additionally, images were graded by a UK National Health System-certified retinal image grader. DR grades based on the International Classification of Diabetic Retinopathy with a grade of 2.0 or higher were considered referable. The AI system was designed to detect optic nerve and macular anomalies outside of DR. A vertical cup to disc ratio of 0.7 and higher and/or macular anomalies recognised at a cut-off of 60% and higher were also considered referable by AI. Results Among 827 participants (59.6% women (n=493)) screened by AI, 33.2% (n=275) were referred for follow-up. Satisfaction with AI screening was high (99.5%, n=823), and 63.7% of participants (n=527) preferred AI over human grading. Compared with human grading, the sensitivity of the AI for referable DR was 92% (95% CI 0.863%, 0.968%), with a specificity of 85% (95% CI 0.751%, 0.882%). Of the participants referred by AI: 88 (32.0%) were for DR only, 109 (39.6%) for DR and an anomaly, 65 (23.6%) for an anomaly only and 13 (4.73%) for other reasons. Adherence to referrals was highest for those referred for DR at 53.4%. Conclusion DR screening using AI led to accurate referrals from diabetes clinics in Rwanda and high rates of participant satisfaction, suggesting AI screening for DR is practical and acceptable.

Feasibility and acceptance of artificial intelligence-based diabetic retinopathy screening in Rwanda

IDF21-0503 CHIldren Living with Diabetes See and Thrive with AI Review: CHILDSTAR

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Impact of Artificial Intelligence Assessment of Diabetic Retinopathy on Referral Service Uptake in a Low-Resource Setting

Feasibility and acceptance of artificial intelligence-based diabetic retinopathy screening in Rwanda

IDF21-0503 CHIldren Living with Diabetes See and Thrive with AI Review: CHILDSTAR