IntroductionDue to a global shortage of healthcare workers, there is a lack of basic healthcare for 4 billion people worldwide, particularly affecting low-income and middle-income countries. The utilisation of AI-based healthcare tools such as symptom assessment applications (SAAs) has the potential to reduce the burden on healthcare systems. The purpose of the AFYA Study (AI-based Assessment oF health sYmptoms in TAnzania) is to evaluate the accuracy of the condition suggestions and urgency advice provided by a user on a Swahili language Ada SAA.Methods and analysisThis study is designed as an observational prospective clinical study. The setting is a waiting room of a Tanzanian district hospital. It will include patients entering the outpatient clinic with various conditions and age groups, including children and adolescents. Patients will be asked to use the SAA before proceeding to usual care. After usual care, they will have a consultation with a study-provided physician. Patients and healthcare practitioners will be blinded to the SAA’s results. An expert panel will compare the Ada SAA’s condition suggestions and urgency advice to usual care and study provided differential diagnoses and triage. The primary outcome measures are the accuracy and comprehensiveness of the Ada SAA evaluated against the gold standard differential diagnoses.Ethics and disseminationEthical approval was received by the ethics committee (EC) of Muhimbili University of Health and Allied Sciences with an approval number MUHAS-REC-09-2019-044 and the National Institute for Medical Research, NIMR/HQ/R.8c/Vol. I/922. All amendments to the protocol are reported and adapted on the basis of the requirements of the EC. The results from this study will be submitted to peer-reviewed journals, local and international stakeholders, and will be communicated in editorials/articles by Ada Health.Trial registration numberNCT04958577.
Background Low- and middle-income countries face difficulties in providing adequate health care. One of the reasons is a shortage of qualified health workers. Diagnostic decision support systems are designed to aid clinicians in their work and have the potential to mitigate pressure on health care systems. Objective The Artificial Intelligence–Based Assessment of Health Symptoms in Tanzania (AFYA) study will evaluate the potential of an English-language artificial intelligence–based prototype diagnostic decision support system for mid-level health care practitioners in a low- or middle-income setting. Methods This is an observational, prospective clinical study conducted in a busy Tanzanian district hospital. In addition to usual care visits, study participants will consult a mid-level health care practitioner, who will use a prototype diagnostic decision support system, and a study physician. The accuracy and comprehensiveness of the differential diagnosis provided by the diagnostic decision support system will be evaluated against a gold-standard differential diagnosis provided by an expert panel. Results Patient recruitment started in October 2021. Participants were recruited directly in the waiting room of the outpatient clinic at the hospital. Data collection will conclude in May 2022. Data analysis is planned to be finished by the end of June 2022. The results will be published in a peer-reviewed journal. Conclusions Most diagnostic decision support systems have been developed and evaluated in high-income countries, but there is great potential for these systems to improve the delivery of health care in low- and middle-income countries. The findings of this real-patient study will provide insights based on the performance and usability of a prototype diagnostic decision support system in low- or middle-income countries. Trial Registration ClinicalTrials.gov NCT04958577; http://clinicaltrials.gov/ct2/show/NCT04958577 International Registered Report Identifier (IRRID) DERR1-10.2196/34298
UNSTRUCTURED Background Africa has the lowest density of healthcare workers globally and digital tools using artificial intelligence (AI) could bridge that gap affordably. A partnership between Ada Health and Praekelt.org sought to integrate Ada for users in South Africa (SA). Challenges and solutions Three challenges were identified to improve the efficacy of health AI in the local setting: Localization: disease incidences and presentations of maternal and child health in SA differ from those in Europe and North America. We adapted Ada’s knowledge base to meet these challenges. For this project, 25 maternal and 25 pediatric health conditions were localized, and one new pediatric condition was created for users of the Ada app. This included adding region-specific incidences and adapting the knowledge base for variations in disease presentation. Readability: the existing readability score of Ada’s consumer-facing medical content was calculated using the Automated Readability Index as grade 11.0 (± 1.8, range = 5.8-17.5). Using Content Design London’s Readability Guidelines, the readability score of Ada’s content was lowered to below grade 8 (7.4 ± 0.8, range = 4.6-10.2) while maintaining medical accuracy. Different approaches to AI: most medical research is conducted in high-income countries and among people with high literacy levels, thereby leading to bias in the system. Using a white-box approach allows intelligent solutions to be delivered that incorporate the needs of the population, including in low and middle income countries. Conclusion By partnering with grassroots and local organizations, such as Praekelt.org, AI companies can reduce the burden on vulnerable healthcare systems, but should be designed and adapted to the needs of the local population to account for regional differences in incidences and disease presentations, and language barriers. Furthermore, wherever possible, efforts should be made to reduce bias in AI by using white-box systems. It is possible to use simple language for consumer-facing medical text without compromising on medical quality. With a few minor adaptations AI-technology can be localized to serve global public health needs.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.