Lisa O'Donnell

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.

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

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. CLINICALTRIAL ClinicalTrials.gov NCT04958577; http://clinicaltrials.gov/ct2/show/NCT04958577 INTERNATIONAL REGISTERED REPORT DERR1-10.2196/34298