Gouda, H. N. et al. Burden of non-communicable diseases in sub-Saharan Africa, 1990-2017: results from the Global Burden of Disease Study 2017. Lancet Glob. Health 7, e1375–e1387 (2019).
Jukarainen, S. et al. Genetic risk factors have a substantial impact on healthy life years. Nat. Med. 28, 1893–1901 (2022).
Miranda, J. J. et al. Understanding the rise of cardiometabolic diseases in low- and middle-income countries. Nat. Med. 25, 1667–1679 (2019).
Ezzati, M., Pearson-Stuttard, J., Bennett, J. E. & Mathers, C. D. Acting on non-communicable diseases in low- and middle-income tropical countries. Nature 559, 507–516 (2018).
NCD Alliance. NCDs: Noncommunicable diseases (NCDs) – mainly cancer, cardiovascular disease, chronic respiratory diseases, and diabetes – are the #1 cause of death and disability worldwide., https://ncdalliance.org/why-ncds/NCDs (2022).
Vos, T. et al. Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet 396, 1204–1222 (2020).
Adrianna, M. et al. The household economic burden of non-communicable diseases in 18 countries. BMJ Glob. Health 5, e002040 (2020).
Ujewe, S. J. & van Staden, W. C. Inequitable access to healthcare in Africa: reconceptualising the “accountability for reasonableness framework” to reflect indigenous principles. Int. J. Equity Health 20, 139 (2021).
Martorell-Marugán, J. et al. in Computational Biology (ed H. Husi) (Codon Publications Copyright: The Authors., 2019).
Gracia, K. C. & Holger, H. in Clinical Methods: The History, Physical, and Laboratory Examinations (eds H. K. Walker, W. D. Hall, & J. W. Hurst) (Butterworths Copyright © 1990, Butterworth Publishers, a division of Reed Publishing., 1990).
Obermeyer, Z. & Emanuel, E. J. Predicting the future — big data, machine learning, and clinical medicine. N. Engl. J. Med. 375, 1216–1219 (2016).
Topol, E. J. High-performance medicine: The convergence of human and artificial intelligence. Nat. Med. 25, 44–56 (2019).
Tenny, S. & Varacallo, M. in StatPearls (StatPearls Publishing Copyright © 2024, StatPearls Publishing LLC., 2024).
Office of Data Science Strategy National Institute of Health. NIH Strategic Plan for Data Science, <https://datascience.nih.gov/sites/default/files/NIH_Strategic_Plan_for_Data_Science_Final_508.pdf> (2021).
Subrahmanya, S. V. G. et al. The role of data science in healthcare advancements: applications, benefits, and future prospects. Ir. J. Med. Sci. (1971 -) 191, 1473–1483 (2022).
Keshavamurthy, R., Dixon, S., Pazdernik, K. T. & Charles, L. E. Predicting infectious disease for biopreparedness and response: A systematic review of machine learning and deep learning approaches. One Health 15, 100439 (2022).
Lin, G., Palopoli, M. & Dadwal, V. in Leveraging Data Science for Global Health (eds Leo Anthony Celi et al.) 77-98 (Springer International Publishing, 2020).
Weng, W.-H. in Leveraging Data Science for Global Health (eds Leo Anthony Celi et al.) 199-217 (Springer International Publishing, 2020).
Marcelo, A. B. in Leveraging Data Science for Global Health (eds Leo Anthony Celi et al.) 307-314 (Springer International Publishing, 2020).
Goldsmith, J. et al. The emergence and future of public health data science. Public Health Rev. 42 https://doi.org/10.3389/phrs.2021.1604023 (2021).
Adebamowo, C. A. et al. The promise of data science for health research in Africa. Nat. Commun. 14, 6084 (2023).
Mendis, S. et al. Global Atlas on cardiovascular disease prevention and control. Published by the World Health Organization in collaboration with the World Heart Federation and the World Stroke Organization (2011).
Mensah, G. A. et al. Global Burden of Cardiovascular Diseases and Risks, 1990-2022. J. Am. Coll. Cardiol. 82, 2350–2473 (2023).
Africa Centres for Disease Control and Prevention. Africa CDC Non Communicable Diseases, Injuries Prevention and Control and Mental Health Promotion Strategy (2022-26). 34 (Africa Centres for Disease Control and Prevention, Addis Ababa, Ethiopia, 2022).
Mensah, G. A. & Mayosi, B. M. The 2011 United Nations High-Level Meeting on Non-Communicable Diseases: The Africa agenda calls for a 5-by-5 approach. Vol. 103 (2012).
Schwartz, L. N., Shaffer, J. D. & Bukhman, G. The origins of the 4 × 4 framework for noncommunicable disease at the World Health Organization. SSM – Popul. Health 13, 100731 (2021).
Page, M. J. et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 372, n71 (2021).
Okekunle, A. P. et al. Harnessing data science to control cardiovascular diseases in Africa: A systematic review. Prospero https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023406237 (2023).
Okekunle, A. P. et al. Data science applications for non-communicable disease prevention and control in Africa: a systematic review protocol. Cardiovasc J Afr 36, 625–633 (2025).
National Library of Medicine. Using PICO to Frame Clinical Questions, https://www.nlm.nih.gov/oet/ed/pubmed/pubmed_in_ebp/02-100.html.
Hupe, M. EndNote X9. J. Electron. Resour. Med. Libraries 16, 117–119 (2019).
Veritas Health Innovation. Covidence Systematic Review Software, <https://www.covidence.org/>
National Institute of Health. Study Quality Assessment Tools, <https://www.nhlbi.nih.gov/health-topics/study-quality-assessment-tools>
França, R. P., Borges Monteiro, A. C., Arthur, R. & Iano, Y. in Trends in Deep Learning Methodologies (eds Vincenzo Piuri, Sandeep Raj, Angelo Genovese, & Rajshree Srivastava) 63-87 (Academic Press, 2021).
LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).
Mohri, M., Rostamizadeh, A. & Talwalkar, A. Foundations of Machine Learning. (The MIT Press, 2012).
Sheikh, H., Prins, C. & Schrijvers, E. in Mission AI: The New System Technology (eds Haroon Sheikh, Corien Prins, & Erik Schrijvers) 15-41 (Springer International Publishing, 2023).
Vapnik, V. The Nature of Statistical Learning Theory. (Springer: New York, 2000).
Uffelmann, E. et al. Genome-wide association studies. Nat. Rev. Methods Prim. 1, 59 (2021).
Sanderson, E. et al. Mendelian randomization. Nat. Rev. Methods Prim. 2, 6 (2022).
Choi, S. W., Mak, T. S.-H. & O’Reilly, P. F. Tutorial: a guide to performing polygenic risk score analyses. Nat. Protoc. 15, 2759–2772 (2020).
Fasman, K. H. & Salzberg, S. L. in New Compr. Biochem. Vol. 32 (eds S. L. Salzberg, D. B. Searls, & S. Kasif) 29-42 (Elsevier, 1998).
Hicks, S. A. et al. On evaluation metrics for medical applications of artificial intelligence. Sci. Rep. 12, 5979 (2022).
White, H. et al. Guidance for producing a Campbell evidence and gap map. Campbell Syst. Rev. 16, e1125 (2020).
Owolabi, M. O. et al. Global synergistic actions to improve brain health for human development. Nat. Rev. Neurol. 19, 371–383 (2023).
Thapa, R., Zengin, A. & Thrift, A. G. Continuum of care approach for managing non-communicable diseases in low- and middle-income countries. J. Glob. Health 10, 010337 (2020).
Achilonu, O. J. et al. Predicting colorectal cancer recurrence and patient survival using supervised machine learning approach: A South African Population-Based Study (vol 9, 694306, 2021). Front. Public Health 9 https://doi.org/10.3389/fpubh.2021.778749 (2021).
Achilonu, O. J. et al. Use of machine learning and statistical algorithms to predict hospital length of stay following colorectal cancer resection: A South African pilot study. Front. Oncol. 11 https://doi.org/10.3389/fonc.2021.644045 (2021).
Achilonu, O. J. et al. A text mining approach in the classification of free-text cancer pathology reports from the South African National Health Laboratory Services. Information 12, 451 (2021).
Achilonu, O. J., Singh, E., Nimako, G., Eijkemans, R. M. J. C. & Musenge, E. Rule-based information extraction from free-text pathology reports reveals trends in south african female breast cancer molecular subtypes and Ki67 expression. BioMed. Res. Int. 2022, 6157861 (2022).
Adua, E. et al. Predictive model and feature importance for early detection of type II diabetes mellitus. Transl. Med. Commun. 6, 17 (2021).
Agossou, C., Atchade, M. N., Djibril, A. M. & Kurisheva, S. V. Mathematical modeling and machine learning for public health decision-making: the case of breast cancer in Benin. Math. Biosci. Eng.: MBE 19, 1697–1720 (2022).
Ahmed Mustafa, E., Yasmine Salah, N., Mai, O., Nancy Diaa, M. & Hala Sadik, E. A step towards the application of an artificial intelligence model in the prediction of intradialytic complications. 58 https://www.ajol.info/index.php/bafm/article/view/255597 (2022).
Akinyokun, O. C., Iwasokun, G. B., Arekete, S. A. & Samuel, R. W. Fuzzy logic-driven expert system for the diagnosis of heart failure disease. Artif. Intell. Res 4, https://doi.org/10.5430/air.v4n1p12 (2015).
Akpa, O. et al. A novel afrocentric stroke risk assessment score: Models from the siren study. J. Stroke Cerebrovasc. Dis. 30, 106003 (2021).
Asmare, M. H., Filtjens, B., Woldehanna, F., Janssens, L. & Vanrumste, B. Rheumatic heart disease screening based on phonocardiogram. Sensors (Basel, Switzerland) 21 https://doi.org/10.3390/s21196558 (2021).
Asmare, M. H. et al. Automated Rheumatic Heart Disease Detection from Phonocardiogram in Cardiology Ward. 13th International Joint Conference on Biomedical Engineering Systems and Technologies – Cognitive Health IT 5, 839-844 (2020).
Asowata, O. J. et al. Risk assessment score and chi-square automatic interaction detection algorithm for hypertension among Africans: Models From the SIREN study. Hypertension 80, 2581–2590 (2023).
Dese, K. et al. Accurate machine-learning-based classification of leukemia from blood smear images. Clin. Lymphoma, Myeloma Leuk. 21, e903–e914 (2021).
Ebrahim, O. A. & Derbew, G. Application of supervised machine learning algorithms for classification and prediction of type-2 diabetes disease status in Afar regional state, Northeastern Ethiopia 2021. Sci. Rep. 13, 7779 (2023).
Ecklu-Mensah, G. et al. Gut microbiota and fecal short chain fatty acids differ with adiposity and country of origin: The METS-Microbiome Study. bioRxiv. https://doi.org/10.1101/2023.03.21.533195 (2023).
El Agouri, H. et al. Assessment of deep learning algorithms to predict histopathological diagnosis of breast cancer: First Moroccan prospective study on a private dataset. BMC Res. Notes 15, 66 (2022).
El Badisy, I. et al. Risk factors affecting patients survival with colorectal cancer in Morocco: survival analysis using an interpretable machine learning approach. Sci. Rep. 14 https://doi.org/10.1038/s41598-024-51304-3 (2024).
Endalie, D. & Abebe, W. T. Analysis of lung cancer risk factors from medical records in Ethiopia using machine learning. PLOS Digital Health 2, e0000308 (2023).
Evwiekpaefe, A. E. & Abdulkadir, N. A predictive model for diabetes mellitus using machine learning techniques (A Study in Nigeria). Afr. J. Inf. Syst. 15 Available at: https://digitalcommons.kennesaw.edu/ajis/vol15/iss1/1(2023).
Gelaw, N. B. et al. Development and validation of risk prediction model for diabetic neuropathy among diabetes mellitus patients at selected referral hospitals, in Amhara regional state Northwest Ethiopia, 2005-2021. PLOS ONE 18, e0276472 (2023).
Hamouda, S. K. M., Wahed, M. E., Abo Alez, R. H. & Riad, K. Robust breast cancer prediction system based on rough set theory at National Cancer Institute of Egypt. Comput. Methods Prog. Biomed. 153, 259–268 (2018).
Haruna, A. A. et al. An Improved C4.5 Data Mining Driven Algorithm for the Diagnosis of Coronary Artery Disease. 2019 International Conference on Digitization (ICD), 48-52 (2019).
Ishaq, F., Jibril, M. & Abubakar, A. Fuzzy based expert system for diagnosis of diabetes mellitus. Int. J. Adv. Sci. Technol. 136, 39–50 (2020).
Islam, M. M. et al. Predicting the risk of hypertension using machine learning algorithms: A cross sectional study in Ethiopia. PLoS One 18, e0289613 (2023).
Khdair, H. Exploring machine learning techniques for coronary heart disease prediction. International Journal of Advanced Computer Science and Applications 12, 28–36 (2021).
Macaulay, B. O., Aribisala, B. S., Akande, S. A., Akinnuwesi, B. A. & Olabanjo, O. A. Breast cancer risk prediction in African women using Random Forest Classifier. Cancer Treat. Res. Commun. 28, 100396 (2021).
Mbonyinshuti, F., Nkurunziza, J., Niyobuhungiro, J. & Kayitare, E. Application of random forest model to predict the demand of essential medicines for non-communicable diseases management in public health facilities. Pan Afr. Med. J. 42, 89 (2022).
Mokoatle, M., Mapiye, D., Marivate, V., Hayes, V. M. & Bornman, R. Discriminatory Gleason grade group signatures of prostate cancer: An application of machine learning methods. PLoS One 17, e0267714 (2022).
Mousa, K. M., Mousa, F. A., Mohamed, H. S. & Elsawy, M. M. Prediction of Foot Ulcers Using Artificial Intelligence for Diabetic Patients at Cairo University Hospital, Egypt. Sage Open Nursing 9 https://doi.org/10.1177/23779608231185873 (2023).
Mpanya, D., Celik, T., Klug, E. & Ntsinjana, H. Predicting in-hospital all-cause mortality in heart failure using machine learning. Front. Cardiovascular Med. 9, 1032524 (2023).
Muhammad, L. J. & Algehyne, E. A. Fuzzy based expert system for diagnosis of coronary artery disease in nigeria. Health Technol. 11, 319–329 (2021).
Muhammad, L. J., Algehyne, E. A. & Usman, S. S. Predictive supervised machine learning models for diabetes mellitus. SN Comput. Sci. 1, 240 (2020).
Muhammad, L. J. et al. Performance Evaluation of Classification Data Mining Algorithms on Coronary Artery Disease Dataset. 2019 9th International Conference on Computer and Knowledge Engineering (ICCKE), 1-5 (2019).
Nassar, A. et al. Tumor mutation burden prediction model in Egyptian breast cancer patients based on next generation sequencing. Asian Pac. J. cancer Prev.: APJCP 22, 2053–2059 (2021).
Okagbue, H. I., Adamu, P. I., Oguntunde, P. E., Obasi, E. C. M. & Odetunmibi, O. A. Machine learning prediction of breast cancer survival using age, sex, length of stay, mode of diagnosis and location of cancer. Health Technol. 11, 887–893 (2021).
Okagbue, H. I., Oguntunde, P. E., Adamu, P. I. & Adejumo, A. O. Unique clusters of patterns of breast cancer survivorship. Health Technol. 12, 365–384 (2022).
Olago, V., Muchengeti, M., Singh, E. & Chen, W. C. Identification of malignancies from free-text histopathology reports using a multi-model supervised machine learning approach. Information 11, 455 (2020).
Othman, M., Elbasha, A. M., Naga, Y. S. & Moussa, N. D. Early prediction of hemodialysis complications employing ensemble techniques. Biomed. Eng. Online 21, 74 (2022).
Premsagar, P., Aldous, C. & Esterhuizen, T. Ten-year predictors of major adverse cardiovascular events in patients without angina. S Afr. Fam. Pr. (2004) 65, e1–e9 (2023).
Qarmiche, N., Chrifi Alaoui, M., El Kinany, K., El Rhazi, K. & Chaoui, N. Soft-Voting colorectal cancer risk prediction based on EHLI components. Inform. Med. Unlocked 33, 101070 (2022).
Rahimeto, S., Debelee, T. G., Yohannes, D. & Schwenker, F. Automatic pectoral muscle removal in mammograms. Evol. Syst. 12, 519–526 (2021).
Rufo, D. D., Debelee, T. G., Ibenthal, A. & Negera, W. G. Diagnosis of diabetes mellitus using gradient boosting machine (Lightgbm). Diagnostics 11, 1714 (2021).
Salie, M. T. et al. Data independent acquisition mass spectrometry in severe Rheumatic Heart Disease (RHD) identifies a proteomic signature showing ongoing inflammation and effectively classifying RHD cases. Cardiovasc. J. Afr. 33, 21 (2021).
Sarkar, S. & Mali, K. Breast Cancer Subtypes Classification with Hybrid Machine Learning Model. Methods Inf. Med. 61, 68–83 (2022).
Sefa-Yeboah, S. M. et al. Development of a mobile application platform for self-management of obesity using artificial intelligence techniques. Int. J. Telemed. Appl. 2021, 6624057 (2021).
Taye, G. D., Gebreselasie, A., Schwenker, F., Amirian, M. & Yohannes, D. Classification of mammograms using texture and CNN based extracted features. J. Biomim., Biomater., Biomed. Eng. 42, 79–97 (2019).
Uba, M. M., Ren, J. D., Sohail, M. N., Irshad, M. & Yu, K. F. Data mining process for predicting diabetes mellitus based model about other chronic diseases: a case study of the northwestern part of Nigeria. Healthc. Technol. Lett. 6, 98–102 (2019).
William, W., Ware, A., Basaza-Ejiri, A. H. & Obungoloch, J. A pap-smear analysis tool (PAT) for detection of cervical cancer from pap-smear images. Biomed. Eng. Online 18, 16 (2019).
Zewdie, E. T., Tessema, A. W. & Simegn, G. L. Classification of breast cancer types, sub-types and grade from histopathological images using deep learning technique. Health Technol. 11, 1277–1290 (2021).
Muhammad, L. J. et al. Machine Learning Predictive Models for Coronary Artery Disease. SN Comput. Sci. 2, 350 (2021).
Udosen, B. et al. Meta-analysis and multivariate GWAS analyses in 77,850 individuals of African ancestry identify novel variants associated with blood pressure traits. Int. J. Mol. Sci. 24 https://doi.org/10.3390/ijms24032164 (2023).
Tekola-Ayele, F. et al. Genome-wide association study identifies African-ancestry specific variants for metabolic syndrome. Mol. Genet. Metab. 116, 305–313 (2015).
Soremekun, O. et al. Lipid traits and type 2 diabetes risk in African ancestry individuals: A Mendelian Randomization study. EBioMedicine 78, 103953 (2022).
Soremekun, C. et al. Multivariate GWAS analysis reveals loci associated with liver functions in continental African populations. PLoS One 18, e0280344 (2023).
Soo, C. C. et al. Genome-wide association study of population-standardised cognitive performance phenotypes in a rural South African community. Commun. Biol. 6, 328 (2023).
Singh, S. et al. Genome-wide association study meta-analysis of blood pressure traits and hypertension in sub-Saharan African populations: an AWI-Gen study. Nat. Commun. 14, 8376 (2023).
Sahibdeen, V. et al. Genetic variants in SEC16B are associated with body composition in black South Africans. Nutr. Diab 8, 43 (2018).
Machipisa, T. et al. Association of Novel Locus With Rheumatic Heart Disease in Black African Individuals: Findings From the RHDGen Study. JAMA Cardiol. 6, 1000–1011 (2021).
Kamiza, A. B. et al. Multi-trait discovery and fine-mapping of lipid loci in 125,000 individuals of African ancestry. Nat. Commun. 14, 5403 (2023).
Huo, D. et al. Evaluation of 19 susceptibility loci of breast cancer in women of African ancestry. Carcinogenesis 33, 835–840 (2012).
Hughes, O. et al. Genome-wide study investigating effector genes and polygenic prediction for kidney function in persons with ancestry from Africa and the Americas. Cell Genom. 4, 100468 (2023).
Hendry, L. M. et al. Insights into the genetics of blood pressure in black South African individuals: the Birth to Twenty cohort. BMC Med. Genomics 11, 2 (2018).
Hamdi, Y. et al. A genome wide SNP genotyping study in the Tunisian population: specific reporting on a subset of common breast cancer risk loci. BMC Cancer 18, 1295 (2018).
Fatumo, S. et al. Metabolic traits and stroke risk in individuals of African Ancestry: Mendelian randomization analysis. Stroke 52, 2680–2684 (2021).
Fatumo, S. et al. Discovery and fine-mapping of kidney function loci in first genome-wide association study in Africans. Hum. Mol. Genet. 30, 1559–1568 (2021).
Choudhury, A. et al. Meta-analysis of sub-Saharan African studies provides insights into genetic architecture of lipid traits. Nat. Commun. 13, 2578 (2022).
Chikowore, T., van Zyl, T., Feskens, E. J. M. & Conradie, K. R. Predictive utility of a genetic risk score of common variants associated with type 2 diabetes in a black South African population. Diab Res. Clin. Pract. 122, 1–8 (2016).
Chikowore, T. et al. Polygenic prediction of type 2 diabetes in Africa. Diab Care 45, 717–723 (2022).
Chikowore, T., Conradie, K. R., Towers, G. W. & van Zyl, T. Common variants associated with type 2 diabetes in a Black South African population of Setswana Descent: African populations diverge. OMICS: A J. Integr. Biol. 19, 617–626 (2015).
Chen, J. et al. Genome-wide association study of type 2 diabetes in Africa. Diabetologia 62, 1204–1211 (2019).
Chen, J. et al. The trans-ancestral genomic architecture of glycemic traits. Nat. Genet. 53, 840–860 (2021).
Chen, G. et al. Genome-wide analysis identifies an african-specific variant in SEMA4D associated with body mass index. Obesity 25, 794–800 (2017).
Boujemaa, M. et al. Uncovering the clinical relevance of unclassified variants in DNA repair genes: a focus on BRCA negative Tunisian cancer families. Front Genet 15, 1327894 (2024).
Boujemaa, M. et al. Health influenced by genetics: A first comprehensive analysis of breast cancer high and moderate penetrance susceptibility genes in the Tunisian population. PLoS One 17, e0265638 (2022).
Boujemaa, M. et al. Germline copy number variations in BRCA1/2 negative families: Role in the molecular etiology of hereditary breast cancer in Tunisia. PLoS One 16, e0245362 (2021).
Boua, P. R. et al. Genetic associations with carotid intima-media thickness link to atherosclerosis with sex-specific effects in sub-Saharan Africans. Nat. Commun. 13, 855 (2022).
Boua, P. R. et al. Novel and known gene-smoking interactions with cIMT identified as potential drivers for atherosclerosis risk in West-African populations of the AWI-Gen study. Front Genet 10, 1354 (2019).
Bentley, A. R. et al. GWAS in Africans identifies novel lipids loci and demonstrates heterogenous association within Africa. Hum. Mol. Genet. 30, 2205–2214 (2021).
Andaleon, A., Mogil, L. S. & Wheeler, H. E. Gene-based association study for lipid traits in diverse cohorts implicates BACE1 and SIDT2 regulation in triglyceride levels. PeerJ 6, e4314 (2018).
Akinyemi, R. O. et al. Novel functional insights into ischemic stroke biology provided by the first genome-wide association study of stroke in indigenous Africans. Genome Med 16, 25 (2024).
Adeyemo, A. A. et al. ZRANB3 is an African-specific type 2 diabetes locus associated with beta-cell mass and insulin response. Nat. Commun. 10, 3195 (2019).
Achilonu, O. J., Fabian, J. & Musenge, E. Modeling long-term graft survival with time-varying covariate effects: An application to a single kidney transplant centre in Johannesburg, South Africa. Front. Public Health 7 https://doi.org/10.3389/fpubh.2019.00201 (2019).
Asante, D. O., Walker, A. N., Seidu, T. A., Kpogo, S. A. & Zou, J. Hypertension and diabetes in Akatsi South District, Ghana: Modeling and forecasting. BioMed. Res. Int. 2022, 9690964 (2022).
Badisy, I. E. et al. Risk factors affecting patients survival with colorectal cancer in Morocco: Survival Analysis using an Interpretable Machine Learning Approach. Res. Square https://doi.org/10.21203/rs.3.rs-2435106/v1 (2023).
Barro, M. et al. Modelling factors associated with therapeutic inertia in hypertensive patients: Analysis using repeated data from a hospital registry in West Africa. Medicine 101 https://doi.org/10.1097/md.0000000000031147 (2022).
Issac, M. S. M., El-Nahid, M. S. & Wissa, M. Y. Is there a role for MDR1, EPHX1 and protein Z gene variants in modulation of warfarin dosage? a study on a cohort of the Egyptian population. Mol. Diagn. Ther. 18, 73–83 (2014).
Mugambi, L., ZÜHlke, L. & Maina, C. W. 1-8 (IEEE).
Narh, C. T., Der, J. B., Ofosu, A., Blettner, M. & Wollschlaeger, D. Describing and modeling the burden of hospitalization of patients with neoplasms in Ghana Using Routine Health Data for 2012–2017. Jco Global Oncol. 8 https://doi.org/10.1200/go.21.00416 (2022).
Peck, D. et al. The use of artificial intelligence guidance for rheumatic heart disease screening by novices. J. Am. Soc. Echocardiogr. 36, 724–732 (2023).
Tran, P. L. et al. Performance of smartphone-based digital images for cervical cancer screening in a low-resource context. Int. J. Technol. Assess. Health Care 34, 337–342 (2018).
Wang, S. et al. Development of a breast cancer risk prediction model for women in Nigeria. Cancer Epidemiol. Biomark. Prev. 27, 636–643 (2018).
Aboelenin, N. A., Elserafi, A., Zaki, N., Rashed, E. A. & al-Shatouri, M. Assessment of artificial intelligence-aided computed tomography in lung cancer screening. Egypt. J. Radiol. Nucl. Med. 54, 74 (2023).
Bellemo, V. et al. Artificial intelligence using deep learning to screen for referable and vision-threatening diabetic retinopathy in Africa: Aclinical validation study. Lancet Digital Health 1, e35–e44 (2019).
Hansen, M. B. et al. Results of automated retinal image analysis for detection of diabetic retinopathy from the Nakuru Study, Kenya. PLoS One 10, e0139148 (2015).
Mathenge, W. et al. Impact of artificial intelligence assessment of diabetic retinopathy on referral service uptake in a low-resource setting: The RAIDERS randomized trial. Ophthalmol. Sci. 2, 100168 (2022).
Parham, G. P. et al. Validation in Zambia of a cervical screening strategy including HPV genotyping and artificial intelligence (AI)-based automated visual evaluation. Infect. Agent. Cancer 18 https://doi.org/10.1186/s13027-023-00536-5 (2023).
Peterson, C. W., Rose, D., Mink, J. & Levitz, D. Real-time monitoring and evaluation of a visual-based cervical cancer screening program using a decision support job aid. Diagnostics 6, 20 (2016).
Abdelsalam, M. M. Effective blood vessels reconstruction methodology for early detection and classification of diabetic retinopathy using OCTA images by artificial neural network. Inform. Med. Unlocked 20, 100390 (2020).
Brodsky, V. et al. Performance of automated classification of diagnostic entities in dermatopathology validated on multisite data representing the real-world variability of pathology workload. Arch. Pathol. Lab. Med. https://doi.org/10.5858/arpa.2021-0550-OA (2022).
Holmstrom, O. et al. Point-of-care digital cytology with artificial intelligence for cervical cancer screening in a resource-limited setting. JAMA Netw. Open 4, e211740 (2021).
Wahl, B., Cossy-Gantner, A., Germann, S. & Schwalbe, N. R. Artificial intelligence (AI) and global health: How can AI contribute to health in resource-poor settings? BMJ Glob. Health 3, e000798 (2018).
Battineni, G., Sagaro, G. G., Chinatalapudi, N. & Amenta, F. Applications of machine learning predictive models in the chronic disease diagnosis. J. Personalized Med. 10, 21 (2020).
Liu, X. et al. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Nat. Med. 26, 1364–1374 (2020).
Collins, G. S. et al. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ 385, e078378 (2024).
Azam, S. et al. Using feature maps to unpack the CNN ‘Black box’ theory with two medical datasets of different modality. Intell. Syst. Appl. 18, 200233 (2023).
Howard, F. M. et al. The impact of site-specific digital histology signatures on deep learning model accuracy and bias. Nat. Commun. 12, 4423 (2021).
Musa, S. M. et al. Paucity of health data in Africa: An obstacle to digital health implementation and evidence-based practice. Public Health Rev. 44 https://doi.org/10.3389/phrs.2023.1605821 (2023).
Adebamowo, C. et al. Ethical oversight of data science health research in Africa. NEJM AI 1, AIpc2400033 (2024).
Obasa, A. E. & Palk, A. C. Responsible application of artificial intelligence in health care. South African J. Sci. 119 https://doi.org/10.17159/sajs.2023/14889 (2023).
Oniani, D. et al. Adopting and expanding ethical principles for generative artificial intelligence from military to healthcare. npj Digital Med. 6, 225 (2023).
Owolabi, M. O. et al. Data resource profile: Cardiovascular H3Africa Innovation Resource (CHAIR). Int. J. Epidemiol. 48, 366–367g (2018).
Omonisi, A. E., Liu, B. & Parkin, D. M. Population-based cancer registration in Sub-Saharan Africa: Its Role in Research and Cancer Control. JCO Global Oncology, 1721-1728 (2020).
Gakunga, R., Parkin, D. M. & Network, O. b. o. t. A. C. R. Cancer registries in Africa 2014: A survey of operational features and uses in cancer control planning. Int. J. Cancer 137, 2045–2052 (2015).
Obasa, A. E. Large language models through the lens of ubuntu for health research in sub-Saharan Africa. South African J. Sci. 120 https://doi.org/10.17159/sajs.2024/16814 (2024).
Consortium, T. H. A. et al. Enabling the genomic revolution in Africa. Science 344, 1346–1348 (2014).
Williamson, A. & Fatumo, S. Genomic diversity improves disease discovery for all. Science 385, 255–256 (2024).
Choudhury, A. et al. Whole-genome sequencing for an enhanced understanding of genetic variation among South Africans. Nat. Commun. 8, 2062 (2017).
Hamdi, Y. et al. Genome Tunisia Project: paving the way for precision medicine in North Africa. Genome Med 16, 104 (2024).
Elmonem, M. A. et al. The Egypt genome project. Nat. Genet. 56, 1035–1037 (2024).
Apeagyei, A. E. et al. Financing health in sub-Saharan Africa 1990–2050: Donor dependence and expected domestic health spending. PLOS Glob. Public Health 4, e0003433 (2024).
Haug, C. J. & Drazen, J. M. Artificial intelligence and machine learning in clinical medicine, 2023. N. Engl. J. Med. 388, 1201–1208 (2023).
Anema, A. et al. Harnessing the web to track the next outbreak: innovations in data science and disease surveillance are changing the way we respond to public health threats. Available at; https://www.americanscientist.org/article/harnessing-the-web-to-track-the-next-outbreak.
Okekunle, A. P. et al. Stroke in Africa: A systematic review and meta-analysis of the incidence and case-fatality rates. Int. J. Stroke 18, 634–644 (2023).
Amirian, P. et al. Using big data analytics to extract disease surveillance information from point of care diagnostic machines. Pervasive Mob. Comput. 42, 470–486 (2017).
Deo, R. C. Machine learning in medicine. Circulation 132, 1920–1930 (2015).
Bedeker, A. et al. A framework for the promotion of ethical benefit sharing in health research. BMJ Glob. Health 7, e008096 (2022).
Staunton, C. & de Vries, J. The governance of genomic biobank research in Africa: reframing the regulatory tilt. J. Law Biosci. 7 https://doi.org/10.1093/jlb/lsz018 (2020).
Chaudhry, I. et al. Strengthening ethics committees for health-related research in sub-Saharan Africa: A scoping review. BMJ Open. 12, e062847 (2022).
Sudoi, A., De Vries, J. & Kamuya, D. A scoping review of considerations and practices for benefit sharing in biobanking. BMC Med. Ethics 22, 102 (2021).
Davenport, T. & Kalakota, R. The potential for artificial intelligence in healthcare. Future Healthc. J. 6, 94–98 (2019).
Owoyemi, A., Owoyemi, J., Osiyemi, A. & Boyd, A. Artificial intelligence for healthcare in Africa. Front. Digital health 2, 6 (2020).
Naidoo, S., Bottomley, D., Naidoo, M., Donnelly, D. & Thaldar, D. W. Artificial intelligence in healthcare: Proposals for policy development in South Africa. South Afr. J. Bioeth. Law 15, 11–16 (2022).
Eze, P., Lawani, L. O., Agu, U. J. & Acharya, Y. Catastrophic health expenditure in sub-Saharan Africa: systematic review and meta-analysis. Bull. World Health Organ. 100, 337–351j (2022).
Thomford, N. E. et al. Implementing artificial intelligence and digital health in resource-limited settings? top 10 lessons we learned in congenital heart defects and cardiology. Omics: A J. Integr. Biol. 24, 264–277 (2020).
Africa Union. AU Data Policy Framework, <https://au.int/en/documents/20220728/au-data-policy-framework> (2022).
Regional Committee for, A. (World Health Organization. Regional Office for Africa, Brazzaville, 2021).