Skip to main content
Have a personal or library account? Click to login
The Case for Local AI Development: Lessons From Computer‑Aided Detection of Tuberculosis and Silicosis in Southern Africa’s Ex‑Miners Cover

The Case for Local AI Development: Lessons From Computer‑Aided Detection of Tuberculosis and Silicosis in Southern Africa’s Ex‑Miners

Open Access
|Feb 2026

References

  1. Dychiao RG, Nazer L, Mlombwa D, Celi LA. Artificial intelligence and global health equity. BMJ. 2024;387:q2194. doi:10.1136/bmj.q2194.
  2. Yu L, Zhai X. Use of artificial intelligence to address health disparities in low‑and middle‑income countries: A thematic analysis of ethical issues. Public Health. 2024;234:7783. doi:10.1016/j.puhe.2024.05.029.
  3. Abràmoff MD, Tarver ME, Loyo‑Berrios N, et al. Considerations for addressing bias in artificial intelligence for health equity. NPJ Digit Med. 2023;6(1):170. doi:10.1038/s41746-023-00913-9.
  4. Sekalala S, Chatikobo T. Colonialism in the new digital health agenda. BMJ Glob Health. 2024;9(2):e014131. doi:10.1136/bmjgh-2023-014131.
  5. Barker S, Yassi A, Spiegel J, Kistnasamy B, Ehrlich R. Determining thresholds for computer‑aided detection for silicosis: An analytic approach. Am J Ind Med. 2025;68(5):464472. doi:10.1002/ajim.23720.
  6. Ehrlich R, Barker S, Rees D, et al. Accuracy of computer–aided detection of occupational lung disease: Silicosis and pulmonary tuberculosis in ex‑miners from the South African gold mines. Int J Environ Res Public Health. 2022;19(19):12402. doi:10.3390/ijerph191912402.
  7. Ehrlich R, Barker S, Tsang VW, Kistnasamy B, Yassi A. Access of migrant gold miners to compensation for occupational lung disease: Quantifying a legacy of injustice. J Migr Health. 2021;4:100065. doi:10.1016/j.jmh.2021.100065.
  8. Spiegel JM, Ehrlich R, Yassi A, et al. Using artificial intelligence for high‑volume identification of silicosis and tuberculosis: A bio‑ethics approach. Ann Glob Health. 2021;87(1):58. doi:10.5334/aogh.3206.
  9. Young C, Barker S, Ehrlich R, Kistnasamy B, Yassi A. Computer‑aided detection for tuberculosis and silicosis in chest radiographs of gold miners of South Africa. Int J Tuberc Lung Dis. 2020;24(4):444451. doi:10.5588/ijtld.19.0624.
  10. World Health Organization. WHO consolidated guidelines on tuberculosis. Module 2: Screening – systematic screening for tuberculosis disease. World Health Organization. Published 2021. Accessed October 22, 2025. https://apps.who.int/iris/bitstream/handle/10665/340255/9789240022676-eng.pdf.
  11. Qin ZZ, Van der Walt M, Moyo S, et al. Computer‑aided detection of tuberculosis from chest radiographs in a tuberculosis prevalence survey in South Africa: External validation and modelled impacts of commercially available artificial intelligence software. Lancet Digit Health. 2024;6(9):e605e613. doi:10.1016/S2589-7500(24)00118-3.
  12. David P‑M, Onno J, Keshavjee S, Khan FA. Conditions required for the artificial‑intelligence‑based computer‑aided detection of tuberculosis to attain its global health potential. Lancet Digit Health. 2022;4(10):e702e704. doi:10.1016/S2589-7500(22)00172-8.
  13. Ehrlich R, Barker S, Montgomery A, Lewis P, Kistnasamy B, Yassi A. Mining migrant worker recruitment policy and the production of a silicosis epidemic in late 20th‑century Southern Africa. Ann Glob Health. 2023;89:25. doi:10.5334/aogh.4059.
  14. Ehrlich R. A century of miners’ compensation in South Africa. Am J Ind Med. 2012;55(6):560569. doi:10.1002/ajim.22030.
  15. Park HH, Girdler Brown BV, Churchyard GJ, White NW, Ehrlich RI. Incidence of tuberculosis and HIV and progression of silicosis and lung function impairment among former Basotho gold miners. Am J Ind Med. 2009;52(12):901908. doi:10.1002/ajim.20767.
  16. Ehrlich R. Commentary: Silica–a multisystem hazard. Int J Epidemiol. 2021;50(4):12261228. doi:10.1093/ije/dyab020.
  17. Ehrlich R, Akugizibwe P, Siegfried N, Rees D. The association between silica exposure, silicosis and tuberculosis: A systematic review and meta‑analysis. BMC public health. 2021;21(1):953. doi:10.1186/s12889-021-10711-1.
  18. The World Bank. Fighting TB among Southern Africa mine workers. The World Bank; 2016.
  19. Basu S, Stuckler D, Gonsalves G, Lurie M. The production of consumption: Addressing the impact of mineral mining on tuberculosis in southern Africa. Global Health. 2009;5(1):11. doi:10.1186/1744-8603-5-11.
  20. Ehrlich R, Murray J, Said‑Hartley Q, Rees D. Silicotuberculosis: A critical narrative review. Eur Respir Rev. 2024;33(174):240168. doi:10.1183/16000617.0168-2024.
  21. Kistnasamy B, Yassi A, Yu J, et al. Tackling injustices of occupational lung disease acquired in South African mines: Recent developments and ongoing challenges. Global Health. 2018;14:60. doi:10.1186/s12992-018-0376-3.
  22. Roberts J. The hidden epidemic amongst former miners: silicosis, tuberculosis and the Occupational Diseases in Mines and Works Act in the Eastern Cape, South Africa. Health Systems Trust and Department of Health; 2009.
  23. Murray J, Davies T, Rees D. Occupational lung disease in the South African mining industry: Research and policy implementation. J Public Health Policy. 2011;32:S65S79. doi:10.1057/jphp.2011.25.
  24. Koçak B, Ponsiglione A, Stanzione A, et al. Bias in artificial intelligence for medical imaging: Fundamentals, detection, avoidance, mitigation, challenges, ethics, and prospects. Diagn Interv Radiol. 2025;31(2):7588. doi:10.4274/dir.2024.242854.
  25. Vaidya A, Chen RJ, Williamson DFK, et al. Demographic bias in misdiagnosis by computational pathology models. Nat Med. 2024;30(4):11741190. doi:10.1038/s41591-024-02885-z.
  26. Zhang J, Zhang S, Shen X, Lukasiewicz T, Xu Z. Multi‑ConDoS: Multimodal contrastive domain sharing generative adversarial networks for self‑supervised medical image segmentation. IEEE Trans Med Imaging. 2024;43(1):7695. doi:10.1109/TMI.2023.3290356.
  27. Yang Y, Zhang H, Gichoya JW, Katabi D, Ghassemi M. The limits of fair medical imaging AI in real‑world generalization. Nat Med. 2024;30(10):28382848. doi:10.1038/s41591-024-03113-4.
  28. Brown A, Tomasev N, Freyberg J, Liu Y, Karthikesalingam A, Schrouff J. Detecting shortcut learning for fair medical AI using shortcut testing. Nat Commun. 2023;14(1):4314. doi:10.1038/s41467-023-39902-7.
  29. Banerjee I, Bhattacharjee K, Burns JL, et al. ‘Shortcuts’ causing bias in radiology artificial intelligence: Causes, evaluation, and mitigation. J Am Coll Radiol. 2023;20(9):842851. doi:10.1016/j.jacr.2023.06.025.
  30. Codlin AJ, Dao TP, Vo LNQ, et al. Independent evaluation of 12 artificial intelligence solutions for the detection of tuberculosis. Sci Rep. 2021;11(1):23895. doi:10.1038/s41598-021-03265-0.
  31. Cross JL, Choma MA, Onofrey JA. Bias in medical AI: Implications for clinical decision‑making. PLOS Digit Health. 2024;3(11):e0000651. doi:10.1371/journal.pdig.0000651.
  32. Weiss K, Khoshgoftaar TM, Wang D. A survey of transfer learning. J Big Data. 2016;3(1):9. doi:10.1186/s40537-016-0043-6.
  33. Salman H, Jain S, Ilyas A, Engstrom L, Wong E, Madry A. When does bias transfer in transfer learning?. arXiv Preprint. Published July 6, 2022. doi:10.48550/arXiv.2207.02842.
  34. Rahman T, Khandakar A, Kadir MA, et al. Reliable tuberculosis detection using chest X‑ray with deep learning, segmentation and visualization. IEEE Access. 2020;8:191586191601. doi:10.1109/ACCESS.2020.3031384.
  35. Wang X, Peng Y, Lu L, Lu Z, Bagheri M, Summers RM. ChestX‑ray8: Hospital‑scale chest X‑ray database and benchmarks on weakly‑supervised classification and localization of common thorax diseases. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017:20972106. doi.10.1109/CVPR.2017.369
  36. Cohen JP, Viviano JD, Bertin P, et al. TorchXRayVision: A library of chest X‑ray datasets and models. In: Proceedings of the 5th International Conference on Medical Imaging with Deep Learning. PMLR; 2022:231249.
  37. Ali S, Abuhmed T, El‑Sappagh S, et al. Explainable Artificial Intelligence (XAI): What we know and what is left to attain trustworthy artificial intelligence. Inf Fusion. 2023;99:101805. doi:10.1016/j.inffus.2023.101805.
  38. Celi LA, Cellini J, Charpignon M‑L, et al. Sources of bias in artificial intelligence that perpetuate healthcare disparities—a global review. PLOS Digit Health. 2022;1(3):e0000022. doi:10.1371/journal.pdig.0000022.
  39. Seyyed‑Kalantari L, Liu G, McDermott M, Chen IY, Ghassemi M. CheXclusion: Fairness gaps in deep chest X‑ray classifiers. In: Biocomputing 2021: Proceedings of the Pacific Symposium. World Scientific; 2020:232243.
  40. Nayak BS, Walton N. Political Economy of Artificial Intelligence. Springer; 2024.
  41. Png M‑T. At the tensions of south and north: Critical roles of global south stakeholders in AI governance. In: Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency. Association for Computing Machinery; 2022:14341445. doi:10.1145/3531146.3533200.
  42. Victor A. Artificial intelligence in global health: An unfair future for health in Sub‑Saharan Africa? Health Aff Sch. 2025;3(2):qxaf023. doi:10.1093/haschl/qxaf023.
  43. Souza R, Stanley EAM, Forkert ND. On the relationship between open science in artificial intelligence for medical imaging and global health equity. In: Workshop on Clinical Image‑Based Procedures. Springer; 2023:289300.
  44. Larson DB. Openness and transparency in the evaluation of bias in artificial intelligence. Radiology. 2023;306(2):e222263.
  45. Nekoto W, Marivate V, Matsila T, et al. Participatory research for low‑resourced machine translation: A case study in African languages. arXiv. Preprint. Published October 5, 2020. doi:10.48550/arXiv.2010.02353.
  46. Ye W, Zheng G, Cao X, Ma Y, Zhang A. Spurious correlations in machine learning: A survey. arXiv Preprint. Published February 20, 2024. doi:10.48550/arXiv.2402.12715.
  47. DeGrave AJ, Janizek JD, Lee S‑I. AI for radiographic COVID‑19 detection selects shortcuts over signal. Nat Mach Intell. 2021;3(7):610619. doi:10.1038/s42256-021-00338-7.
  48. Brady AP, Allen B, Chong J, et al. Developing, purchasing, implementing and monitoring AI tools in radiology: practical considerations. A multi‑society statement from the ACR, CAR, ESR, RANZCR & RSNA. Can Assoc Radiol J. 2024;75(2):226244.
  49. Muralidharan V, Adewale BA, Huang CJ, et al. A scoping review of reporting gaps in FDA‑approved AI medical devices. NPJ Digit Med. 2024;7(1):273. doi:10.1038/s41746-024-01270-x.
DOI: https://doi.org/10.5334/aogh.5064 | Journal eISSN: 2214-9996
Language: English
Submitted on: Nov 7, 2025
Accepted on: Jan 20, 2026
Published on: Feb 12, 2026
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2026 Sean Terespolsky, Annalee Yassi, Rodney Ehrlich, Joshua Bruton, Karen Lockhart, Hairong Wang, Richard Klein, Warrick Sive, John Statheros, Jerry M. Spiegel, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.