Aguiar, F.S., Torres, R.C., Pinto, J.V., Kritski, A.L., Seixas, J.M. and Mello, F.C. (2016) Development of Two Artificial Neural Network Models to Support the Diagnosis of Pulmonary Tuberculosis in Hospitalized Patients in Rio de Janeiro, Brazil. Medical & Biological Engineering & Computing, 54, 1751-1759.
https://doi.org/10.1007/s11517-016-1465-1

has been cited by the following article:

• TITLE: Artificial Intelligence for Global Health: Difficulties and Challenges: A Narrative Review

  AUTHORS: Nazia S. Sadat, Maryam S. Shuttari, Mir Sadat-Ali

  KEYWORDS: Artificial Intelligence, Global Health, Implementation, Pitfalls

  JOURNAL NAME: Open Journal of Epidemiology, Vol.14 No.1, February 20, 2024

  ABSTRACT: Global health (GH) aims to improve healthcare for all people on the planet and eradicate all avoidable diseases and deaths. The inception of Artificial Intelligence (AI) is innovating healthcare practices and improving patient outcomes by shuffling enormous volumes of health data—from health records and clinical studies to genetic information analyzing it much faster than humans. AI also helps in the improvement of medical imaging and medical diagnosis. There is an increased optimism regarding the use of applications of AI locally but can these facets be translated globally in the advancement and delivery of healthcare with the help of AI. At present majority of AI developments and applications in health care provide to the needs of developed countries and there is little effort to develop programs which could help to improve healthcare delivery globally. We performed this narrative review to assess the difficulties and discrepancies in implementing AI in global health delivery and find ways to improve.