By Team
Ho, Aug. 08, GNA – Inconsistent power supplies and unstable internet connectivity, have been identified as a major challenge heavily impeding the effective deployment of Artificial Intelligence (AI) in rural healthcare facilities.
This was made known by Professor Jerry John Kponyo, the Principal Investigator of the Responsible Artificial Intelligence Lab (RAIL) at the Kwame Nkrumah University of Science and Technology (KNUST).
Prof. Kponyo said this while speaking on the topic, “Responsible AI and Health in Ghana: Challenges and Opportunities,” during an International Conference on AI in Healthcare and Pharmacy.
The Conference was held at University of Health and Allied Sciences (UHAS) at Ho in the Volta Region on the theme: “Accelerating Adoption of Healthcare AI in Ghana: From Vision and Policy to Practice and Impact.”
Prof. Kponyo also noted the presence of algorithmic bias as another barrier to the effective integration of AI into the healthcare ecosystem, saying several diagnostic tools were trained on foreign datasets and consequently omitted the unique genetic markers and lifestyle determinants of the local population.
He emphasised on trust and adoption challenges, noting that cultural resistance, a fear of displacement among workforce, and the nature of unexplainable AI tools hindered widespread acceptance.
He also highlighted data fragmentation and security as significant impediments, drawing attention to non-standardised records, systemic non-interoperability, and critical gaps in patient data privacy.
The Principal Investigator said responsible Al meant smart tools that supported medical decisions safely, fairly, openly, and with respect for human dignity.
He stated that AI tools must support doctors rather than operate as unquestionable “black boxes,” noting that biased or unclear systems could cause dangerous medical errors, ultimately putting patient lives at risk.
Prof. Kponyo emphasised that governing AI integration in healthcare was vital to ensure that every citizen received the same high standard of care, regardless of their location.
He stressed that healthcare AI also required clear clinical rules, explainable models for transparency, rigorous real-world testing, and strong security standards to protect patient data.
GNA
Edited by Maxwell Awumah/ Christabel Addo