Ghana: Inconsistent power supplies and unstable internet connectivity have been identified as major challenges heavily impeding the effective deployment of Artificial Intelligence (AI) in rural healthcare facilities. This revelation was shared 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).
According to Ghana News Agency, Prof. Kponyo addressed these challenges during a talk on 'Responsible AI and Health in Ghana: Challenges and Opportunities,' at an International Conference on AI in Healthcare and Pharmacy. The conference, held at the University of Health and Allied Sciences (UHAS) in Ho, Volta Region, focused on 'Accelerating Adoption of Healthcare AI in Ghana: From Vision and Policy to Practice and Impact.'
Prof. Kponyo identified algorithmic bias as another barrier to the effective integration of AI into the healthcare ecosystem. He explained that many diagnostic tools are trained on foreign datasets, which fail to incorporate the unique genetic markers and lifestyle determinants of the local population, thus limiting their effectiveness.
He also discussed trust and adoption challenges, highlighting cultural resistance, workforce fears of displacement, and the complexity of unexplainable AI tools as factors hindering widespread acceptance. Data fragmentation and security were also significant impediments, with non-standardised records, systemic non-interoperability, and gaps in patient data privacy drawing particular concern.
The Principal Investigator emphasized that responsible AI should involve smart tools that support medical decisions safely, fairly, and respectfully, maintaining human dignity. He warned against AI tools operating as unquestionable 'black boxes,' noting that biased or unclear systems could lead to dangerous medical errors, potentially risking patient lives.
Prof. Kponyo stressed the importance of governing AI integration in healthcare to ensure that every citizen receives a consistent high standard of care, irrespective of their location. He underscored the necessity for healthcare AI to have clear clinical rules, explainable models for transparency, rigorous real-world testing, and robust security standards to safeguard patient data.