Consider a standard clinical scenario: a physician at an urban hospital in Accra uses an AI-assisted diagnostic platform to evaluate complex patient laboratory trends.
The algorithm either flags an obscure clinical risk that saves a life, or it miscalculates, missing a critical diagnostic marker. The patient suffers from severe clinical complications.
Under current Ghanaian jurisprudence, a definitive question remains dangerously unanswered: Who is legally liable?
This is no longer a hypothetical exercise. Artificial intelligence and predictive software are rapidly entering Ghanaian healthcare infrastructure, yet the legislative and ethical frameworks required to govern them are lagging far behind. Valentine Golden Ghanem is stepping directly into this regulatory vacuum.
The Intersection of Medicine and Law
Ghanem approaches this legal frontier not as a theorist, but as a practitioner. With over a decade of frontline experience as a licensed medical scientist and an established publication record in spatial epidemiology, Ghanem has watched the digital transformation of African healthcare happen in real time.
Recognizing that technology is far outpacing policy, he enrolled in a Master of Laws (LL.M.) in International Law at Liverpool John Moores University, specializing in global health regulation and human rights frameworks. His objective is highly specific: to cultivate an interdisciplinary expertise that pure clinicians or pure legal scholars rarely possess.
“The individuals writing the rules for clinical AI must understand both the mathematical parameters of an algorithm and the messy realities of patient care.”
Three Critical Vulnerabilities
Ghanem’s interdisciplinary analysis identifies three urgent regulatory deficits in Ghana’s current health ecosystem:
• The Liability Vacuum: The Public Health Act contains no provisions addressing AI-augmented medical decision-making. If an automated system contributes to a misdiagnosis, the division of responsibility between the attending clinician, the healthcare facility, and the software developer remains completely ambiguous.
• Data Protection Lag: AI models require vast volumes of highly sensitive longitudinal patient data to maintain predictive accuracy. While Ghana operates a Data Protection Commission, the statutory text was designed for static databases, leaving patient data vulnerable to commercial exploitation and unconsented secondary analyses in the era of high-throughput AI.
• Demographic and Algorithmic Bias: The vast majority of commercial medical AI models are trained on Western datasets. Applying an algorithm optimised for populations in London or Chicago directly to patients in Kumasi introduces major clinical risks. Differences in genetic profiles, clinical presentation, and social determinants mean that a tool’s foreign validation does not guarantee domestic safety.
The Path Forward
Ghanem does not advocate for slowing the adoption of digital health technologies. In a public health system navigating chronic workforce shortages and severe geographic inequities, the efficiency gains of clinical AI are indispensable.
Instead, he argues for a proactive regulatory framework: mandatory domestic validation of foreign clinical models using local patient data before deployment, specialised health-data governance standards, and clear statutory definitions of liability. Without these guardrails, technological adoption isn’t progress—it is simply unmanaged risk.
