Ghana’s 2026 National AI Strategy signals ambition.

It places the country within the global race for artificial intelligence and positions healthcare as a priority sector.

But ambition alone is not progress. 

The real test is whether AI can solve actual healthcare or simply add another layer of complexity to systems already under strain.

Artificial intelligence is often framed as transformative, but it is neither new nor inherently intelligent.

At its core, AI is pattern recognition, systems trained on data to make predictions.

What these systems lack is context.

And in healthcare, context is everything.

Models that perform well in controlled environments frequently fail in real-world clinical settings.

This is not a technical glitch; it is a predictable consequence of deploying tools that are not designed for the systems they enter.

System-level intervention
This is where Ghana’s strategy risks falling short.

Too often, AI is treated as a technological upgrade rather than a system-level intervention.

But AI does not operate in isolation.

It is embedded within workflows, data infrastructures, institutional hierarchies, and clinical decision-making processes.

Ignoring this reality leads to failure, not innovation.

Recent experience offers a warning.

The challenges surrounding the Publican AI system at Ghana’s ports were not caused by faulty algorithms alone, but by poor integration.

The system was misaligned with legal frameworks, disconnected from operational workflows, and positioned in ways that disrupted human decision-making.

The result was friction, inefficiency, and declining trust.

If this can happen in trade, where losses are financial and reversible, the implications for healthcare are far more serious.

In clinical settings, system failure is not an inconvenience, it is a risk to human life.

The promise of AI in healthcare is real.

It can support diagnostics, strengthen surveillance, and improve system efficiency.

For a country like Ghana, where workforce shortages, fragmented information systems, and operational bottlenecks persist, these tools could be transformative.

But without proper governance, they can just as easily amplify existing weaknesses.

Regulatory framework

Ghana’s current regulatory framework is not sufficient for this task.

The Data Protection Act provides a foundation, but AI in healthcare requires more than data privacy.

It demands clear standards for accountability, risk classification, auditing, and ongoing performance monitoring.

Without this, AI systems may be deployed without adequate validation, exposing patients to risks related to bias, opacity, and system failure.

Equally critical is the issue of data. Ghana’s health data systems remain fragmented and poorly integrated, creating barriers for both development and evaluation.

This not only limits local innovation but also increases reliance on imported AI solutions trained on foreign datasets.

The result is predictable: tools that do not reflect local disease patterns, health-seeking behaviours, or system realities.

In healthcare, this is not just inefficient, it is inequitable.

What is needed is not just better algorithms, but better evaluation.

Current approaches rely heavily on pre-deployment validation, often detached from real-world conditions.

This is no longer sufficient.

AI systems must be tested within the environments in which they will operate.

Emerging approaches, such as silent trials, offer a more responsible path, allowing systems to be evaluated in live clinical settings without influencing care decisions.

This generates evidence on safety, reliability, and real-world performance before full deployment.

In healthcare, this is not optional; it is an ethical necessity.

Execution

Ghana now faces a choice.

It can pursue AI as a symbol of technological advancement or it can treat it as a tool for strengthening health systems.

The difference lies in execution.

Success will depend on aligning AI with clinical realities, embedding it within workflows, strengthening data systems, and building regulatory structures that prioritise safety and accountability.

AI will not improve healthcare simply because it is deployed.

It will do so only if it is designed, governed and implemented as part of the system itself.

If Ghana gets this right, AI could become a powerful driver of better health outcomes.

If it does not, the country risks investing in sophisticated technologies that deepen, rather than solve, existing challenges.

In the end, the question is not whether Ghana adopts AI.

It is whether Ghana adopts it wisely.

*The writers are a PhD holder in Ethics in Health AI and a Health Informatics Systems & Research Specialist