Recent Study Highlights Challenges and Opportunities for AI in Healthcare
A recent study published by the Tunisian Institute for Strategic Studies (ITES), titled "Artificial Intelligence, a Lever for the Social Role of the State," revealed that data governance is a major obstacle to the deployment of artificial intelligence (AI) in healthcare, with gaps in data collection and standardization.
Key Findings
The study, conducted as part of a participatory approach involving experts and field actors, found that digital infrastructure in the healthcare sector is insufficient to support large-scale AI deployment. Additionally, the financing of the healthcare system is limited, hindering the adoption of advanced technologies.
Recommendations
Experts recommend establishing a strong national governance framework for AI in healthcare, with priorities on high-impact use cases. They also advocate for the adoption of a specific framework law for AI in healthcare to ensure data protection and ethics.
Priority Use Cases
The study identified the following priority use cases in the healthcare sector, considered to have the highest feasibility in the short term:
- Predictive optimization of medication and essential supply management using AI
- Creating a dynamic mapping and analyzing territorial and social health inequalities using AI
- Analyzing clusters for priority diseases
- Early detection of rapidly spreading epidemics
- Implementing a health information and orientation chatbot for citizens
Emerging Ecosystem
According to the study, Tunisia is witnessing the emergence of a dynamic ecosystem of initiatives that leverage AI to address healthcare challenges, including improving care quality, optimizing hospital management, and strengthening prevention and public health.
Strategic Lever
"Artificial intelligence now represents a strategic lever for modernizing the healthcare system in Tunisia," the study emphasizes. AI provides concrete solutions to several structural challenges, such as:
- Human resource shortages
- Increasing pressure on hospital infrastructure
- Regional disparities in access to care
- Underutilization of medical data
Regulatory Framework
However, the absence of a specific legal framework for AI in healthcare has created a blind spot for medical-legal responsibility, model transparency, and risk management.
