AI is reshaping industries in the MENA region, but healthcare is still catching up. Why is that?
A brief survey that was conducted among university alumni (24 responses out of 517; 4.6% response rate) offers preliminary insights on the adoption of AI in the workflows of various sectors (beyond ChatGPT/Gemini) and the stance of the participants towards AI. Although not fully representative, it highlights 👇
📊 Who’s Using AI?
- Engineering: 0% adoption
- Healthcare: 16.7% adoption
- تعليم: 33% adoption
- Business: 37.5% adoption
The results show that industries such as Engineering and Healthcare adopt AI cautiously. But here’s the twist! While 25% reported using advanced AI, even among those not using advanced AI (75%), 71% still have a favorable view towards using it. The interest is there; so what’s holding AI back?
While our survey does not capture data that can further explain these challenges, the DIRECT Framework clearly summarizes the key barriers to AI adoption in Healthcare
D – Data Challenges: Siloed patient records & strict privacy rules make AI training tough.
I – Infrastructure Issues: Outdated hospital systems struggle to support AI.
R – Resource Constraints: AI talent is scarce, and costs are high.
E – Ethical & Regulatory Hurdles: Who’s responsible if AI makes an error? Regulations are still evolving.
C – Cognitive Decline: Too much AI reliance could weaken clinical judgment.
T – Traditional & Cultural Resistance: Some decision-makers hesitate to embrace AI-driven change.
These barriers must be addressed to accelerate AI adoption while preserving human capabilities. Recognizing these challenges is vital for developing strategies that foster balanced technological progress and unlock AI’s full potential.
💭 What do you think? Which barrier is the biggest challenge for AI in healthcare? And what steps should we be taking to overcome these barriers? Reach out to us at healthtech@zaka.news and let’s discuss!
