A team from University College of Dentistry at the University of Lahore conducted a cross-sectional survey among 451 medical and dental clinicians in Pakistan. Employing the General Attitude towards Artificial Intelligence Scale and a self-formulated readiness questionnaire, they quantified practitioners’ positive and negative perceptions, familiarity, and confidence in operating AI systems to facilitate informed AI adoption in resource-constrained settings.

Key points

• Surveyed 451 public and private medical/dental practitioners in Pakistan using GAAIS and a custom readiness tool.
• Positive attitude mean score was 3.6±0.54; negative attitude mean score was 2.8±0.71 on a 5-point Likert scale.
• Dental practitioners showed significantly higher confidence in AI operation (38.4% vs. 29.8%, p=0.047) and willingness for AI in diagnosis (68.5% vs. 57%, p=0.004).

Why it matters: This study underscores critical practitioner readiness and ethical considerations necessary to guide successful AI integration in resource-limited healthcare systems.

A cross-sectional study at a Turkish university hospital utilized the MAIRS-MS and OTOC scales to quantitatively assess 195 healthcare professionals’ readiness for medical AI and their openness to organizational change, revealing significant positive attitudes and demographic patterns in AI adoption readiness.

Key points

• Validated the four‐factor MAIRS-MS scale (cognitive, ability, vision, ethical) for measuring medical AI readiness among 195 hospital staff.
• Applied EFA and CFA to confirm construct validity, achieving RMSEA=0.087 and CFI=0.96 for MAIRS-MS and RMSEA=0.00 and CFI=1.00 for OTOC.
• Used SEM to model relationships, finding a low but significant positive correlation (r=0.236) between AI readiness and openness to organizational change.

Why it matters: This study demonstrates that targeted training and change management can leverage healthcare workers’ positive AI readiness to accelerate safe and effective AI integration in clinical practice.

In a recent 2025 study, Fatih Orhan and Mehmet Nurullah Kurutkan from BMC Health Services Research demonstrate how machine learning, applied to Turkey Health Survey data, predicts healthcare demand. By examining predisposing, enabling, and need factors, the study reveals the impact of demographics and chronic conditions. This research offers practical insights for optimizing healthcare resource allocation.