Does AI affect treatment decision-making and outcomes?

Fraser Macdonald, Dental Tribune International

Wed. 22. April 2026

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RIYADH, Saudi Arabia: While many artificial intelligence (AI) systems for dental diagnosis demonstrate high accuracy, their real clinical value depends on their influence on treatment decision-making and patient outcomes. A new systematic review and meta-analysis has investigated this and presents the evidence so far on both the potential and the limitations of AI across specialties in clinical practice.

Crucially, the review moves beyond technical performance to assess how AI affects clinicians’ diagnostic judgement in practice. The evidence shows that AI assistance improved clinicians diagnostic performance and increased diagnostic agreement between clinicians interpreting the same images, suggesting that these systems can reduce variability in radiographic interpretation and serve as reliable adjuncts to clinical judgement.

AI also appears to contribute to faster diagnostic judgement and improved workflow efficiency. The review found that diagnostic tasks that typically require significant time can be completed far more quickly with AI support, without compromising accuracy, and that AI can help clinicians identify the location of abnormalities more accurately on dental images.

However, the review highlights an important evidence gap. While AI appears to support treatment planning indirectly by improving image interpretation and clinicians’ confidence in diagnosis, the review could draw no conclusions regarding its impact on treatment decisions and success. Evidence on treatment planning was limited by the small number of studies, variation in their clinical contexts and the absence of patient-centred outcomes.

Across multiple studies, the review found that AI systems demonstrated high accuracy in analysing dental images, particularly in detecting disease, identifying teeth and delineating anatomical boundaries, supporting diagnostic interpretation across different dental imaging contexts. However, differences in AI models, imaging techniques and validation methods led to high variability in diagnostic performance across dental tasks and clinical settings. Also, many studies relied on retrospective data, and few included external validation. This raises concerns about how well the findings apply across routine clinical settings.

Overall, AI shows strong potential to enhance diagnostic decision-making and support treatment planning, particularly as a tool to augment clinician judgement, something of central importance also to the patient experience. However, its true impact on treatment planning and patient outcomes remains unclear, and the authors point out that robust prospective research is needed to confirm its clinical value in routine practice.

The article, titled “Artificial intelligence in dental treatment planning and diagnostic decision‐making: A systematic review and meta‐analysis”, was published online in the April 2026 issue of Clinical and Experimental Dental Research.