Artificial intelligence in implant prosthetics: the role of evidence synthesis and transformation of clinical navigation

Modern implantology faces an exponential increase in scientific data, requiring clinicians to constantly adapt and filter information.

A new study, which compared the responses of ChatGPT and Google Gemini with the conclusions of 74 systematic reviews published in 2023–2025, shows a high level of concordance with the evidence base in implant prosthodontics, which has direct implications for clinical practice and educational programs.

Artificial intelligence as a navigation tool

The authors used a clinico-methodological approach in which clinical questions were formulated based on the objectives of the reviews and presented as identical prompts to both platforms — this increased the reproducibility of the comparison and reduced response variability caused by query formulation. In conditions of limited time and volume of literature read, the role of such systems is not to replace critical thinking, but to serve as a means of preliminary filtering, synthesis and structuring of data for further clinical analysis.

Study methodology

The comparison was based on a blinded assessment by two independent experts, which reduced the subjective contribution of the assessors and increased the reliability of the conclusions; key parameters included concordance of conclusions regarding therapeutic strategies, material and technical solutions and treatment outcomes. The use of identical prompts and the analysis of five clinical domains made it possible to determine the degree of concordance of AI responses with current recommendations and to identify areas where additional validation and clarification are required.

Clinical conclusions and limitations

For most domains, the responses of both models corresponded to the conclusions of systematic reviews without statistically significant differences, however differences were observed in the expressed confidence — ChatGPT more often indicated moderate confidence, while Gemini indicated high, which affects interpretation and decision-making. Limitations of the study include the potential for dataset bias, temporal lags in model updates, the absence of a detailed analysis of the quality of primary sources and the inability to replace individual clinical patient assessment.

Synchronization: confidence and validation

Confidence serves not merely as a metric — it affects clinical navigation and can serve as a marker for additional verification of information; consequently, mechanisms for calibration of trust, internal validation and documentation of the sources used by the model are required. At the institutional level it is important to implement algorithm validation protocols, periodic reassessment of models’ compliance with current recommendations and the involvement of multidisciplinary teams in verifying clinical responses.

Educational ecosystem: structure and content

Chatbots have the potential to accelerate the diffusion of knowledge, support the standardization of approaches and reduce barriers to the implementation of innovations — provided they are integrated into curricula, continuing medical education programs and clinical protocols. The role of artificial intelligence should be defined as auxiliary — a toolkit for preparing literature reviews, formulating differential diagnoses and treatment planning — with mandatory verification of conclusions by a clinical expert and documentation of the level of evidence.

Practical recommendations for clinicians

Use AI as an auxiliary tool when preparing for treatment planning and discussing complex cases; always verify key points in primary sources and systematic reviews; take into account the calibration of the model’s confidence when making decisions — with high confidence perform an additional critical appraisal, with moderate — seek confirmation in guidelines; implement local validation protocols and reference databases for verification of responses; document AI use in the medical record and inform the patient about the degree of uncertainty in the prognosis.

Conclusion

The study emphasizes that modern LLM platforms demonstrate promising concordance with the evidence base in implant prosthodontics and can become part of an integrated clinical ecosystem, however their role should remain auxiliary, with mandatory validation, critical interpretation and oversight by qualified specialists. Safe and effective implementation requires prompt standardization of prompts, regular model updates, multidisciplinary validation and educational initiatives aimed at improving media literacy among clinicians.

Source

Original publication