Harnessing the power of collective intelligence in dentistry: a pilot study in Victoria, Australia.

BACKGROUND: In many dental settings, diagnosis and treatment planning is the responsibility of a single clinician, and this process is inevitably influenced by the clinician's own heuristics and biases. Our aim was to test whether collective intelligence increases the accuracy of individual diagnoses and treatment plans, and whether such systems have potential to improve patient outcomes in a dental setting. METHODS: This pilot project was carried out to assess the feasibility of the protocol and appropriateness of the study design. We used a questionnaire survey and pre-post study design in which dental practitioners were involved in the diagnosis and treatment planning of two simulated cases. Participants were provided the opportunity to amend their original diagnosis/treatment decisions after viewing a consensus report made to simulate a collaborative setting. RESULTS: Around half (55%, n = 17) of the respondents worked in group private practices, however most practitioners (74%, n = 23) did not collaborate when planning treatment. Overall, the average practitioners' self-confidence score in managing different dental disciplines was 7.22 (s.d. 2.20) on a 1-10 scale. Practitioners tended to change their mind after viewing the consensus response, particularly for the complex case compared to the simple case (61.5% vs 38.5%, respectively). Practitioners' confidence ratings were also significantly higher (p < 0.05) after viewing the consensus for complex case. CONCLUSION: Our pilot study shows that collective intelligence in the form of peers' opinion can lead to modifications in diagnosis and treatment planning by dentists. Our results lay the foundations for larger scale investigations on whether peer collaboration can improve diagnostic accuracy, treatment planning and, ultimately, oral health outcomes.

Accuracy of Treatment Recommendations by Pragmatic Evidence Search and Artificial Intelligence: An Exploratory Study.

There is extensive literature emerging in the field of dentistry with the aim to optimize clinical practice. Evidence-based guidelines (EBGs) are designed to collate diagnostic criteria and clinical treatment for a range of conditions based on high-quality evidence. Recently, advancements in Artificial Intelligence (AI) have instigated further queries into its applicability and integration into dentistry. Hence, the aim of this study was to develop a model that can be used to assess the accuracy of treatment recommendations for dental conditions generated by individual clinicians and the outcomes of AI outputs. For this pilot study, a Delphi panel of six experts led by CoTreat AI provided the definition and developed evidence-based recommendations for subgingival and supragingival calculus. For the rapid review-a pragmatic approach that aims to rapidly assess the evidence base using a systematic methodology-the Ovid Medline database was searched for subgingival and supragingival calculus. Studies were selected and reported based on the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA), and this study complied with the minimum requirements for completing a restricted systematic review. Treatment recommendations were also searched for these same conditions in ChatGPT (version 3.5 and 4) and Bard (now Gemini). Adherence to the recommendations of the standard was assessed using qualitative content analysis and agreement scores for interrater reliability. Treatment recommendations by AI programs generally aligned with the current literature, with an agreement of up to 75%, although data sources were not provided by these tools, except for Bard. The clinician's rapid review results suggested several procedures that may increase the likelihood of overtreatment, as did GPT4. In terms of overall accuracy, GPT4 outperformed all other tools, including rapid review (Cohen's kappa 0.42 vs. 0.28). In summary, this study provides preliminary observations for the suitability of different evidence-generating methods to inform clinical dental practice.

Determinants of Clinical Decision Making under Uncertainty in Dentistry: A Scoping Review.

Clinical decision-making for diagnosing and treating oral and dental diseases consolidates multiple sources of complex information, yet individual clinical judgements are often made intuitively on limited heuristics to simplify decision making, which may lead to errors harmful to patients. This study aimed at systematically evaluating dental practitioners' clinical decision-making processes during diagnosis and treatment planning under uncertainty. A scoping review was chosen as the optimal study design due to the heterogeneity and complexity of the topic. Key terms and a search strategy were defined, and the articles published in the repository of the National Library of Medicine (MEDLINE/PubMed) were searched, selected, and analysed in accordance with PRISMA-ScR guidelines. Of the 478 studies returned, 64 relevant articles were included in the qualitative synthesis. Studies that were included were based in 27 countries, with the majority from the UK and USA. Articles were dated from 1991 to 2022, with all being observational studies except four, which were experimental studies. Six major recurring themes were identified: clinical factors, clinical experience, patient preferences and perceptions, heuristics and biases, artificial intelligence and informatics, and existing guidelines. These results suggest that inconsistency in treatment recommendations is a real possibility and despite great advancements in dental science, evidence-based practice is but one of a multitude of complex determinants driving clinical decision making in dentistry. In conclusion, clinical decisions, particularly those made individually by a dental practitioner, are potentially prone to sub-optimal treatment and poorer patient outcomes.