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Open research questions in Dental Radiography and Imaging

557 gap statements mined from Dental Radiography and Imaging papers in our 7.9M-paper local library, which holds 3,432 papers on the topic — drawn mostly from each paper's own stated research gap, future-work, challenge and limitation notes, and its abstract. The ones listed below are a selection still marked open; each names the study that raised it, with a DOI link where the paper has one.

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What the literature leaves open

  • The study was retrospective. The study had a limited sample size of 60 patients. Discrepancies between AI-generated and clinician-derived treatment plans were observed in a subset of cases.

    Unveiling AI-Augmented Periodontal Decision-Making: A Comparative Clinical Evaluation of Diagnocat-Based Radiographic Deep Learning and ChatGPT-Driven Treatment Planning Versus Clinical Expert Judgement · 2026 · DOI

  • Clustering is a particularly important limitation. The studies did not address a common clinical question, making it difficult to estimate a pooled operating point. Most nominal uncertainty estimates could not account for clustering.

    Diagnostic Accuracy of Artificial Intelligence for Dental Caries Detection Across Clinical Imaging Modalities: A Systematic Review and Descriptive Synthesis · 2026 · DOI

  • The existing evidence has limitations, including high risk of bias and lack of external validation. There is a need for locked-model external validation and paired prospective evaluations with patient-relevant outcomes.

    Diagnostic Accuracy of Artificial Intelligence for Dental Caries Detection Across Clinical Imaging Modalities: A Systematic Review and Descriptive Synthesis · 2026 · DOI

  • Limited accuracy in assessing bone defects using traditional radiographic methods. Lack of a clear distinction between radiographic findings and clinical diagnosis of furcation involvement. Need for a more comprehensive preoperative evaluation.

    The application of deep learning in intelligent assessment of horizontal bone loss in mandibular first molars · 2026 · DOI

  • The lack of normal anatomical references in reconstructing extensive mandibular defects. The limitations of conventional methods for developing prediction models in real clinical scenarios.

    Comparison of multivariate linear regression and random forest model in developing mandibular chin prediction reconstruction equation · 2026 · DOI

  • The study had a limited sample size of 300 professionals. The response rate was approximately 30% for specialists and 40% for non-specialists. The study did not include specialists from other fields in the construct validation phase.

    Specialists’ agreement and construct validation of a visual scale for assessing mucosal healing after third molar extraction · 2026 · DOI

  • Limited studies have investigated the use of AI-based methods for predicting peri-implantitis onset. Prior studies have reported variable performance of CNN architectures in predicting peri-implantitis onset.

    Accuracy of transfer learning in predicting peri-implantitis onset from intraoral radiograph: A multi-center longitudinal retrospective study · 2026 · DOI

  • The study identifies the need for improved accuracy and precision in guided apical surgery. The study highlights the limitations of conventional freehand techniques in guided apical surgery. The study investigates the potential of digital guides to improve the outcomes of guided apical surgery.

    Accuracy of Surgical Guides in Guided Apical Surgery: An In Vitro Comparative Study · 2025 · DOI

  • The gap in the current literature is the lack of comparison between 3D DICOM data and PDF cone beam computer topography reports for buccal bone height measurements. The study aims to fill this gap by investigating the effect of viewing modality on buccal bone height measurements.

    Can Viewing Modality Affect Frontal Mandibular Bone Height Measurement? A Comparison Between 3D Digital Imaging and Communications in Medicine Viewer and Printed Portable Document Format Cone Beam Computer Tomography Reports · 2025 · DOI

  • There is a lack of proper evaluation and management of impacted wisdom teeth. The current clinical curriculum primarily focuses on simple exodontia, which may contribute to the gap.

    Assessing the Management and Evaluation of Impacted Wisdom Teeth in a Dental Teaching Hospital · 2025 · DOI

  • The available intraoral appliances do not provide adequate anchorage. Extraoral appliances are unreliable without patient cooperation. There is a need to investigate the relationships between facial morphology and alveolar crest cortical bone thickness.

    Relationship Between Facial Types and Alveolar Crest Cortical Bone Thickness and CT Values Determined by Multidetector Computed Tomography · 2025 · DOI

  • The need for a method that can effectively integrate high-quality semantic information from the Transformer branch into the CNN branch. The challenge of ineffective collaborative fusion between features from the CNN and Transformer dual-branch architectures.

    A Dual-Stream Dental Panoramic X-Ray Image Segmentation Method Based on Transformer Heterogeneous Feature Complementation · 2025 · DOI

  • The importance of supervised interpretation, especially in populations with low disease prevalence or in cases where radiographic artifacts and anatomical variability may mimic pathology, was noted. The study highlighted the need for refining AI models to further enhance the detection of subtle radiographic changes in elite athletes.

    Diagnostic Performance of AI-Assisted Software in Sports Dentistry: A Validation Study · 2025 · DOI

  • In conclusion, AI has the potential to improve diagnostics and influence treatment choices, but current evidence is limited and inconsistent regarding its impact on specificity, decision quality, and cost-effectiveness.

    Assessing the Role of Artificial Intelligence in Caries Detection and Clinical Decision-Making: A Scoping Review · 2026 · DOI

  • Further high‐quality, methodologically robust research is warranted to validate and optimize these approaches for clinical use.

    Diagnostic Performance of Artificial Intelligence in Detecting Periodontal Diseases Using Cone‐Beam Computed Tomography: A Systematic Review · 2026 · DOI

  • Combined clinical‐radiographic evaluation and prospective validation are warranted.

    Association Between Suspected Cemental Tears and Periodontal Breakdown: A Large Retrospective Study on Periapical Radiographs · 2026 · DOI

  • The study was limited to a secondary analysis of a dataset from a previous cross-sectional study. The study only accounted for information regarding the conditions defined for the goal of the study (dental caries, periodontitis and periapical lesions). The AI software was used in its standard operating mode, without manual overrides or post-processing.

    Diagnostic Performance of AI-Assisted Software in Sports Dentistry: A Validation Study · 2025 · DOI

  • The study was retrospective, - The study had a limited sample size of 110 CBCT scans, - The study only included adult patients with atrophied mandibles, - The study did not provide evidence of clinical…

    CBCT-Based Anatomical Assessment of Lingual Bone Availability Relevant to the Mental Foramen Bypass Concept in Posterior Atrophied Mandibles · 2026 · DOI

  • Further studies are needed to investigate the clinical efficacy of the findings, - Future research should focus on the application of CBCT in implant planning and placement, - Studies should be conducted to compare the results of this study with other anatomical assessment…

    CBCT-Based Anatomical Assessment of Lingual Bone Availability Relevant to the Mental Foramen Bypass Concept in Posterior Atrophied Mandibles · 2026 · DOI

  • The study relies on a specific dataset of CBCT images, - The model's performance may not generalize to other types of images or datasets, - The study does not provide a direct comparison with other deep learning models, - The study is limited to the diagnosis of horizontal bone loss in mandibular first…

    The application of deep learning in intelligent assessment of horizontal bone loss in mandibular first molars · 2026 · DOI

  • Refinement of the scale to improve inter-rater agreement. Further validation of the scale in different clinical settings. Investigation of the scale’s utility in other areas of dentistry.

    Specialists’ agreement and construct validation of a visual scale for assessing mucosal healing after third molar extraction · 2026 · DOI

  • Investigating other CNN architectures, - Exploring other transfer learning approaches, - Applying the model to larger…

    Accuracy of transfer learning in predicting peri-implantitis onset from intraoral radiograph: A multi-center longitudinal retrospective study · 2026 · DOI

  • The study was conducted at a single dental hospital, - The sample size was limited to 270 cases, - The study was retrospective, - The study relied on electronic medical records and panoramic…

    Assessing the Management and Evaluation of Impacted Wisdom Teeth in a Dental Teaching Hospital · 2025 · DOI

  • Limited to 3D dental segmentation, - Requires a small proportion of labeled data, - Computational cost reduction is compared to existing self-supervised…

    Multi-Encoding Contrastive Learning for Dual-Stream Self-Supervised 3D Dental Segmentation Network · 2025 · DOI

  • In-depth exploration of datasets, - Handling of abstract and complex data patterns, - Robust learning…

    Comparison of multivariate linear regression and random forest model in developing mandibular chin prediction reconstruction equation · 2026 · DOI

Most-cited papers in Dental Radiography and Imaging

Most recent work

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This page shows what the Dental Radiography and Imaging literature already flags as unresolved. To narrow it to your specific question, search the Research Gap Finder: the search is free with a free account and lists the papers closest to your topic first. Unlocking that topic (50 credits, charged once) fills the comparison table from our 7.9M-paper local library and writes the gaps from its rows.

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Open questions raised by papers listed on this page include: (1) The existing evidence has limitations, including high risk of bias and lack of external validation (“Diagnostic Accuracy of Artificial Intelligence for Dental Caries Detection…”, 2026); (2) Lack of a clear distinction between radiographic findings and clinical diagnosis of furcation involvement (“The application of deep learning in intelligent assessment of horizontal bone…”, 2026); (3) The lack of normal anatomical references in reconstructing extensive mandibular defects (“Comparison of multivariate linear regression and random forest model in…”, 2026); (4) Further high‐quality, methodologically robust research is warranted to validate and optimize these approaches for clinical use (“Diagnostic Performance of Artificial Intelligence in Detecting Periodontal…”, 2026); (5) Combined clinical‐radiographic evaluation and prospective validation are warranted (“Association Between Suspected Cemental Tears and Periodontal Breakdown: A Large…”, 2026). 557 gap statements have been mined from Dental Radiography and Imaging papers in our 7.9M-paper local library, which holds 3,432 papers on the topic; the gaps come from whichever of those papers state one. They are mostly the research gaps the authors state and the papers' abstracts, plus future-work, limitations and challenges passages. The ones listed below are a selection still marked open; each names the study that raised it, with a DOI link where the paper has one, so you can read the original claim in context.

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