Performance of AI chatbots on controversial topics in oral medicine, pathology, and radiology.

OBJECTIVES: In this study, we assessed 6 different artificial intelligence (AI) chatbots (Bing, GPT-3.5, GPT-4, Google Bard, Claude, Sage) responses to controversial and difficult questions in oral pathology, oral medicine, and oral radiology. STUDY DESIGN: The chatbots' answers were evaluated by board-certified specialists using a modified version of the global quality score on a 5-point Likert scale. The quality and validity of chatbot citations were evaluated. RESULTS: Claude had the highest mean score of 4.341 ± 0.582 for oral pathology and medicine. Bing had the lowest scores of 3.447 ± 0.566. In oral radiology, GPT-4 had the highest mean score of 3.621 ± 1.009 and Bing the lowest score of 2.379 ± 0.978. GPT-4 achieved the highest mean score of 4.066 ± 0.825 for performance across all disciplines. 82 out of 349 (23.50%) of generated citations from chatbots were fake. CONCLUSIONS: The most superior chatbot in providing high-quality information for controversial topics in various dental disciplines was GPT-4. Although the majority of chatbots performed well, it is suggested that developers of AI medical chatbots incorporate scientific citation authenticators to validate the outputted citations given the relatively high number of fabricated citations.

Generative Adversarial Networks in Digital Histopathology: Current Applications, Limitations, Ethical Considerations, and Future Directions.

Generative adversarial networks (GANs) have gained significant attention in the field of image synthesis, particularly in computer vision. GANs consist of a generative model and a discriminative model trained in an adversarial setting to generate realistic and novel data. In the context of image synthesis, the generator produces synthetic images, whereas the discriminator determines their authenticity by comparing them with real examples. Through iterative training, the generator allows the creation of images that are indistinguishable from real ones, leading to high-quality image generation. Considering their success in computer vision, GANs hold great potential for medical diagnostic applications. In the medical field, GANs can generate images of rare diseases, aid in learning, and be used as visualization tools. GANs can leverage unlabeled medical images, which are large in size, numerous in quantity, and challenging to annotate manually. GANs have demonstrated remarkable capabilities in image synthesis and have the potential to significantly impact digital histopathology. This review article focuses on the emerging use of GANs in digital histopathology, examining their applications and potential challenges. Histopathology plays a crucial role in disease diagnosis, and GANs can contribute by generating realistic microscopic images. However, ethical considerations arise because of the reliance on synthetic or pseudogenerated images. Therefore, the manuscript also explores the current limitations and highlights the ethical considerations associated with the use of this technology. In conclusion, digital histopathology has seen an emerging use of GANs for image enhancement, such as color (stain) normalization, virtual staining, and ink/marker removal. GANs offer significant potential in transforming digital pathology when applied to specific and narrow tasks (preprocessing enhancements). Evaluating data quality, addressing biases, protecting privacy, ensuring accountability and transparency, and developing regulation are imperative to ensure the ethical application of GANs.

Detection of extracranial and intracranial calcified carotid artery atheromas in cone beam computed tomography using a deep learning convolutional neural network image segmentation approach.

OBJECTIVE: We leveraged an artificial intelligence deep-learning convolutional neural network (DL CNN) to detect calcified carotid artery atheromas (CCAAs) on cone beam computed tomography (CBCT) images. STUDY DESIGN: We obtained 137 full-volume CBCT scans with previously diagnosed CCAAs. The DL model was trained on 170 single axial CBCT slices, 90 with extracranial CCAAs and 80 with intracranial CCAAs. A board-certified oral and maxillofacial radiologist confirmed the presence of each CCAA. Transfer learning through a U-Net-based CNN architecture was utilized. Data allocation was 60% training, 10% validation, and 30% testing. We determined the accuracy of the DL model in detecting CCAA by calculating the mean training and validation accuracy and the area under the receiver operating characteristic curve (AUC). We reserved 5 randomly selected unseen full CBCT volumes for final testing. RESULTS: The mean training and validation accuracy of the model in detecting extracranial CCAAs was 92% and 82%, respectively, and the AUC was 0.84 with 1.0 sensitivity and 0.69 specificity. The mean training and validation accuracy in detecting intracranial CCAAs was 61% and 70%, respectively, and the AUC was 0.5 with 0.93 sensitivity and 0.08 specificity. Testing of full-volume scans yielded an AUC of 0.72 and 0.55 for extracranial and intracranial CCAAs, respectively. CONCLUSION: Our DL model showed excellent discrimination in detecting extracranial CCAAs on axial CBCT images and acceptable discrimination on full-volumes but poor discrimination in detecting intracranial CCAAs, for which further research is required.

Central mechanisms of pain in orofacial pain patients: Implications for management.

BACKGROUND: Central sensitization (CS) is a form of neuroplasticity characterized by changes in the neural sensitivity, responsiveness, and/or output that are not contingent on peripheral input nor activity-dependent. CS is characterized by activation of unmyelinated C-fibers resulting in a cascade of events at molecular and cellular levels which eventuate into generation of synaptic currents at rest. CS, therefore, contributes to heightened generalized pain sensitivity, further complicates the process of reaching a diagnosis, and increases the possibility of treatment failure. BODY: Trigeminal nerve is the main sensory supplier of the anterior part of the head, including the intraoral structures. Primary afferent nociceptors of the trigeminal nerve and low threshold mechanoreceptors synapse with wide dynamic range (WDR) neurons in the pons. This multifaceted network of nerve interactions which is further complicated by the modulatory circuits that can suppress or heighten the activity of WDR neurons is one of the main contributors to CS. The importance of CS in orofacial pain disorders is emphasized in the context of chronic pain development. As for all chronic pain conditions, it is crucial to consider the biopsychosocial aspects of chronic orofacial pain in managing this diverse group of conditions. This review highlights current understanding of the biopsychosocial model and central mechanisms contributing to the pathogenesis of chronic orofacial pain.

The use of artificial intelligence, machine learning and deep learning in oncologic histopathology.

BACKGROUND: Recently, there has been a momentous drive to apply advanced artificial intelligence (AI) technologies to diagnostic medicine. The introduction of AI has provided vast new opportunities to improve health care and has introduced a new wave of heightened precision in oncologic pathology. The impact of AI on oncologic pathology has now become apparent, and its use with respect to oral oncology is still in the nascent stage. DISCUSSION: A foundational overview of AI classification systems used in medicine and a review of common terminology used in machine learning and computational pathology will be presented. This paper provides a focused review on the recent advances in AI and deep learning in oncologic histopathology and oral oncology. In addition, specific emphasis on recent studies that have applied these technologies to oral cancer prognostication will also be discussed. CONCLUSION: Machine and deep learning methods designed to enhance prognostication of oral cancer have been proposed with much of the work focused on prediction models on patient survival and locoregional recurrences in patients with oral squamous cell carcinomas (OSCC). Few studies have explored machine learning methods on OSCC digital histopathologic images. It is evident that further research at the whole slide image level is needed and future collaborations with computer scientists may progress the field of oral oncology.

Suppression of Adaptive Responses to Targeted Cancer Therapy by Transcriptional Repression.

Acquired drug resistance is a major factor limiting the effectiveness of targeted cancer therapies. Targeting tumors with kinase inhibitors induces complex adaptive programs that promote the persistence of a fraction of the original cell population, facilitating the eventual outgrowth of inhibitor-resistant tumor clones. We show that the addition of a newly identified CDK7/12 inhibitor, THZ1, to targeted therapy enhances cell killing and impedes the emergence of drug-resistant cell populations in diverse cellular and in vivo cancer models. We propose that targeted therapy induces a state of transcriptional dependency in a subpopulation of cells poised to become drug tolerant, which THZ1 can exploit by blocking dynamic transcriptional responses, promoting remodeling of enhancers and key signaling outputs required for tumor cell survival in the setting of targeted therapy. These findings suggest that the addition of THZ1 to targeted therapies is a promising broad-based strategy to hinder the emergence of drug-resistant cancer cell populations.Significance: CDK7/12 inhibition prevents active enhancer formation at genes, promoting resistance emergence in response to targeted therapy, and impedes the engagement of transcriptional programs required for tumor cell survival. CDK7/12 inhibition in combination with targeted cancer therapies may serve as a therapeutic paradigm for enhancing the effectiveness of targeted therapies. Cancer Discov; 8(1); 59-73. ©2017 AACR.See related commentary by Carugo and Draetta, p. 17This article is highlighted in the In This Issue feature, p. 1.