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.

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.

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.

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

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.

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.

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 cancer models.