Imaging modalities like X-rays, computerized tomography scans, and magnetic resonance imaging provide detailed views of teeth, bones, and soft tissues (White and Pharoah, 2014). These tools enhance the precision of diagnoses and treatments, ensuring better patient outcomes. Among the current imaging exams, radiographs are the most common in dentistry (White and Pharoah, 2014; Langlais and Miller, 2016), being requested to identify various pathologies like cavities, periodontal disease, impacted teeth, and bone infections (Chang et al., 2020; Yüksel et al., 2021) and track the progress of dental treatments. One of the most commonly used radiographs in dentistry is the panoramic radiograph (White and Pharoah, 2014; Langlais and Miller, 2016; Silva et al., 2018), which is an extraoral imaging technique where the X-ray film or sensor remains outside the patient's mouth during acquisition. In a single image, the panoramic radiograph provides a comprehensive view of both upper and lower jaws, but with less detail of the mouth structures (Haring and Jansen, 2000; Silva et al., 2018; Jader et al., 2018; Pinheiro et al., 2021). Figure 1 depicts an example of a panoramic radiograph, revealing the structures and their overlaps, which can lead to cluttered readings.

Robotic technologies are becoming increasingly popular in dentistry due to the high level of precision required in delicate dental procedures. Most dental robots available today are designed for implant surgery, helping dentists to accurately place implants in the desired position and depth. In this paper, we introduce the DentiBot, the first robot specifically designed for dental endodontic treatment. The DentiBot is equipped with a force and torque sensor, as well as a string-based Patient Tracking Module, allowing for real-time monitoring of endodontic file contact and patient movement. We propose a 6-DoF hybrid position/force controller that enables autonomous adjustment of the surgical path and compensation for patient movement, while also providing protection against endodontic file fracture. In addition, a file flexibility model is incorporated to compensate for file bending. Pre-clinical evaluations performed on acrylic root canal models and resin teeth confirm the feasibility of the DentiBot in assisting endodontic treatment.

Artificial intelligence, cognitive computing, machine learning, data mining, natural language processing: The terminology surrounding the efforts to enhance how businesses access, sort and analyze the vast quantities of content being created every day is almost as vast as the content itself -- and no less confusing. The words are often used interchangeably, and while some have clear definitions, others do not. Claims that artificial intelligence (AI) will transform health care, law and "business" as a whole are rampant, but with the term left undefined, it is difficult to sort what is blue sky and what is real opportunity. With everything from smart homes to self-driving cars to chatbots being labeled as artificial intelligence, the conversation should really start with a clear working definition of what AI is and what it's not. Artificial intelligence, in the broadest sense, is the act of a machine solving a problem similar to the way a human would.

As AI-powered automated personal assistants have increasingly found their way into our lives, their tremendous power at certain tasks has often been undermined by their inability to fuse the data available to them into a comprehensive view of our lives and, perhaps most importantly, their inability to actively anticipate our needs rather than merely passively respond to queries posted to them. How might adding proactive anticipatory reasoning and the ability to look across data allow our future AI assistants to be far more useful in our day-to-day lives? As I was taking a cab to the airport this past Friday, I noticed everywhere around me preparations for the Marine Corp Marathon, which I had completely forgotten was this weekend and which meant that when I returned early Sunday morning I was going to have difficulty getting home given all of the road closures in my neighborhood. Yet, despite having access to my calendar, which clearly noted my return flight arrival on Sunday, and being able to tell me that there was a giant marathon running directly through my neighborhood on Sunday with road closures all around my home, my AI assistant was unable to connect the two and anticipate that I might have trouble getting home via my usual route on Sunday. Heading to a meeting a week ago, my assistant could tell me there was a huge traffic delay along the way when I explicitly asked for a traffic update, but was unable on its own to connect that to my next calendar appointment and proactively suggest 30 minutes earlier that I leave half an hour early to avoid being late.