Generative artificial intelligence in dentistry: Current approaches and future challenges

• URL: http://arxiv.org/abs/2407.17532v1
• Date: Wed, 24 Jul 2024 03:33:47 GMT
• Title: Generative artificial intelligence in dentistry: Current approaches and future challenges
• Authors: Fabián Villena, Claudia Véliz, Rosario García-Huidobro, Sebastián Aguayo,
• Abstract summary: generative AI (GenAI) models bridge the usability gap of AI by providing a natural language interface to interact with complex models. In dental education, the student now has the opportunity to solve a plethora of questions by only prompting a GenAI model. GenAI can also be used in dental research, where the applications range from new drug discovery to assistance in academic writing.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Artificial intelligence (AI) has become a commodity for people because of the advent of generative AI (GenAI) models that bridge the usability gap of AI by providing a natural language interface to interact with complex models. These GenAI models range from text generation - such as two-way chat systems - to the generation of image or video from textual descriptions input by a user. These advancements in AI have impacted Dentistry in multiple aspects. In dental education, the student now has the opportunity to solve a plethora of questions by only prompting a GenAI model and have the answer in a matter of seconds. GenAI models can help us deliver better patient healthcare by helping practitioners gather knowledge quickly and efficiently. Finally, GenAI can also be used in dental research, where the applications range from new drug discovery to assistance in academic writing. In this review, we first define GenAI models and describe their multiple generation modalities; then, we explain and discuss their current and potential applications in Dentistry; and finally, we describe the challenges these new technologies impose in our area.

Related papers

• Early Adoption of Generative Artificial Intelligence in Computing Education: Emergent Student Use Cases and Perspectives in 2023 [38.83649319653387]
  There is limited prior research on computing students' use and perceptions of GenAI. We surveyed all computer science majors in a small engineering-focused R1 university. We discuss the impact of our findings on the emerging conversation around GenAI and education.
  arXiv  Detail & Related papers  (2024-11-17T20:17:47Z)
• Model-based Maintenance and Evolution with GenAI: A Look into the Future [47.93555901495955]
  We argue that Generative Artificial Intelligence (GenAI) can be used as a means to address the limitations of Model-Based Engineering (MBM&E) We propose that GenAI can be used in MBM&E for: reducing engineers' learning curve, maximizing efficiency with recommendations, or serving as a reasoning tool to understand domain problems.
  arXiv  Detail & Related papers  (2024-07-09T23:13:26Z)
• Explainable Generative AI (GenXAI): A Survey, Conceptualization, and Research Agenda [1.8592384822257952]
  We elaborate on why XAI has gained importance with the rise of GenAI and its challenges for explainability research. We also unveil novel and emerging desiderata that explanations should fulfill, covering aspects such as verifiability, interactivity, security, and cost.
  arXiv  Detail & Related papers  (2024-04-15T08:18:16Z)
• Prompt Smells: An Omen for Undesirable Generative AI Outputs [4.105236597768038]
  We propose two new concepts that will aid the research community in addressing limitations associated with the application of GenAI models. First, we propose a definition for the "desirability" of GenAI outputs and three factors which are observed to influence it. Second, drawing inspiration from Martin Fowler's code smells, we propose the concept of "prompt smells" and the adverse effects they are observed to have on the desirability of GenAI outputs.
  arXiv  Detail & Related papers  (2024-01-23T10:10:01Z)
• Identifying and Mitigating the Security Risks of Generative AI [179.2384121957896]
  This paper reports the findings of a workshop held at Google on the dual-use dilemma posed by GenAI. GenAI can be used just as well by attackers to generate new attacks and increase the velocity and efficacy of existing attacks. We discuss short-term and long-term goals for the community on this topic.
  arXiv  Detail & Related papers  (2023-08-28T18:51:09Z)
• Large-scale Generative Simulation Artificial Intelligence: the Next Hotspot in Generative AI [12.393966743563544]
  GenAI has impressed us with substantial breakthroughs in natural language processing and computer vision. LS-GenAI is the next hotspot for GenAI to connect.
  arXiv  Detail & Related papers  (2023-08-03T02:04:04Z)
• Investigating Explainability of Generative AI for Code through Scenario-based Design [44.44517254181818]
  generative AI (GenAI) technologies are maturing and being applied to application domains such as software engineering. We conduct 9 workshops with 43 software engineers in which real examples from state-of-the-art generative AI models were used to elicit users' explainability needs. Our work explores explainability needs for GenAI for code and demonstrates how human-centered approaches can drive the technical development of XAI in novel domains.
  arXiv  Detail & Related papers  (2022-02-10T08:52:39Z)
• GenNI: Human-AI Collaboration for Data-Backed Text Generation [102.08127062293111]
  Table2Text systems generate textual output based on structured data utilizing machine learning. GenNI (Generation Negotiation Interface) is an interactive visual system for high-level human-AI collaboration in producing descriptive text.
  arXiv  Detail & Related papers  (2021-10-19T18:07:07Z)
• A User-Centred Framework for Explainable Artificial Intelligence in Human-Robot Interaction [70.11080854486953]
  We propose a user-centred framework for XAI that focuses on its social-interactive aspect. The framework aims to provide a structure for interactive XAI solutions thought for non-expert users.
  arXiv  Detail & Related papers  (2021-09-27T09:56:23Z)
• Empowering Things with Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things [98.10037444792444]
  We show how AI can empower the IoT to make it faster, smarter, greener, and safer. First, we present progress in AI research for IoT from four perspectives: perceiving, learning, reasoning, and behaving. Finally, we summarize some promising applications of AIoT that are likely to profoundly reshape our world.
  arXiv  Detail & Related papers  (2020-11-17T13:14:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.