Sep 2026· International Dental Journal· 37 references
Artificial Intelligence in Healthcare and Education
Abstract
Artificial intelligence-based systems (AIS) are becoming part of dentistry. Their integration raises unresolved tensions for users such as trust, explainability, responsibility, professional autonomy and real-world usefulness. This concise review examines these tensions and discusses how they may shape the adoption of AIS in dentistry. This concise review was designed as a narrative review. An initial screening suggested that challenges associated with the implementation of AIS in dentistry appear to remain insufficiently explored in the dental literature. An author with expertise in marketing then formulated a list of AIS implementation issues considered well established in marketing research, drawing particularly on reference publications from leading journals. Based on these issues, the authors identified a set of tensions, and the dental authors translated them into concrete examples and implications for dental implementation. In dentistry, these tensions arise at three interconnected levels. At the human level, AIS may support diagnosis, risk communication and shared decision-making, but may also foster over-reliance or unjustified rejection. At the human–AIS interface, tools can flag lesions, prioritise cases or automate workflows that redistribute agency and require clear boundaries between assistance, delegation and responsibility. At the AIS level, dental applications face persistent issues of generalisability, privacy, adaptability and validation across populations, devices and care settings. The value of AIS in dentistry will depend less on technical performance alone than on the capacity to design, evaluate and implement user-centered tools with a co-designed approach involving all stakeholders, from patients and clinicians to institutions and decision-makers. Anticipating these tensions may help support trustworthy, context-sensitive and sustainable AIS adoption in oral health care.
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al.· arXiv.org· 62 citations· ⚡3
The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.
Zheying Zhang, M. Rayhan, Tomas Herda et al.· International Conference on...· 48 citations· ⚡4
This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.
Abdul Malik Sami, Z. Rasheed, Kai-Kristian Kemell et al.· arXiv.org· 44 citations· ⚡2
The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.
Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al.· arXiv.org· 41 citations
Exploring how generative AI could make machine vision more accessible to businesses. The post GenEye in a Box: Making Machine Vision Something You Can Just Ask For appeared first on GPT-Lab.
MIT News · Artificial Intelligence· news.mit.eduOct 8, 2026
Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks. Agent Lightning connects existing agents to RL training, making it easier to improve them without rebuilding them. The post Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses appeared first on Microsoft Research.
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