Sep 2026· Healthcare· Vol 14, pp. 3047· 74 references
Artificial Intelligence in Healthcare and Education
Abstract
Background/Objectives: Artificial intelligence is increasingly used in medicine, particularly in orthopedics, to support medical image interpretation, diagnosis, clinical decision-making, and treatment selection. Its integration into clinical practice depends largely on doctors’ perceptions and attitudes toward this technology. This study aimed to identify the main factors associated with Romanian orthopedic doctors’ attitudes toward AI and their intention to use it in clinical practice. Methods: The study included 166 doctors specializing in Orthopedics and Traumatology in Romania. Data were collected through an online questionnaire using non-probability snowball sampling. The conceptual model examined the associations between five factors and doctors’ attitudes toward AI: perceived usefulness, perceived ease of use, trust in AI, digital competencies, and perceived advantages. The relationship between attitude and intention to use AI was also analyzed. The model was tested using structural equation modeling with WarpPLS 8.0. Results: All factors were positively and significantly associated with doctors’ attitudes toward AI. Perceived usefulness showed the largest association with attitude (β = 0.27), followed by digital competencies (β = 0.22). The model explained 83% of the variance in attitude. Attitude was positively associated with intention to use AI (β = 0.65), with the model explaining 43% of the variance in intention. The model showed a GoF of 0.707 and an AFVIF of 2.645. Conclusions: The findings may support healthcare managers in developing digitalization strategies, structured digital competency training, and clear institutional guidelines for the safe and effective use of AI in clinical practice.
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
MIT professors Adam Berinsky and Chara Podimata discuss a new project to track how AI chatbots are responding to questions about the 2026 U.S. midterm elections.
Microsoft Research Blog· microsoft.comOct 30, 2026
Evaluating Microsoft Copilot at enterprise scale requires test data that captures the full complexity of real organizations, yet real enterprise data contains sensitive information that cannot be used for evaluation under any circumstances. The post From synthetic enterprise data evaluations to real Microsoft Copilot AI confidence appeared first on Microsoft Research.
Critical flaws, compromised release channels and agentic AI threats landed within days of each other in mid-October 2026. Why “Black October” may say more about how well we now find weaknesses than about the attacks themselves. The post Black October? The Week AI Changed the Cybersecurity Conversation appeared first on GPT-Lab.
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