generative ai

AISyst: AI‐Powered Interactive Visual System to Assist With Fidelity Assessment of Synthetic Tabular Data

Evaluating synthetic data produced by generative models remains a critical challenge in sensitive domains such as healthcare and finance. Ensuring that such data is ‘faithful’ to real data is essential for downstream applications and decision-making, including regulatory compliance. This paper introduces an AI-powered...

ARTIFICIAL INTELLIGENCE IN MODERN AND AYURVEDIC ANATOMY EDUCATION: CURRENT APPLICATIONS, CHALLENGES, AND FUTURE PERSPECTIVES

Anatomy education serves as the cornerstone of medical training across both modern and traditional medicine systems. However, traditional pedagogical approaches—relying heavily on cadaveric dissection and two-dimensional illustrations—face mounting challenges, including cadaver shortages, ethical concerns, and the need...

GENERATIVE AI AND SELF-MEDICATION: RISKS, RELIABILITY, AND PATIENT SAFETY IN THE ERA OF AI-BASED HEALTHCARE

Health-professions students are increasingly using artificial intelligence (AI) tools, especially large language model (LLM)-based chatbots like ChatGPT, for academic tasks as well as informal drug information retrieval, symptom checking, and self-medication decision-making.[2,9,3,7] Due to their dual roles as future g...

ARTIFICIAL INTELLIGENCE IN MODERN AND AYURVEDIC ANATOMY EDUCATION: CURRENT APPLICATIONS, CHALLENGES, AND FUTURE PERSPECTIVES

Anatomy education serves as the cornerstone of medical training across both modern and traditional medicine systems. However, traditional pedagogical approaches—relying heavily on cadaveric dissection and two-dimensional illustrations—face mounting challenges, including cadaver shortages, ethical concerns, and the need...

GENERATIVE AI AND SELF-MEDICATION: RISKS, RELIABILITY, AND PATIENT SAFETY IN THE ERA OF AI-BASED HEALTHCARE

Health-professions students are increasingly using artificial intelligence (AI) tools, especially large language model (LLM)-based chatbots like ChatGPT, for academic tasks as well as informal drug information retrieval, symptom checking, and self-medication decision-making.[2,9,3,7] Due to their dual roles as future g...

AI explained: a guide for non-technical readers

AI Explained: A Guide for Non-Technical Readers builds understanding of artificial intelligence from first principles rather than diving in at the top. Written by experienced policy and technical experts, the book walks through rules and logic-based approaches, statistical methods, neural networks, machine learning, an...

Personalizing Assignments in the AI Era: The Role of Engaged Pedagogy

Generative artificial intelligence (Gen AI) has taken the academic world by storm. This G.I.F.T.S. paper argues that personalizing academic assignments may curb students’ tendency to copy and paste content from Gen AI outputs. I demonstrate how I personalized a written assignment in one of my courses. A total of 81 pap...

AI Ethics in Industry: A Research Framework

This paper discusses a research framework for implementing AI ethics in industrial settings and presents a starting point for empirical studies into AI ethics but is still being developed further based on its practical utilization.

Implementing Ethics in AI: An industrial multiple case study

This paper provides a baseline for ethics in AI based software development by reporting results from an industrial multiple case study on AI systems development in the health care sector, and explores the current state of practice out on the field in the absence of formal methods and tools for ethically aligned design.

Continuous experimentation on artificial intelligence software: a research agenda

A holistic view of an iterative, continuous approach to develop industrial AI software basing on business goals, requirements and Minimum Viable Products is described and a research agenda with seven questions for future studies is proposed.

Time for AI (Ethics) Maturity Model Is Now

It is argued that AI software is still software and needs to be approached from the software development perspective, and whether the focus should be on AI ethics or the quality of an AI system, called a maturity model for the development of AI systems is discussed.

Implementing AI Ethics in a Software Engineering Project-Based Learning Environment - The Case of WIMMA Lab