Andrzej A Kononowicz
Biographic Data
| ID | 7720101 |
|---|---|
| NAME | Andrzej A Kononowicz |
| GIVEN NAMES | Andrzej A |
| FAMILY NAME | Kononowicz |
| SIGNATURE | KONONOWICZ A A |
| AFFILIATIONS | Jagiellonian University |
| ORCID | 0000-0003-2956-2093 |
| VERIFIED | Yes |
| TOTAL WORKS | 7 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 7 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2019 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
ChatGPT versus human authors
Purpose This study investigates whether ChatGPT can generate clinically accurate and pedagogically valuable maps for clinical reasoning (CR) training. The aim is to assess its potential as a tool for supporting the creation of high-quality educational resources for CR training.Materials and methods We selected 10 diverse virtual patients (VPs) from the European iCoViP project. For each case, CR concept maps were generated by a custom ChatGPT mode…
Large language models for generating key-feature questions in medical education
In this study, we conducted a descriptive study to evaluate the quality of KFQs generated by OpenAI's o3 model. We developed a reusable generic prompt for KFQ generation, designed in alignment with the Medical Council of Canada's KFQ development guidelines. We also created an evaluation metric to systematically assess the quality of the KFQs based on the KFQ development guideline. Twenty unique cardiology-focused KFQs were created using recent Eu…
Case-based MCQ generator
WHAT IS THE EDUCATIONAL CHALLENGE?: A fundamental challenge in medical education is creating high-quality, clinically relevant multiple-choice questions (MCQs). ChatGPT-based automatic item generation (AIG) methods need well-designed prompts. However, the use of these prompts is hindered by the time-consuming process of copying and pasting, a lack of know-how among medical teachers, and the generalist nature of standard ChatGPT, which often lacks…
Planning a Collection of Virtual Patients to Train Clinical Reasoning
The development of the blueprint required a stepwise approach, which can be replicated for the creation of other VP or case collections. We consider the blueprint an appropriate starting point for the actual creation of the VPs, but constant updating and refining is needed
Clinical Reasoning Needs to Be Explicitly Addressed in Health Professions Curricula
Clinical reasoning entails the application of knowledge and skills to collect and integrate information, typically with the goal of arriving at a diagnosis and management plan based on the patient's unique circumstances and preferences. Evidence-informed, structured, and explicit teaching and assessment of clinical reasoning in educational programs of medical and other health professions remain unmet needs. We herein summarize recommendations for…
The need for longitudinal clinical reasoning teaching and assessment
Background: Clinical reasoning is a key ability essential for practising health professionals. However, little is known about the current global adoption of clinical reasoning teaching and assessment.Purpose: We aimed to provide insights into how clinical reasoning is deliberately taught and assessed in curricula worldwide and to identify needs and perceived barriers for teaching clinical reasoning to students and educators.Methods: A questionnai…
Virtual Reality for Health Professions Education
No prominent works on this page.
Virtual Reality for Health Professions Education
The need for longitudinal clinical reasoning teaching and assessment
Background: Clinical reasoning is a key ability essential for practising health professionals. However, little is known about the current global adoption of clinical reasoning teaching and assessment.Purpose: We aimed to provide insights into how clinical reasoning is deliberately taught and assessed in curricula worldwide and to identify needs and perceived barriers for teaching clinical reasoning to students and educators.Methods: A questionnai…
Clinical Reasoning Needs to Be Explicitly Addressed in Health Professions Curricula
Clinical reasoning entails the application of knowledge and skills to collect and integrate information, typically with the goal of arriving at a diagnosis and management plan based on the patient's unique circumstances and preferences. Evidence-informed, structured, and explicit teaching and assessment of clinical reasoning in educational programs of medical and other health professions remain unmet needs. We herein summarize recommendations for…
Planning a Collection of Virtual Patients to Train Clinical Reasoning
The development of the blueprint required a stepwise approach, which can be replicated for the creation of other VP or case collections. We consider the blueprint an appropriate starting point for the actual creation of the VPs, but constant updating and refining is needed
Case-based MCQ generator
WHAT IS THE EDUCATIONAL CHALLENGE?: A fundamental challenge in medical education is creating high-quality, clinically relevant multiple-choice questions (MCQs). ChatGPT-based automatic item generation (AIG) methods need well-designed prompts. However, the use of these prompts is hindered by the time-consuming process of copying and pasting, a lack of know-how among medical teachers, and the generalist nature of standard ChatGPT, which often lacks…
Large language models for generating key-feature questions in medical education
In this study, we conducted a descriptive study to evaluate the quality of KFQs generated by OpenAI's o3 model. We developed a reusable generic prompt for KFQ generation, designed in alignment with the Medical Council of Canada's KFQ development guidelines. We also created an evaluation metric to systematically assess the quality of the KFQs based on the KFQ development guideline. Twenty unique cardiology-focused KFQs were created using recent Eu…
ChatGPT versus human authors
Purpose This study investigates whether ChatGPT can generate clinically accurate and pedagogically valuable maps for clinical reasoning (CR) training. The aim is to assess its potential as a tool for supporting the creation of high-quality educational resources for CR training.Materials and methods We selected 10 diverse virtual patients (VPs) from the European iCoViP project. For each case, CR concept maps were generated by a custom ChatGPT mode…
Clinical Reasoning and Diagnostic Skills (5 works) · Medicine (4 works) · Psychology (4 works) · Artificial Intelligence in Healthcare and Education (3 works) · Computer Science (3 works) · Innovations in Medical Education (3 works) · Medical education (3 works) · Radiology practices and education (3 works) · Curriculum (2 works) · Descriptive statistics (2 works)