Current Issue : April-June Volume : 2026 Issue Number : 2 Articles : 5 Articles
Artificial intelligence (AI) is transforming healthcare and medical education, offering opportunities to enhance learning, diagnostic reasoning, and personalized instruction. However, its integration into undergraduate medical education (UME) remains a challenge, necessitating a balance between technological advancements and ethical considerations. This study investigates best practices for incorporating AI into UME curriculum through a qualitative thematic analysis of recent literature. Key themes identified include AI’s potential to improve medical student performance, ethical concerns related to privacy and bias, and the need for expanded AI education to allow for curricular integration. While AI offers significant benefits in medical training, challenges such as academic integrity, patient confidentiality, and the risk of over-reliance on AI highlight the necessity for structured, ethical, and evidence-based AI education. The findings underscore the importance of developing comprehensive curricula that equip future physicians with the knowledge and critical thinking skills required to navigate AI-assisted healthcare responsibly....
As artificial intelligence (AI) advances, there is growing interest in leveraging this technology to enhance climate change research and responses. While AI has been applied in quantitative climate research, its role in qualitative research remains underdeveloped. Yet, qualitative inquiry is essential for understanding how individuals perceive and experience the effects of climate change. This study aimed to both (1) gain a deeper understanding of New York City residents’ perceptions and lived experiences of climate change and (2) evaluate the suitability of AI for analyzing qualitative data. Using StreetTalk, a qualitative method involving street-intercept video interviews and social media dissemination, research teams analyzed interview transcripts through four approaches: humanonly, human-then-AI, AI-then-human, and AI-only. Co-authors were then provided with anonymized (blinded) versions of the final theme sets that they did not contribute to and evaluated them using a standardized rubric developed for this study. The AI-then-human approach produced the most comprehensive and contextually accurate results, yielding nine key themes: (1) personal responsibility and action, (2) community unity and support, (3) government and corporate responsibility, (4) concern for future generations, (5) climate change impact, (6) climate-related conspiracy theories, (7) low literacy around local climate change, (8) helplessness, and (9) competing interests around climate change. These findings provide valuable local perspectives to guide evidence-based strategies for climate mitigation and community engagement. This research also represents an initial step toward establishing best practices for integrating AI into qualitative data analysis....
Background and Objectives: Spirometry is the most widely used pulmonary function test for diagnosing respiratory diseases. Its progressive incorporation into non-specialized settings, such as primary care, raises challenges for ensuring the reliability of results. In this context, tools based on artificial intelligence (AI) techniques have emerged as promising solutions to support quality control in spirometry. This systematic review aims to synthesize the available evidence on their application in this field. Methods: A systematic search was conducted in PubMed and IEEE Xplore to identify peer-reviewed original studies, published between 2014 and June 2025, that applied AI to spirometry quality control. The search and data extraction followed the PRISMA guidelines. Results: Six studies met the inclusion criteria. Four analyzed the acceptability and usability of the maneuver, and two focused on detecting errors committed during test performance. The most widely used models were convolutional neural networks, used in four studies, whereas two studies employed other conventional machine learning models. Three models reported area under the ROC curve values higher than 0.88. Conclusions: AI-based tools show great potential to assist in spirometry quality control, both in determining acceptability and in detecting errors. However, current studies remain scarce and highly heterogeneous in both objectives and methods. Broader, multicenter research, including validation in non-specialized settings, is required to confirm their clinical utility and facilitate their implementation in clinical practice....
Background/Objectives: Arrhythmic recurrence is a common issue affecting a significant percentage of patients undergoing transcatheter ablation (TCA) of Atrial Fibrillation (AF). The use of artificial intelligence (AI) for the identification of electrocardiographic predictors of post-ablation recurrence may offer a valuable and cost-effective approach to improve risk stratification and optimize follow-up. This study aims to investigate the relationship between post-procedural electrocardiographic (ECG) P-wave parameters, measured using AI, and AF recurrence in patients undergoing transcatheter ablation (TCA). Methods: Seventy-four patients (age 62.36 ± 10.4 years) with a diagnosis of AF were retrospectively analyzed. ECGs were processed using AI software to analyze P-wave-related variables. All patients had either an implantable loop recorder (ILR) or another form of cardiac implantable electronic device (CIED). Results: Post-procedural P-wave amplitude in lead II (PwA in lead II) showed a significant association with AF recurrence, defined as an average arrhythmic burden >6% at one-year follow-up. Conclusions: These findings underscore the potential of PwA in lead II as a biomarker for the follow-up of patients undergoing TCA and highlight the contribution of AI in the analysis of electrocardiographic parameters predictive of AF recurrence. Together, these results may contribute to the development of early risk-stratification strategies following catheter ablation....
The fourth industrial revolution, driven by Artificial Intelligence (AI) and Generative AI (GenAI), is rapidly transforming human life, with profound effects on education, employment, operational efficiency, social behavior, and lifestyle. While AI tools potentially offer unprecedented support in learning and problem-solving, their integration into education raises critical questions about cognitive development and long-term intellectual capacity. Drawing parallels to previous industrial revolutions that reshaped human biological systems, this paper explores how GenAI introduces a new level of abstraction that may relieve humans from routine cognitive tasks, potentially enhancing performance but also risking a cognitively sedentary condition. We position levels of abstraction as the central theoretical lens to explain when GenAI reallocates cognitive effort toward higher-order reasoning and when it induces passive reliance. We present a conceptual model of AI-augmented versus passive trajectories in cognitive development and demonstrate its utility through a simulation-platform case study, which exposes concrete failure modes and the critical role of expert interventions. Rather than a hypothesis-testing empirical study, this paper offers a conceptual synthesis and concludes with mitigation strategies organized by abstraction layer, along with platform-centered implications for pedagogy, curriculum design, and assessment....
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