Artificial intelligence in scientific medical writing: Legitimate and deceptive uses and ethical concerns

Artificial Intelligence Use in Medical Education: Best Practices and Future Directions

PURPOSEOF REVIEW

This review examines the various ways artificial intelligence (AI) has been utilized in medical education (MedEd)and presents ideas that will ethically and effectively leverage AI in enhancing the learning experience of medical trainees.

RECENT FINDINGS

AI has improved accessibility to learning material in a manner that engages the wider population. It has utility as a reference tool and can assist academic writing by generating outlines, summaries and identifying relevant reference articles. As AI is increasingly integrated into MedEd and practice, its regulation should become a priority to prevent drawbacks to the education of trainees. By involving physicians in AI design and development, we can best preserve the integrity, quality, and clinical relevance of AI-generated content. In adopting the best practices for AI use, we can maximize its benefits while preserving the ethical standards of MedEd with the goal of improving learning outcomes.

The role of artificial intelligence in cardiovascular research: Fear less and live bolder

BACKGROUND

Artificial intelligence (AI) has captured the attention of everyone, including cardiovascular (CV) clinicians and scientists. Moving beyond philosophical debates, modern cardiology cannot overlook AI's growing influence but must actively explore its potential applications in clinical practice and research methodology.

METHODS AND RESULTS

AI offers exciting possibilities for advancing CV medicine by uncovering disease heterogeneity, integrating complex multimodal data, and enhancing treatment strategies. In this review, we discuss the innovative applications of AI in cardiac electrophysiology, imaging, angiography, biomarkers, and genomic data, as well as emerging tools like face recognition and speech analysis. Furthermore, we focus on the expanding role of machine learning (ML) in predicting CV risk and outcomes, outlining a roadmap for the implementation of AI in CV care delivery. While the future of AI holds great promise, technical limitations and ethical challenges remain significant barriers to its widespread clinical adoption.

CONCLUSIONS

Addressing these issues through the development of high-quality standards and involving key stakeholders will be essential for AI to transform cardiovascular care safely and effectively.