Cracking the heart code: using ChatGPT’s Data Analyst feature for cardiovascular imaging research

Cardiovascular diseases remain the leading cause of morbidity and mortality globally, posing significant challenges to public health. The rapid evolution of artificial intelligence (AI), particularly with large language models such as ChatGPT, has introduced transformative possibilities in cardiovascular medicine. This review examines ChatGPT’s broad applications in enhancing clinical decision-making—covering symptom analysis, risk assessment, and differential diagnosis; advancing medical education for both healthcare professionals and patients; and supporting research and academic communication. Key challenges associated with ChatGPT, including potential inaccuracies, ethical considerations, data privacy concerns, and inherent biases, are discussed. Future directions emphasize improving training data quality, developing specialized models, refining AI technology, and establishing regulatory frameworks to enhance ChatGPT’s clinical utility and mitigate associated risks. As cardiovascular medicine embraces AI, ChatGPT stands out as a powerful tool with substantial potential to improve therapeutic outcomes, elevate care quality, and advance research innovation. Fully understanding and harnessing this potential is essential for the future of cardiovascular health.

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How effective is an LLM-based Data Analysis Automation Tool? A Case Study with ChatGPT's Data Analyst

Miranda,

E. C. Campelo

2024

Anais Do XXXIX Simpósio Brasileiro De Banco De Dados (SBBD 2024)

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Artificial Intelligence (AI) tools are increasingly becoming integral to analytical processes. This paper evaluates the potential of Large Language Models (LLMs), specifically OpenAI's ChatGPT’s Data Analyst, in data analysis. We conducted a structured experiment employing this tool in 36 questions spanning descriptive, diagnostic, predictive, and prescriptive analyses to assess its effectiveness. The study revealed an overall efficiency rate of 86.11%, with robust performance in the descriptive and diagnostic categories but reduced efficacy in the more complex predictive and prescriptive tasks. By discussing the strengths and limitations of a state-of-the-art LLM-based tool in aiding data scientists, this study aims to mark a critical milestone for future developments in the field, particularly as a reference for the open-source community.

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Cracking the heart code: using ChatGPT’s Data Analyst feature for cardiovascular imaging research

Cited by 3 publications

References 4 publications

Using ChatGPT’s data analyst feature for cardiovascular imaging research: correspondence

Using ChatGPT’s data analyst feature for cardiovascular imaging research: correspondence

Harnessing the Power of ChatGPT in Cardiovascular Medicine: Innovations, Challenges, and Future Directions

How effective is an LLM-based Data Analysis Automation Tool? A Case Study with ChatGPT's Data Analyst

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