Masafumi Shibata, MD, PhD
Clinical Instructor, Cardiothoracic Surgery
Administrative Appointments
-
Clinical Instructor, Cardiothoracic Surgery (2024 - Present)
-
Visiting Instructor, Cardiothoracic Surgery (2022 - 2024)
-
Special Research Scholar, Department of Cardiovascular Surgery, Nippon Medical School Hospital, Tokyo, Japan (2023 - Present)
-
Assistant Professor, Department of Cardiovascular Surgery, Nippon Medical School Hospital, Tokyo, Japan (2019 - 2022)
Honors & Awards
-
TSF Scholarship, Thoracic Surgery Foundation (2024)
-
Pilot Funding for Educational and Research Projects to Enhance Diversity, Equity, and Inclusion, Department of Cardiothoracic Surgery, Stanford University (2023)
-
Travel Grant, Nippon Medical School (2019)
-
Winner, U40 OPCAB contest (2018)
-
Finalist, Challengers’ Live Demonstrations (2016)
Boards, Advisory Committees, Professional Organizations
-
Board Certified Cardiovascular Surgeon, Japanese Society for Cardiovascular Surgery (2020 - Present)
-
Board Certified Surgeon, Japan Surgical Society (2020 - Present)
-
Member, Japan Surgical Society (2010 - Present)
-
Member, Japanese Association for Thoracic Surgery (2012 - Present)
-
Member, Japanese Society for Cardiovascular Surgery (2012 - Present)
-
Member, Japanese Circulation Society (2012 - Present)
-
Member, Stanford Cardiovascular Institute (2023 - Present)
Professional Education
-
Research Fellow, Heart Center Leipzig at the University of Leipzig, Leipzig, Germany, Structural Heart Disease (2022)
-
Board Certification, Japanese Board of Cardiovascular Surgery, Cardiovascular Surgery (2020)
-
PhD, Graduate School of Medicine, Nippon Medical School, Tokyo, Japan (2019)
-
Residency, Nippon Medical School Hospital, Tokyo, Japan (2012)
-
Medical Education, Nippon Medical School, Tokyo, Japan, Medicine (2010)
All Publications
-
Harnessing the Power of ChatGPT in Cardiovascular Medicine: Innovations, Challenges, and Future Directions.
Journal of clinical medicine
2024; 13 (21)
Abstract
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.
View details for DOI 10.3390/jcm13216543
View details for PubMedID 39518681
-
Effect of graft sizing in valve-sparing aortic root replacement for bicuspid aortic valve: The Goldilocks ratio.
JTCVS techniques
2024; 25: 1-7
Abstract
To investigate the effect of graft sizing on valve performance in valve-sparing aortic root replacement for bicuspid aortic valve.In addition to a diseased control model, 3 representative groups-free-edge length to aortic/graft diameter (FELAD) ratio <1.3, 1.5 to 1.64, and >1.7-were replicated in explanted porcine aortic roots (n = 3) using straight grafts sized respective to the native free-edge length. They were run on a validated ex vivo univentricular system under physiological parameters for 20 cycles. All groups were tested within the same aortic root to minimize inter-root differences. Outcomes included transvalvular gradient, regurgitation fraction, and orifice area. Linear mixed effects model and pairwise comparisons were employed to compare outcomes across groups.The diseased control had mean transvalvular gradient 10.9 ± 6.30 mm Hg, regurgitation fraction 32.5 ± 4.91%, and orifice area 1.52 ± 0.12 cm2. In ex vivo analysis, all repair groups had improved regurgitation compared with control (P < .001). FELAD <1.3 had the greatest amount of regurgitation among the repair groups (P < .001) and 1.5-1.64 the least (P < .001). FELAD <1.3 and >1.7 exhibited greater mean gradient compared with both control and 1.5 to 1.64 (P < .001). Among the repair groups, 1.5 to 1.64 had the largest orifice area, and >1.7 the smallest (P < .001).For a symmetric bicuspid aortic valve, performance after valve-sparing aortic root replacement shows a bimodal distribution across graft size. As the FELAD ratio departs from 1.5 to 1.64 in either direction, significant increases in transvalvular gradient are observed. FELAD <1.3 may also result in suboptimal improvement of baseline regurgitation.
View details for DOI 10.1016/j.xjtc.2024.03.025
View details for PubMedID 38899072
View details for PubMedCentralID PMC11184666