Daiju Ueda

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Associate Professor

Osaka University

Japan

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About

Dr. Daiju Ueda is an Associate Professor at Osaka Metropolitan University, Japan. His research focuses on the intersection of artificial intelligence and healthcare, encompassing areas such as diagnostic performance, climate change impacts, and the development of advanced imaging techniques. Recent publications include a systematic review of generative AI in healthcare and studies on AI-based models for estimating pulmonary function and classifying cardiac functions.

Recent Grants

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Generating DSA images without misregistration artifacts using deep learning

Open Date: 2020-04-01

Close Date: 2024-03-01

Articles (9)

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2025

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2024

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2024

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2024

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2023

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2023

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2023

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2023

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2021

Collaborators (6)

Daisuke Horiuchi

Osaka University

JAPAN

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Yukio Miki

JAPAN

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Takehiro Uda

Lecturer

Osaka University

JAPAN

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Hiroyuki Tatekawa

Osaka University

JAPAN

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Hirotaka Takita

Osaka University Graduate School of Medicine

JAPAN

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Yasutaka Fushimi

Kyoto Prefectural University of Medicine

JAPAN

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Daiju Ueda

Research Interests

Explore related searches

Contact this professor

About

Recent Grants

Articles (9)

A systematic review and meta-analysis of diagnostic performance comparison between generative AI and physicians

A deep learning-based model to estimate pulmonary function from chest x-rays: multi-institutional model development and validation study in Japan

Climate change and artificial intelligence in healthcare: Review and recommendations towards a sustainable future

Fairness of artificial intelligence in healthcare: review and recommendations

AI-based Virtual Synthesis of Methionine PET from Contrast-enhanced MRI: Development and External Validation Study

Diagnostic Performance of ChatGPT from Patient History and Imaging Findings on the Diagnosis Please Quizzes

Chest radiography as a biomarker of ageing: artificial intelligence-based, multi-institutional model development and validation in Japan

Artificial intelligence-based model to classify cardiac functions from chest radiographs: a multi-institutional, retrospective model development and validation study

Deep learning–based angiogram generation model for cerebral angiography without misregistration artifacts

Collaborators (6)

Daisuke Horiuchi

Yukio Miki

Takehiro Uda

Hiroyuki Tatekawa

Hirotaka Takita

Yasutaka Fushimi

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