August 1, 2025: I've been appointed as an editorial board member of Communications Medicine!

July 1, 2025: I've been appointed as a trainee editorial board of Radiology: Artificial Intelligence!

May 26, 2025: Just crossed 1,000 citations on Google Scholar!

March 1, 2025: Thrilled to announce that I joined the Department of Urology at Stanford University as a Postdoctoral Scholar!

February 1, 2025: I've been appointed as an editorial board member of European Radiology Experimental!

January 28, 2025: I received the 2024 Reviewer of the Year Award

December 20, 2024: I officially obtained my doctorate #2 🥳

November 20, 2024: I just submitted my 100th journal peer-review!

November 1, 2024: I will be joining Stanford Medicine as a postdoc!

July 31, 2024: I officially obtained my 1st doctoral degree 🥳

July 24, 2024: Editorial comment on our Radiology article by RSNA is available online [Click here]

"Intraindividual Comparison of Different Methods for Automated BPE Assessment at Breast MRI: A Call for Standardization"

By: G. Müller-Franzes, F. Khader, S. Tayebi Arasteh, et al. in Radiology

June 15, 2024: Happy to announce that I received the June 2024 issue of the Paper of the Month award from the RWTH Aachen University for the paper on streamlining clinical studies with LLMs.

May 16, 2024: Celebrating 40 journal peer-reviews 🥳 See Web of Science for more!

May 10, 2024: I had the opportunity to present 2 abstracts at the 105. Deutschen Röntgen­kongress (105th German X-ray congress) in Wiesbaden, Germany:

• Tapping the Pool of Non-Medical Images for Enhanced AI-Based Chest Radiography Analysis
  
  
• The Future is Collaborative: A Systematic Analysis of Federated Learning and Framework Parameters in the AI-Based Interpretation of Chest Radiographs

March 14, 2024: My journal papers 1, 2, 3, 4, and 5 on diagnostic deep learning using chest radiographs are published!

Feb 21, 2024: My paper on streamlining clinical studies with LLMs is now published by Nature Communications!

By: S. Tayebi Arasteh, et al. in Nature Communications

Feb 8, 2024: Celebrating 200 citations on Google Scholar🥳

Feb 1, 2024: I will be presenting the following 2 abstracts on May 10, 2024 at 105. Deutschen Röntgen­kongress (105th German X-ray congress) in Wiesbaden, Germany!

• "Tapping the Pool of Non-Medical Images for Enhanced AI-Based Chest Radiography Analysis"
  
  
• "The Future is Collaborative: A Systematic Analysis of Federated Learning and Framework Parameters in the AI-Based Interpretation of Chest Radiographs"

Jan 17, 2024: Editorial comment on my Radiology AI article by RSNA is available online [Click here]

"Securing Collaborative Medical AI by Using Differential Privacy: Domain Transfer for Classification of Chest Radiographs"

By: S. Tayebi Arasteh, et al. in Radiology: Artificial Intelligence

Dec 16, 2023: Final version of the open-source article is now published! [Click here]

"Encrypted federated learning for secure decentralized collaboration in cancer image analysis"

By: D. Truhn*, S. Tayebi Arasteh*, et al. in Medical Image Analysis

Jul 16, 2023: Our publications 1 and 2 on applying DDPMs on medical images are available in Scientific Reports!

Kudos to my colleagues Firas Khader and Gustav Müller-Franzes!

March 21, 2023: Editorial comment on our Radiology article by RSNA is available online [Click here]

"Using Machine Learning to Reduce the Need for Contrast Agents in Breast MRI through Synthetic Images"

By: G. Müller-Franzes, L. Huck, S. Tayebi Arasteh, et al. in Radiology

Feb 1, 2023: I will be presenting the following 2 abstracts on May 13, 2023 in RÖKO 2023!

• "Automatic Evaluation of Chest Radiographs – The Data Source Matters, But How Much Exactly?" [Click here]
  
• "Training of AI Models Beyond the Local Dataset Using Federated Learning with 695,000 Non-Identically-Labeled Chest Radiographs" [Click here]

Feb 1, 2023: Celebrating 20 citations on Google Scholar🥳

Nov 29, 2022: RSNA Annual Meeting 2022 in Chicago was amazing!

I had the opportunity to present my abstract on:
"Secure Federated Learning for Decentralized Collaboration in Development of AI Models"