Sophia Wakefield | Medicine and Health Sciences | Women Researcher Award

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Dr. Sophia Wakefield | Medicine and Health Sciences | Women Researcher Award

Doctor University of Leeds United Kingdom

📚 Dr. Sophia Wakefield, born in 1999, is a passionate and accomplished medical professional specializing in Trauma and Orthopaedics. Currently employed as a Hospital Doctor with Health Education Yorkshire and Humber, she is committed to advancing clinical practice, research, and education in her field.

Profile

Scopus

Education

🎓 Postgraduate Education

  • Master of Research in Medicine (Distinction) – September 2023 to September 2024
  • Postgraduate Certificate in Medical Education (Distinction) – September 2023 to June 2024

🎓 Undergraduate Education

  • MBBCh – September 2017 to July 2023
  • BSc in Clinical Sports Science (First Class Honours) – September 2021 to June 2022

Experience

💼 Dr. Wakefield is currently a Hospital Doctor at Health Education Yorkshire and Humber. Her extensive clinical training includes experiences in Trauma and Orthopaedics and medical education. She actively participates in research projects and leadership roles in national organizations.

Research Interests

🔬 Dr. Wakefield’s research focuses on:

  • Trauma and Orthopaedics
  • Rehabilitation techniques
  • Medical education innovation
  • Fracture management and surgical techniques

Awards and Achievements

🏆 Prizes and Awards

  • Second place, Oral Presentation at RCSEd Foundation Trainees Surgical Society National Conference (2024)
  • Postgraduate £6k funding grant awarded by Leeds Hospitals Charity (2024)

👩‍🔬 National Leadership Roles

  • Social Media Lead for British Orthopaedic Medical Students Association (2022–2023)
  • Student Ambassador for Students for Research Network (2022–current)

Publications Top Notes: 

📖 Peer-Reviewed Articles

Wakefield, S.M., Kanakaris, N.K., & Giannoudis, P.V. (2025). Sexual and Urinary Dysfunction Following Isolated Acetabulum Fractures: A Systematic Review of the Literature. J Clin Med, 1(230). Read Here

Cited by: 12 articles

Watson, P., Hanna, D., Wakefield, S. M., et al. (2024). Undergraduate rheumatology teaching in the UK: A survey of current practice by teachers and students. Rheumatology Advances in Practice, 8(4). Read Here

Cited by: 8 articles

Wakefield, S. M., Rodham, P. L., & Giannoudis, P. V. (2024). The management of intertrochanteric hip fractures: An update. Orthopaedics and Trauma, 38(2), 70–77. Read Here

Cited by: 15 articles

Wakefield, S. M., et al. (2024). Distraction osteogenesis versus induced membrane technique for infected tibial non-unions. European Journal of Trauma and Emergency Surgery, 50(3), 705–721. Read Here

Cited by: 20 articles

Yashbir Singh | Medicine and Health Sciences | Best Researcher Award

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Dr. Yashbir Singh | Medicine and Health Sciences | Best Researcher Award

Assistant Prof Mayo clinic, Rochester United States

Yashbir Singh is an Assistant Professor in Radiology with extensive experience in biomedical engineering and computational deep learning methods for medical imaging analysis. His research focuses on AI-driven approaches for early cancer detection, particularly in liver cancer, and has made substantial contributions to the integration of topological data analysis with deep learning. With a background in pharmaceutics, bioinformatics, and biomedical engineering, Yashbir brings a unique interdisciplinary perspective to medical imaging. His work is instrumental in advancing AI methodologies for clinical applications and patient-specific risk assessments in liver diseases. 🌐💡

Profile

Google Scholar

Orcid

Education

Yashbir Singh completed his Bachelor’s in Pharmaceutics and Drug Design from M.J.P. Rohilkhand University, Bareilly, India, followed by a Master’s in Bioinformatics from the Indian Institute of Information Technology, Allahabad. He then earned his PhD in Biomedical Engineering from Chung Yuan Christian University, Taiwan. After completing his postdoctoral work in AI Radiology at Mayo Clinic, he continued his research at the same institution as a Postdoctoral Fellow in AI Radiology. 🎓🔬

Experience

Yashbir Singh has held various prestigious positions, including visiting faculty at Harvard Medical School and a medical scientist at WVU Medicine, West Virginia. He has also been a junior research fellow at IIT Mandi, India, and a teaching assistant at IIIT Allahabad, India, and Chung Yuan Christian University, Taiwan. His diverse experiences across academia and clinical environments have significantly shaped his research in medical imaging and AI. 🌍👨‍⚕️

Research Interest

Dr. Singh’s research interests lie in the intersection of artificial intelligence, medical imaging, and oncology. He has pioneered the use of algebraic topology-based machine learning for predicting clinical outcomes, specifically in liver diseases like primary sclerosing cholangitis. His current work focuses on developing deep learning frameworks for early cancer detection, integrating machine learning with topological data analysis, and improving patient-specific diagnostic tools. 🔍💻🧬

Award

Yashbir Singh has received several prestigious awards, including the 2024 Research Award from RSNA and Best Paper Presenter at IEEE CCWC 2022. His work has been recognized for its innovation in AI-driven medical imaging and clinical applications. In addition, he has received Best Reviewer Awards from ICSPIS and the 2020 Medical Imaging Best Paper Award from SPIE. These accolades highlight his leadership in the field of AI-based medical research. 🏆📜

Publication Top Notes:

Yashbir Singh has contributed to over 20 high-impact publications. Some notable works include:

Decoding ChatGPT: a taxonomy of existing research, current challenges, and possible future directions

Mitigating bias in radiology machine learning: 1. Data handling

Mitigating bias in radiology machine learning: 2. Model development

The future of gpt: A taxonomy of existing chatgpt research, current challenges, and possible future directions

A low-cost texture-based pipeline for predicting myocardial tissue remodeling and fibrosis using cardiac ultrasound

Identification of Novel Abiotic Stress Proteins in Triticum aestivum Through Functional Annotation of Hypothetical Proteins

Algebraic topology-based machine learning using MRI predicts outcomes in primary sclerosing cholangitis

His work is highly cited, demonstrating a significant impact on the field of AI and medical imaging, especially in the context of liver disease. 📖📈