Best Researcher Award
Vasiliki Bikia — Stanford University, United States
Vasiliki Bikia is a researcher, whose research activity is on spanning artificial intelligence, biomedical data, medical imaging, vascular science, and computational health research. Her ORCID record identifies Stanford University as an employment affiliation and lists multiple peer-reviewed research works. [1]
Abstract
Vasiliki Bikia’s documented research profile demonstrates interdisciplinary activity connecting artificial intelligence with biomedical and clinical research. Her ORCID record lists studies involving medical image analysis, digital health data, dermatology, vascular ageing, and cardiovascular modelling. The documented publication record provides a basis for academic recognition within an interdisciplinary research award framework. [1]
Keywords
Artificial Intelligence; Medical Imaging; Digital Health; Biomedical Data Science; Vascular Ageing; Computational Modelling; Clinical Research; Machine Learning.
Introduction
Bikia’s listed works illustrate research across computational and biomedical domains, with artificial intelligence appearing prominently in recent publications. Her 2026 publication addresses FHIR-based interoperable digital-health data workflows, demonstrating an additional connection between computational methods and healthcare systems. [3]
Research Profile
The ORCID record identifies Stanford University as Bikia’s employment affiliation and records a verified Stanford email domain. The publication portfolio includes artificial intelligence in dermatology, medical imaging, vascular ageing, cardiovascular physiology, and biomedical informatics, reflecting a broad interdisciplinary profile. [1]
Research Contributions
Documented contributions include work on arterial pulse-wave modelling, arterial compliance, vascular ageing, cardiac output estimation, and computational haemodynamics. More recent research extends toward artificial intelligence, medical imaging, digital health interoperability, and dermatological applications. [5]
Publications
The ORCID record lists peer-reviewed journal publications from 2020 through 2026, with DOI identifiers are for the documented works. Examples include articles in Medical Image Analysis, npj Digital Medicine, Annual Review of Biomedical Data Science, Scientific Reports, and the European Heart Journal – Digital Health. [1]
Research Impact
The bibliometric information reports 37 documents, 484 citations, and an h-index of 13. These indicators, together with the documented publication record, provide quantitative context for evaluating scholarly activity.The ORCID record also documents peer-review activity for EBioMedicine and European Radiology. [1]
Award Suitability
The research profile is suitable for consideration for the Best Researcher Award because it combines a documented interdisciplinary publication record with research activity relevant to artificial intelligence and healthcare. The evidence includes recent AI-related research and established biomedical modelling studies. Final award decisions should be based on the organizer’s eligibility criteria, submitted evidence, and independent evaluation procedures.
Conclusion
Vasiliki Bikia’s available research record demonstrates sustained scholarly activity across artificial intelligence, digital health, medical imaging, vascular research, and computational biomedicine. The profile can therefore be presented for consideration within the Scientific World Research Awards framework, subject to formal verification and award criteria.
External Links
References
- ORCID. (2026). Vasiliki Bikia ORCID record and works. ORCID.
https://orcid.org/0000-0002-4660-1560
- Bikia, V., et al. (2026). Med-REDUCE: Representation transfer and efficiency under resolution constraints. Medical Image Analysis.
https://doi.org/10.1016/j.media.2026.104305
- Bikia, V., et al. (2026). Spezi Data Pipeline: Streamlining FHIR-based interoperable digital health data workflows. npj Digital Medicine.
https://doi.org/10.1038/s41746-026-02666-7
- Alastruey, J., Charlton, P. H., Bikia, V., et al. (2023). Arterial pulse wave modeling and analysis for vascular-age studies: a review from VascAgeNet. American Journal of Physiology-Heart and Circulatory Physiology.
https://doi.org/10.1152/ajpheart.00705.2022
- Utti, V., Bikia, V., Agarwal, A. A., & Daneshjou, R. (2025). Integrating Artificial Intelligence in Dermatological Cancer Screening and Diagnosis: Efficacy, Challenges, and Future Directions. Annual Review of Biomedical Data Science.
https://doi.org/10.1146/annurev-biodatasci-103123-094521