Turku University of Applied Sciences
Relevante Arbeiten
Meistzitierte Publikationen im Bereich Gesundheit & MedTech
A review on generative AI models for synthetic medical text, time series, and longitudinal data
Mohammad Loni, Fatemeh Poursalim, Mehdi Asadi et al.
2025 · 19 Zit.
Machine learning in a time of COVID-19 - Can machine learning support Community Health Workers (CHWs) in low and middle income countries (LMICs) in the new normal?
Mats Stage Baxter, Alan White, Mari Lahti et al.
2021 · 12 Zit.
Machine Learning and Deep Learning Models for Automated Protocoling of Emergency Brain MRI Using Text from Clinical Referrals
Heidi Huhtanen, Mikko Nyman, Antti Karlsson et al.
2025 · 9 Zit.
Does <scp>ChatGPT</scp> Ignore Article Retractions and Other Reliability Concerns?
Mike Thelwall, Marianna Lehtisaari, Irini Katsirea et al.
2025 · 5 Zit.
Towards fair decentralized benchmarking of healthcare AI algorithms with the Federated Tumor Segmentation (FeTS) challenge
Maximilian Zenk, Ujjwal Baid, Sarthak Pati et al.
2025 · 3 Zit.
Finnish perspective on using synthetic health data to protect privacy: the PRIVASA project
Tinja Pitkämäki, Tapio Pahikkala, Ileana Montoya Perez et al.
2024 · 1 Zit.
Advancing AI initiatives in nursing academics: Case studies and insights from thought leaders
Paulina Sockolow, Martin Michalowski, Laura‐Maria Peltonen et al.
2025 · 1 Zit.
A rapid review on the application of common data models in healthcare: Recommendations for data governance and federated learning in artificial intelligence development
Hanna von Gerich, Taridzo Chomutare, Ville Kytö et al.
2025 · 1 Zit.
A roadmap for federated learning projects using health data to guide sustainable artificial intelligence development in the European Union
Janne Kommusaar, Silja Elunurm, Taridzo Chomutare et al.
2025 · 0 Zit.
Federated Learning and 5G/6G‐Based Internet of Medical Things (IoMT): Applications, Key Enabling Technologies, Open Issues and Future Research Directions
Abdul Ahad, Kazi Istiaque Ahmed, Farhan Ullah et al.
2026 · 0 Zit.
Towards Inclusive AI System Development for Disease Risk Prediction: Collecting, Prioritising and Incorporating User Stories from Heterogeneous Stakeholders
Nicholas Shopland, Andrew Burton, David J. Brown et al.
2026 · 0 Zit.