Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
The global evolution and impact of systems biology and artificial intelligence in stem cell research and therapeutics development: a scoping review
7
Zitationen
23
Autoren
2024
Jahr
Abstract
Advanced bioinformatics analysis, such as systems biology (SysBio) and artificial intelligence (AI) approaches, including machine learning (ML) and deep learning (DL), is increasingly present in stem cell (SC) research. An approximate timeline on these developments and their global impact is still lacking. We conducted a scoping review on the contribution of SysBio and AI analysis to SC research and therapy development based on literature published in PubMed between 2000 and 2024. We identified an 8 to 10-fold increase in research output related to all 3 search terms between 2000 and 2021, with a 10-fold increase in AI-related production since 2010. Use of SysBio and AI still predominates in preclinical basic research with increasing use in clinically oriented translational medicine since 2010. SysBio- and AI-related research was found all over the globe, with SysBio output led by the (US, n = 1487), (UK, n = 1094), Germany (n = 355), The Netherlands (n = 339), Russia (n = 215), and France (n = 149), while for AI-related research the US (n = 853) and UK (n = 258) take a strong lead, followed by Switzerland (n = 69), The Netherlands (n = 37), and Germany (n = 19). The US and UK are most active in SCs publications related to AI/ML and AI/DL. The prominent use of SysBio in ESC research was recently overtaken by prominent use of AI in iPSC and MSC research. This study reveals the global evolution and growing intersection among AI, SysBio, and SC research over the past 2 decades, with substantial growth in all 3 fields and exponential increases in AI-related research in the past decade.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.214 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.071 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.429 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.776 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.418 Zit.
Autoren
- Thayna Silva-Sousa
- Júlia Nakanishi Usuda
- Nada Al-Arawe
- Francisca Frias
- Irene Hinterseher
- Rusan Catar
- Christian Luecht
- Katarina Riesner
- Alexander Hackel
- Lena F. Schimke
- Haroldo Dutra Dias
- Igor Salerno Filgueiras
- Helder I. Nakaya
- Niels Olsen Saraiva Câmara
- Stefan Fischer
- Gabriela Riemekasten
- Olle Ringdén
- Olaf Penack
- Tobias Winkler
- Georg N. Duda
- Dennyson Fonseca
- Otávio Cabral-Marques
- Guido Moll
Institutionen
- Medizinische Hochschule Brandenburg Theodor Fontane(DE)
- Humboldt-Universität zu Berlin(DE)
- Brandenburg University of Technology Cottbus-Senftenberg(DE)
- University of Potsdam(DE)
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin(DE)
- Universidade de São Paulo(BR)
- Charité - Universitätsmedizin Berlin(DE)
- Freie Universität Berlin(DE)
- University Hospital Schleswig-Holstein(DE)
- University of Lübeck(DE)
- Institute of Immunology(HR)
- Institute of Biomedical Science(GB)
- Institute of Bioinformatics(IN)
- Karolinska Institutet(SE)
- D’Or Institute for Research and Education(BR)