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@article{2317118,
author = {Plass, Markus and Wittner, Rudolf and Holub, Petr and Frexia, Francesca and Mascia, Cecilia and Gallo, Matej and Müller, Heimo and Geiger, Jörg},
article_location = {NETHERLANDS},
article_number = {DEC},
doi = {http://dx.doi.org/10.1016/j.nbt.2023.09.006},
keywords = {Artificial intelligence; Provenance; Biological material; Traceability},
language = {eng},
issn = {1871-6784},
journal = {NEW BIOTECHNOLOGY},
title = {Provenance of specimen and data – A prerequisite for AI development in computational pathology},
url = {https://www.sciencedirect.com/science/article/pii/S1871678423000493},
volume = {78},
year = {2023}
}
TY - JOUR
ID - 2317118
AU - Plass, Markus - Wittner, Rudolf - Holub, Petr - Frexia, Francesca - Mascia, Cecilia - Gallo, Matej - Müller, Heimo - Geiger, Jörg
PY - 2023
TI - Provenance of specimen and data – A prerequisite for AI development in computational pathology
JF - NEW BIOTECHNOLOGY
VL - 78
IS - DEC
SP - 22-28
EP - 22-28
PB - ELSEVIER
SN - 18716784
KW - Artificial intelligence
KW - Provenance
KW - Biological material
KW - Traceability
UR - https://www.sciencedirect.com/science/article/pii/S1871678423000493
N2 - AI development in biotechnology relies on high-quality data to train and validate algorithms. The FAIR principles (Findable, Accessible, Interoperable, and Reusable) and regulatory frameworks such as the In Vitro Diagnostic Regulation (IVDR) and the Medical Device Regulation (MDR) specify requirements on specimen and data provenance to ensure the quality and traceability of data used in AI development. In this paper, a framework is presented for recording and publishing provenance information to meet these requirements. The framework is based on the use of standardized models and protocols, such as the W3C PROV model and the ISO 23494 series, to capture and record provenance information at various stages of the data generation and analysis process. The framework and use case illustrate the role of provenance information in supporting the development of high-quality AI algorithms in biotechnology. Finally, the principles of the framework are illustrated in a simple computational pathology use case, showing how specimen and data provenance can be used in the development and documentation of an AI algorithm. The use case demonstrates the importance of managing and integrating distributed provenance information and highlights the complex task of considering factors such as semantic interoperability, confidentiality, and the verification of authenticity and integrity.
ER -
PLASS, Markus, Rudolf WITTNER, Petr HOLUB, Francesca FREXIA, Cecilia MASCIA, Matej GALLO, Heimo MÜLLER and Jörg GEIGER. Provenance of specimen and data – A prerequisite for AI development in computational pathology. \textit{NEW BIOTECHNOLOGY}. NETHERLANDS: ELSEVIER, 2023, vol.~78, DEC, p.~22-28. ISSN~1871-6784. Available from: https://dx.doi.org/10.1016/j.nbt.2023.09.006.
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