TY - JOUR

T1 - The Legal Dimensions of Big Data, Artificial Intelligence and Machine Learning in Pharmaceutical Innovation (original title in accepted version)

T2 - Editorial to special issue of the European Pharmaceutical Law Review on "Big Data, AI & Machine Learning in Pharmaceutial Innovation"

AU - Minssen, Timo

AU - Seitz, Claudia

PY - 2019

Y1 - 2019

N2 - Multiple factors indicate that big data, artificial intelligence and machine learning will play a crucial role in the evolution of pharmaceutical innovation.The ongoing paradigm shift is fuelled by rapid technical advances that have greatly transformed and enhanced many facets of the pharmaceutical sector. Huge datasets are driving new drug discoveries and are making clinical trials more efficient, while sophisticated data models have enabled healthcare professionals to better predict and prevent illness using new technology ranging from precision medicine to AI assisted diagnostics as well as genetic engineering and sequencing. Big data and AI have also transformed international collaboration in the areas of drug discovery and development and in the future, the combination of pharmaceutical datasets with information technology based AI solutions will lead to new tools and improvements in pharmaceutical fields such as generative chemistry, image segmentation and analysis as well as optimization of cell and gene therapies. However, despite all the advantages of fostering pharmaceutical innovation and improving patient care, this paradigm shift has brought significant legal challenges in various areas of the law, both on a national and international level. While some of these technologies have not materialised yet and some areas are burdened with unrealistic hype and expectations, it cannot be denied that legal and regulatory challenges are already crystalizing.

AB - Multiple factors indicate that big data, artificial intelligence and machine learning will play a crucial role in the evolution of pharmaceutical innovation.The ongoing paradigm shift is fuelled by rapid technical advances that have greatly transformed and enhanced many facets of the pharmaceutical sector. Huge datasets are driving new drug discoveries and are making clinical trials more efficient, while sophisticated data models have enabled healthcare professionals to better predict and prevent illness using new technology ranging from precision medicine to AI assisted diagnostics as well as genetic engineering and sequencing. Big data and AI have also transformed international collaboration in the areas of drug discovery and development and in the future, the combination of pharmaceutical datasets with information technology based AI solutions will lead to new tools and improvements in pharmaceutical fields such as generative chemistry, image segmentation and analysis as well as optimization of cell and gene therapies. However, despite all the advantages of fostering pharmaceutical innovation and improving patient care, this paradigm shift has brought significant legal challenges in various areas of the law, both on a national and international level. While some of these technologies have not materialised yet and some areas are burdened with unrealistic hype and expectations, it cannot be denied that legal and regulatory challenges are already crystalizing.

UR - https://eplr.lexxion.eu/article/EPLR/2019/4/3

U2 - https://doi.org/10.21552/eplr/2019/4/3

DO - https://doi.org/10.21552/eplr/2019/4/3

M3 - Editorial

VL - 3

SP - 139

EP - 141

JO - European Pharmaceutical Law Review

JF - European Pharmaceutical Law Review

SN - 2511-7157

IS - 4

ER -