The capabilities of AI systems are increasingly impressive, largely based on machine learning. This technique allows for the “training” of algorithms by providing vast amounts of data, which in turn leads to the automation of the algorithm and a radical increase in its “cognitive” capabilities, particularly through generalization, drawing conclusions from the obtained data, and predictive language models that allow for highly probable prediction of the next word that fits a given statement or sentence, taking into account its context. Most AI systems are based, to be precise, on a specific variety or technique of machine learning, referred to as an artificial neural network, or deep neural network, a metaphor that approximates the logic of AI systems to the functions of the human brain.
Covering 13 practice areas and 17 jurisdictions – from Albania to Ukraine – the awards were grounded in Legal 500’s independent research, ensuring credibility and impartial recognition across the region.
We would like to share the information that the leader of the Legal 500 CEE Awards 2025 (WARSAW, Thursday 16 October 2025 | The The Westin Warsaw) in the pharma and biotech law (LIFESCIENCE AND HEALTHCARE) category was selected Małgorzata Kiełtyka, partner of KG LEGAL Kiełtyka Gładkowski – Professional Partnership. Attorney Law Firm.
Currently, useful data includes not only specific information organized into rows, columns, or databases, but also data that is not organized in any specifically defined way. This constitutes the majority of data we encounter, including images and text documents such as tweets and blog posts. Thousands of individuals and organizations generate it daily, with little regard for how it can be used. It is precisely thanks to unstructured data that such rapid AI development is possible through machine learning, which involves training algorithms to find patterns and correlations in large data sets.
On September 11th, we had the pleasure of participating in an event organized by LifeScience Cluster on the application of artificial intelligence in healthcare. The webinar began with an explanation of the very concept of AI, which is controversial and riddled with myths. A key element of AI is machine learning. It involves creating predictions based on previous data. Within machine learning, deep learning, a model based on neural networks, was developed. The difference between traditional machine learning and deep learning lies primarily in the number of neural network layers. These enable the recognition of increasingly complex relationships. The concept of data science, which lies between artificial intelligence and data analysis, was also mentioned. It is the art of combining data with practice. This allows for the automation of many processes and better decision-making.
Under EU Law, namely Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation) (hereinafter “GDPR”) and the pending entry into application of Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence and amending Regulations (EC) No 300/2008, (EU) No 167/2013, (EU) No 168/2013, (EU) 2018/858, (EU) 2018/1139 and (EU) 2019/2144 and Directive 2014/90/EU, (EU) 2016/797 and (EU) 2020/1828 (Artificial Intelligence Act) (hereinafter “AIA”), the use of sensitive data (including medical data) for AI training would only be possible after obtaining consent, in cases specified by law, or when using anonymized data. AIA is not a lex specialis vis-à-vis the GDPR, so when using personally identifiable data, using data for AI model training requires meeting the requirements of both acts.
