POLAND Law and Practice Contributed by: Barbara Kiełtyka, Jakub Gładkowski and Małgorzata Kiełtyka, Kieltyka Gladkowski KG Legal
The Polish Act on Artificial Intelligence Systems should be adopted in 2025. 9.2 Regulatory Sandboxes and Innovation Programmes Poland has not yet introduced regulatory sandboxes specifically for the healthcare sector, but they are planned under the Act on Artificial Intelligence Sys - tems. The Committee for the Development and Secu - rity of Artificial Intelligence is to take steps to establish them. They will allow manufacturers who qualify in a competition announced by the Chairman of the Com - mittee for the Development and Security of Artificial Intelligence to test AI systems in a controlled environ - ment. Poland’s AI development policy until 2030 envisages the creation of three AI sandboxes and a cross-border regulatory sandbox. To date, limited measures have been implemented to test innovative solutions, such as the Urban Tech Hub launched by the Polish Devel - opment Fund. 9.3 International Harmonisation In 2024, the World Health Organization (WHO) pre - sented new guidelines for national governments regarding healthcare. In Poland, these recommenda - tions have been primarily implemented through the Personal Data Protection Act and government bills aimed at implementing the provisions of the AI Act. These will likely include the establishment of the Com - mission for the Development and Security of Artifi - cial Intelligence as a supervisory authority, and the President of the Personal Data Protection Office will exercise oversight over high-risk AI systems. These guidelines also include provisions on the use of LLM. The GMLP published by IMDRF establishes ten guid - ing principles to ensure the safety and effectiveness of AI-based medical devices. Additionally, SaMD WG/N81 regulates issues related to medical device software. ISO 42001 also provides specific tools and methods to aid in, among other things, the implementation of the AI Act. It specifies requirements for data quality, ethical standards, and provides for the planning of
development and educational activities for personnel. These provisions are also implemented in Poland by Article 12 of the Code of Medical Ethics. 9.4 Emerging Legal Challenges Already in the vertical LLMs, the creators, within the meaning of the AI Act, focus on fact-checking in the language models training when training networks of AI models. This phenomenon is especially true for hori - zontal models, ie, multi-agent models of AI systems, which hold promise for breakthroughs in medicine and scientific breakthroughs (superintelligence). In prac - tice, the problem is that such AI networks are trained on so-called natural language, and the language mod - els that search and precess online resources treat these resources as a representation of reality. Another challenge is the protection of personal data and privacy. The EHDS is introducing electronic health records, which can also be used for research and innovation. The risk of false data and results must also be considered. Large AI models process vast amounts of information, the reliability of which can be difficult to verify, which can lead to erroneous analyses and clinical decisions. There is a risk of manipulation, for example, training models on falsified data on drug effectiveness, which can be used for unfair competi - tion and falsifying clinical trial results. However, AI can also be used to verify facts and detect false informa - tion. To make the AI system reliable and make fewer errors, an “adaptive model” is typically introduced, based on the model’s ability to analyse data in real time and make automatic corrections.
10. Practical Considerations in Healthcare AI 10.1 Compliance Strategies
AI system developers and users are required to imple - ment risk mitigation mechanisms, GDPR compliance, and often a post-market monitoring plan. Systems with medical applications must be appropriately risk- classified, which impacts regulatory obligations, and developers and users must continuously monitor implemented systems.
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