Healthcare AI 2025

POLAND Law and Practice Contributed by: Barbara Kiełtyka, Jakub Gładkowski and Małgorzata Kiełtyka, Kieltyka Gladkowski KG Legal

a medical procedure and whether the doctor, as the organiser and main person responsible for a medical procedure using AI, is able to demonstrate that they acted in accordance with the state of the art. 5. Ethical and Governance Considerations for Healthcare AI 5.1 Ethical Frameworks Ethics within the legal framework for the use of AI will be comprehensively addressed this year for the first time. The regulatory process for the General Purpose AI Code of Conduct is in its final stages of preparation. This will be a legally binding document applicable in EU jurisdictions, issued by the Artificial Intelligence Council and the Office for Artificial Intelligence. Ethical issues are explicitly addressed in the latest Polish Code of Medical Ethics in the context of patient consent (Article 12). As for other healthcare provid - ers, certain ethical obligations can be inferred from general standards for improving qualifications to the latest technical knowledge. An example is the Code of Ethics for Laboratory Diagnosticians, who, accord - ing to Article 10, should strive to obtain reliable test results and interpret them in accordance with current scientific knowledge and technical standards. The ethical standards that must be observed in accordance with the latest Polish Code of Medical Ethics include informing the patient about the use of AI algorithms, obtaining informed consent from the patient to the use of AI, using only such AI algorithms that are acceptable for medical use and, above all, always having the final decision made by a human. 5.2 Transparency and Explainability Transparency and explainability are required for high- risk systems such as healthcare. Transparency is par - ticularly important for combating the so-called ‘black box’ problem. To minimise risk, requirements must be met before the system is commercially deployed. The information given should include the character - istics, capabilities and limitations of the AI system’s effectiveness. However, the concept of transparency

in AI research is fragmented and often limited to the transparency of the algorithm itself. It has been proposed by many who follow the industry closely that AI transparency operates at three levels: algorithmic, interactional and social. Patients must be informed about the use of AI in their case, if its use concerns their treatment or diagnosis, and about the potential consequences. Mere efficien - cies gained through AI (eg, directing patients to their appointed doctors in waiting rooms or using AI to bet - ter navigate supplies in pharmacies) will not require such transparency. Article 13 (2)(f) of the GDPR also mandates the right to information about data processing, and the method and purpose of any profiling, as specified in Article 22. The GDPR primarily focuses on requirements for patient information and explanations regarding deci - sions made by AI. A patient may not be subject to a decision made solely by AI unless they expressly consent thereto. 5.3 Bias and Fairness There are four main forms of algorithmic bias: • bias concerning minorities (caused by too little or, on the contrary, too much data about a given group); • bias caused by lack of data (especially when the information is included in matrices); • technical bias (a common example is that mela - noma is easier to detect in white skin than in black skin); and • label bias (poor definition of labels at the AI internal level). The preamble to the AI Act mentions in Recital 75 that what should characterise high-risk AI systems (including those used in healthcare) is their techni - cal robustness, which should also include providing “technical solutions to prevent or limit harmful or other undesirable behaviour”. An example is the existence of tools allowing the AI to be interrupted (the system enters a “fail-safe” state) in the event of errors or when predefined boundaries are exceeded.

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