POLAND Law and Practice Contributed by: Barbara Kiełtyka, Jakub Gładkowski and Małgorzata Kiełtyka, Kieltyka Gladkowski KG Legal
1. Use of Healthcare AI 1.1 Types and Applications of Healthcare AI The dawn of the AI era means that, finally, every operation and decision of systems does not have to be defined based on specific instructions, and their adaptation no longer requires code modifica - tions, unlike those based on sorting algorithms (eg, quicksort, mergesort) or search algorithms (eg, binary search). The use of AI-based systems in the Polish healthcare system will become increasingly common. Examples of AI systems in healthcare include: • using AI systems for more efficient production of antibody-based drugs; • providing hospitals with the software necessary to implement AI solutions; • software based on AI algorithms that aim to improve clinical outcomes, such as the develop - ment of algorithms for triaging patients in emer - gency rooms; • use of AI in the development of radiology reports and the Aesculap language model; • creating voicebots using AI to assist in registering patients for hospitals and clinics and reducing the number of missed appointments; • models designed to accelerate the process of drug testing, including even prototypes, and the assessment of clinical value and the risk of adverse events; • AI solutions designed to improve diagnostics by training algorithms to detect and assess measur - able changes and analysing them over time; and • robotic surgery – surgical AI systems equipped with advanced processors and algorithms that allow them to process large amounts of data in real time. 1.2 Key Benefits and Challenges The AI revolution in medicine, in practice, means the acceleration of medical procedures in physician diag - nosis. AI primarily helps physicians analyse data by combining data from a specific medical procedure for a specific patient with a modelled approach to the data. AI can identify diseases in their early stages
and with greater precision and speed than humans can, for example, in diagnostic imaging. AI is capable of personalising therapy through data analysis. This means that AI can develop individual treatment plans and eliminate unwanted side effects. AI, therefore, transcends the barrier of diagnostic capabilities, as it not only can detect diseases but goes a step further: predicting a patient’s future con - dition by identifying patterns suggesting health prob - lems, for example, using data from wearable devices such as watches or fitness trackers, which is mandat - ed by Article 17 of Regulation (EU) 2024/1689 laying down harmonised rules on artificial intelligence (the “AI Act”). AI can also support clinical trials by identifying suit - able candidates for participation in final-phase clini - cal evaluation programmes for new drugs or medical devices and also shortens drug production time. Challenges of AI One challenge of AI in medicine is the risk of AI drift, also known as model drift, where subsequent input data differs from training data. This can lead to, for example, incorrect diagnoses when input data (eg, X-rays) differ from those known to the model, or when the model was trained primarily on older individuals, but over time began to accept and diagnose more young individuals (Article 67 of the AI Act). Another significant risk and barrier is AI hallucina - tion, which involves AI creating information that is not based on actual data or a known source (substantively false). The postulate that the final decision should be made by a human is therefore important (Article 27 of the AI Act). Also among the challenges facing AI is protecting patient data privacy and implementing appropriate control and security mechanisms. AI requires large sets of medical data, such as images (eg, X-rays, CT scans), laboratory test results and genomic records. However, these are particularly sensitive data. Pro - cessing them carries risks such as patient re-identifi - cation after pseudonymisation, unauthorised access or data leakage (Articles 75 and 76 of the AI Act).
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