AUSTRIA Law and Practice Contributed by: Harald Strahberger and Florian Sesztak, Kinstellar
1. Use of Healthcare AI 1.1 Types and Applications of Healthcare AI In Austria, the use of AI in the healthcare setting is already quite advanced, with the technology being employed in a variety of ways to improve patient care and safety. AI is used in the following areas of health - care. Diagnostic Tools AI is widely used in Austria for medical diagnostics, particularly in radiology, pathology, and dermatology. These systems support physicians in identifying dis - eases such as cancer, lung infections, and skin con - ditions through automated image analysis. Hospitals across the country have also integrated these tools into routine workflows, making diagnostics one of the most mature and widely adopted areas of AI in Aus - trian healthcare. Treatment Planning and Clinical Decision Support AI applications are increasingly used to support clinical decision-making and personalised treatment planning. These tools analyse patient data, including genetic and clinical indicators, to identify optimal ther - apies and predict outcomes. AI is particularly valuable in oncology and chronic disease management, where it helps in tailoring interventions to individual patients. While still in development in some areas, these sys - tems are expanding in research hospitals and are emphasised in Austria’s national eHealth strategy. Drug Discovery and Development In Austria’s strong biotech and pharmaceutical sec - tors, AI is being applied to accelerate drug discov - ery and clinical research. Tools are used to identify potential drug targets, simulate molecule behaviour, and design more efficient clinical trials. Universities and life science companies frequently integrate AI into preclinical research and development, supported by national and EU-level funding initiatives. Operational and Administrative Applications AI is also being deployed in hospital administration to improve efficiency. Applications include automated patient scheduling, triage systems, documentation, billing, and fraud detection. Furthermore, chatbots are used for patient communication and basic inquir -
ies. These tools help reduce administrative workload and streamline hospital operations. While adoption is not yet universal, many public hospitals are piloting or implementing these technologies. Remote Monitoring and Telemedicine AI-enabled remote monitoring is gaining momentum, particularly in the management of chronic diseases and elderly care. Wearables and smart devices track patients’ vital signs and behaviour in real time, trig - gering alerts when needed. Telemedicine services, enhanced by AI triage tools, allow for virtual consul - tations and follow-up care. These technologies gained prominence during the COVID-19 pandemic and are now a central focus of Austria’s long-term digital health strategy. 1.2 Key Benefits and Challenges Several key benefits drive the use of AI in the Austrian healthcare sector. One of the most notable impacts is the improvement of patient outcomes by enabling early diagnosis and personalised treatment, particu - larly in fields such as oncology, radiology, and chronic disease management. It is also assumed that the use of AI can lead to better results in patient studies, since it can analyse vast amounts of data quickly and accu - rately. At the same time, AI increases system efficien - cy by automating administrative tasks (eg, scheduling and documentation, billing, etc) and optimising clinical workflows, resulting in a reduced workload for health - care professionals and improved resource utilisation. AI also enhances clinical decision-making by integrat - ing vast amounts of medical data to support therapy selection and risk prediction. In the pharmaceutical sector, AI accelerates drug development by expedit - ing processes such as target identification, molecule screening and clinical trial design. Finally, AI is driv - ing the expansion of scalable digital health solutions, eg, telemedicine, remote monitoring and patient self- management tools. Despite the growing potential of AI and digital tech - nologies in healthcare, several key challenges and concerns remain. A significant challenge is the ongo - ing lack of data integration. Despite the availability of existing systems, interoperability between different healthcare sectors and digital platforms remains lim - ited, hindering the seamless exchange of information.
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