AI Approaches in Processing and Using Data in Personalized Medicine
It addresses the need for better data analysis in healthcare to support doctors and patients, but appears to be an incremental review of existing methods without presenting new results.
The paper discusses the application of various artificial intelligence techniques, such as machine learning and federated learning, to process large-scale patient data for personalized medicine, aiming to improve treatment recommendations and health outcomes.
In modern dynamic constantly developing society, more and more people suffer from chronic and serious diseases and doctors and patients need special and sophisticated medical and health support. Accordingly, prominent health stakeholders have recognized the importance of development of such services to make patients life easier. Such support requires the collection of huge amount of patients complex data like clinical, environmental, nutritional, daily activities, variety of data from smart wearable devices, data from clothing equipped with sensors etc. Holistic patients data must be properly aggregated, processed, analyzed, and presented to the doctors and caregivers to recommend adequate treatment and actions to improve patients health related parameters and general wellbeing. Advanced artificial intelligence techniques offer the opportunity to analyze such big data, consume them, and derive new knowledge to support personalized medical decisions. New approaches like those based on advanced machine learning, federated learning, transfer learning, explainable artificial intelligence open new paths for more quality use of health and medical data in future. In this paper, we will present some crucial aspects and characteristic examples in the area of application of a range of artificial intelligence approaches in personalized medical decisions.