Human health is one of the main areas of investment in artificial intelligence. The use of AI in the field of healthcare pursues the following goals:
- disease prediction;
- identification of groups of patients with high risk of diseases;
- organization of preventive measures.
The development of AI and Big Data methods opens up new health-saving opportunities, allowing to analyze huge amounts of information, opening a new era in the science and practice of healthcare management.
AILab concentrates its work on the research and development of breakthrough solutions for the analysis of medical images, texts, videos – a collection of heterogeneous clinical information, as well as sequences of similar types of data using artificial intelligence.
The COVID-19 pandemic has shown that more advanced methods are needed for transparent interaction between a doctor and AI and overall improvement of the effectiveness of medical decision-making based on AI. An important focus, according to the developers and researchers of AILab, it to do research on methods for working with a limited amount of input data.
In the course of research, AI training methods can be developed for analyzing heterogeneous data, data with weak markup, self-learning methods, with a special focus on the generalizability of these methods to different data sources.
Important result of AILab’s activity is the creation of effective metrics and practices for verification, validation and monitoring of AI systems in medical organizations.
AI technologies created by AILab should be implemented, among other things, in the form of software tools for creating medical decision support systems that ensure their reliability (general reliability of conclusions obtained using AI and tested on provided data), security (no harm to the patient, protection against hacking, unauthorized access, negative external influence, etc.), privacy (including anonymization of this data and differentiation of access to it).
Examples of the mentioned above technologies are technologies for processing multimodal medical data from different sources, building models based on incomplete, unbalanced, inaccurately annotated data, etc.
The developed systems can be focused on a wide range of medical care tasks, including primary diagnosis and routing of patients, conducting control diagnostic studies, choosing treatment tactics, assisting in planning and conducting therapeutic and surgical interventions.
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