Digital Consultant for Doctors: An Algorithm to Guide Therapy in Complex Cases
Russian researchers have completed the development of a unique artificial intelligence system for medicine. The hybrid algorithm, capable of analyzing data and reasoning much like an experienced physician, is designed to support doctors in selecting drug therapy in complex clinical situations.
Core of the Design
Specialists at the Institute of Automation and Control Processes of the Far Eastern Branch of the Russian Academy of Sciences have completed work on a new intelligent medical system. The software suite is designed to assist physicians in planning and monitoring drug therapy. Unlike many existing systems that rely on rigid rules or function as complex classifiers, the Far Eastern development is built on a hybrid architecture. It combines two powerful methods.
At the heart of the new system is an intelligent synthesis of two approaches. On one side are extensive and deeply structured knowledge graphs – complex, formalized databases in which medical knowledge about diseases, medications, mechanisms of action, side effects, and genetic factors is linked into a single logical network. This is the system’s “hard science” layer, its academic memory that contains validated protocols and established facts.
The second component gives the tool its real power and flexibility – a reasoning-by-analogy mechanism. This element allows the algorithm to move beyond simple pattern matching and operate in conditions of incomplete information or rare, highly complex cases.
The system functions much like an experienced physician confronted with an ambiguous situation. It turns to a memory of prior clinical cases, identifies historical precedents similar to the current patient, analyzes similarities and differences, and on that basis proposes well-founded therapeutic options. In this way, the AI does not merely retrieve a ready-made answer. It performs a form of intellectual reasoning that closely mirrors clinical thinking and professional intuition.
Accuracy, Safety, and Clinical Support
The deployment of intelligent systems represents a practical implementation of one of the key directions in the digital transformation of Russian healthcare. The system is positioned to enable a fundamentally new level of precision in medical prescribing. Within seconds, it can correlate hundreds of parameters – a patient’s full medical history, up-to-date laboratory results, genetic predispositions, all current medications, and their subtle interactions. This enables a shift from standardized treatment schemes to genuinely personalized therapy, where decisions are tailored to a patient’s unique clinical profile.
Equally important, the system serves as a safety mechanism that significantly reduces clinical risk. The algorithm continuously checks every potential prescription for dangerous interactions, particularly in cases of polypharmacy, where multiple drugs are taken simultaneously and it becomes difficult for a human clinician to track all possible combinations. Drawing on the analysis of thousands of similar cases, the system can issue early warnings about rare but serious side effects, making treatment not only more effective but also substantially safer.
The development is especially valuable in supporting physicians dealing with complex or rare conditions, as well as for doctors working in regions where access to expert panels and specialist consultations may be limited. In this context, the system acts as an always-available intellectual consultant – a second opinion that can suggest diagnostic hypotheses or therapeutic options grounded in global clinical experience.
Finally, the system addresses one of the most constrained resources in healthcare – physicians’ time. Doctors no longer need to manually process vast volumes of data, systematize new clinical guidelines, review dozens of case histories in search of analogues, or track emerging scientific literature. The artificial intelligence performs this analytical work and presents a structured, actionable conclusion.
Roadmap for Deployment
The researchers emphasize that the project is now moving from theory into practice. The next step is pilot testing in real clinical settings at medical institutions in Russia’s Far East. This phase will focus on collecting feedback, fine-tuning the algorithms for routine clinical workflows, and generating the first validated data on effectiveness.
In the medium term, plans include integration with federal and regional medical information systems such as EMIAS, certification as regulated medical software, and expansion of deployment to other regions of Russia.
Further development will expand the system’s functionality through the addition of modules for laboratory data analysis, medical imaging, and dynamic monitoring of treatment effectiveness. This trajectory points toward the creation of a universal assistant platform for attending physicians. The hybrid intelligence principle underlying the system is also of interest to the global scientific and IT communities, as it demonstrates a practical model for combining structured medical knowledge with adaptive reasoning.
