Explainable solutions from artificial intelligence for health-care support systems

For decades, efforts to standardize medical care have struggled to fundamentally reduce errors and unjustified variations in medical practice, largely due to the influence of the human factor. The formalization of clinical guidelines and computer-assisted interpretation makes it possible to provide decision-support tools to improve health-care quality. They can better influence clinician behavior than narrative guidelines. Medical ontologies and algorithms based on such ontologies allow the interpretation of formalized clinical documents (guidelines). To support health professionals as consultants, systems must provide reliable knowledge and rely on approaches explicitly explaining their recommendations. Integrating software engineering, knowledge engineering, and artificial intelligence advancements can provide health-care professionals with computer-interpretable clinical guidelines. These should be decision-support complexes combined under a common terminological framework capable of understanding patient health documents. The research focuses on an emerging concept of manufacturing systems working with digital clinical guidelines. The paper presents an architectural principle, a new technology for creating viable clinical decision support systems. It presents a development environment for constructing and controlling the system’s improvements. The main contributions of the study include the automation of multiple physician tasks by filling a single structured “medical history,” integration of formalized knowledge from clinical guidelines and other reliable sources to satisfy both the relevance of the methods used and personalization to patient, transparency of all applicable knowledge, explainability of advice based on the essence of the knowledge and linked to the source, and the integrability of decision-support complexes with neural network services, capable of inputting data from a structured medical history.