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Artificial Intelligence in Health

ORIGINAL RESEARCH ARTICLE
Explainable solutions from artificial intelligence

for health-care support systems

1,2
Valeriya Gribova 1,2 and Elena Shalfeeva *
1 Laboratory of Intelligent Systems, Institute of Automation and Control Processes, Far Eastern
Branch of RAS, Vladivostok, Primorsky Krai, Russia
2 Department of Software Engineering and Artificial Intelligence, Far Eastern Federal University,
Vladivostok, Primorsky Krai, Russia

Abstract
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
*Corresponding author: decision-support complexes combined under a common terminological framework
Elena Shalfeeva capable of understanding patient health documents. The research focuses on an
(shalf@iacp.dvo.ru)
emerging concept of manufacturing systems working with digital clinical guidelines.
Citation: Gribova V, Shalfeeva The paper presents an architectural principle, a new technology for creating viable
E. Explainable solutions from
artificial intelligence for health-care clinical decision support systems. It presents a development environment for
support systems. Artif Intell Health. constructing and controlling the system’s improvements. The main contributions
2025;2(3):138-153. of the study include the automation of multiple physician tasks by filling a single
doi: 10.36922/aih.5736
structured “medical history,” integration of formalized knowledge from clinical
Received: October 31, 2024 guidelines and other reliable sources to satisfy both the relevance of the methods
Revised: March 1, 2025 used and personalization to patient, transparency of all applicable knowledge,
explainability of advice based on the essence of the knowledge and linked to the
Accepted: April 25, 2025
source, and the integrability of decision-support complexes with neural network
Published online: May 8, 2025 services, capable of inputting data from a structured medical history.
Copyright: © 2025 Author(s).
This is an Open-Access article
distributed under the terms of the Keywords: Explanatory decision support system; Knowledge-enabled system;
Creative Commons Attribution Interpretable clinical guideline; Knowledge ontology; Viable system
License, permitting distribution,
and reproduction in any medium,
provided the original work is
properly cited.
1. Introduction
Publisher’s Note: AccScience
Publishing remains neutral with One way to improve health-care quality, reduce unwarranted variation in practice, and
regard to jurisdictional claims in
published maps and institutional lower health-care costs is through e-consultants. These tools are intelligent services and
affiliations. computer-interpreted clinical recommendations integrated into the workflow of medical

Volume 2 Issue 3 (2025) 138 doi: 10.36922/aih.5736