TY - GEN
T1 - A Boxology-Based Analysis of Design Patterns for Neuro-Symbolic Medical Decision Making Systems
AU - Ng, Chi Him
AU - ten Teije, Annette
AU - van Harmelen, Frank
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025
Y1 - 2025
N2 - This paper presents a structured analysis of neuro-symbolic design patterns for medical decision-making systems through a graphical notation (the “boxology”) for neuro-symbolic architectures. We formalize and validate five archetypal neuro-symbolic architectures initially defined through textual descriptions and informal diagrams by Kierner et al. We systematically define and refine these archetypes across 68 systems from the literature. Our contributions include: (i) a formalization of these archetypes, (ii) empirical validation of these archetypes via system refinements, (iii) enhanced understanding of neuro-symbolic integration in clinical applications, and (iv) establishing the boxology as a robust tool for comparative architectural analysis. The findings indicate that the elementary patterns within the boxology framework remain consistent across clinical applications, offering new avenues for systematic development and comparison in neuro-symbolic AI for healthcare.
AB - This paper presents a structured analysis of neuro-symbolic design patterns for medical decision-making systems through a graphical notation (the “boxology”) for neuro-symbolic architectures. We formalize and validate five archetypal neuro-symbolic architectures initially defined through textual descriptions and informal diagrams by Kierner et al. We systematically define and refine these archetypes across 68 systems from the literature. Our contributions include: (i) a formalization of these archetypes, (ii) empirical validation of these archetypes via system refinements, (iii) enhanced understanding of neuro-symbolic integration in clinical applications, and (iv) establishing the boxology as a robust tool for comparative architectural analysis. The findings indicate that the elementary patterns within the boxology framework remain consistent across clinical applications, offering new avenues for systematic development and comparison in neuro-symbolic AI for healthcare.
UR - https://www.scopus.com/pages/publications/105009831890
UR - https://www.scopus.com/pages/publications/105009831890#tab=citedBy
U2 - 10.1007/978-3-031-95838-0_33
DO - 10.1007/978-3-031-95838-0_33
M3 - Conference contribution
AN - SCOPUS:105009831890
SN - 9783031958373
VL - 1
T3 - Lecture Notes in Computer Science
SP - 333
EP - 343
BT - Artificial Intelligence in Medicine
A2 - Bellazzi, Riccardo
A2 - Sacchi, Lucia
A2 - Juarez Herrero, José Manuel
A2 - Zupan, Blaž
PB - Springer Nature
T2 - 23rd International Conference on Artificial Intelligence in Medicine, AIME 2025
Y2 - 23 June 2025 through 26 June 2025
ER -