Work

Beyond Algorithms: Conceptual Innovation in Medical Imaging AI

Year: 2026

Type: article

Abstract: , which reframes what problems are posed, how success is measured, and why an approach is clinically relevant. We argue that prevailing incentive structures, training pathways, and publication norms disproportionately reward algorithmic novelty, particularly for early-career researchers, while at times undervaluing conceptual contributions that are essential for scientific maturation and clinical translation. Through representative examples from medical imaging AI, we show how insufficient conceptual grounding can lead to misaligned objectives, fragile generalization, and limited real-world impact. We conclude with actionable recommendations for researchers, mentors, reviewers, and journals to better recognize, support, and integrate conceptual innovation alongside algorithmic advances.

Source: IEEE Access

DOI: 10.1109/access.2026.3733388

Author: Mark A. Anastasio

Institution: Washington University in St. Louis

Language: English

Topic: Artificial Intelligence in Healthcare and Education

Subfield: Health Informatics

Field: Medicine

Domain: Health Sciences

Open Access status: gold

Estimated APC paid: $2,075

Funders: National Institutes of Health, National Institute of Biomedical Imaging and Bioengineering