Work

Technical review of a clinician-driven low-code workflow for anatomical segmentation in radiologic imaging

Year: 2026

Type: article

Abstract: Artificial intelligence (AI) holds immense promise for enhancing medical imaging analysis, particularly in the realm of anatomical segmentation. However, the technical complexities of developing and deploying AI models, often requiring substantial coding expertise, have traditionally posed a barrier to entry for many clinicians. This review explores the emerging landscape of low-code (LC) AI solutions in medical imaging, focusing on their potential to empower radiologists and other healthcare professionals to actively participate in AI development. We examine a practical workflow using the fastMONAI library, an LC extension of established tools like MONAI and fastai, demonstrating how clinicians can train a U-Net-based model for cardiac MRI segmentation with minimal coding. This approach significantly reduces the technical overhead, enabling clinicians to focus on clinically relevant aspects of model development and customization. The review highlights the benefits of LC AI in fostering a more inclusive and collaborative environment for AI innovation in radiology, while also acknowledging the potential limitations and considerations for successful implementation.

Source: Future Health

DOI: 10.25259/fh_24_2025

Authors: Ankush Ankush, Babu Lal, Samriddhi Burman, Ojasvi Nagayach, Sanjay Sardessai

Institutions: All India Institute of Medical Sciences Bhopal, Manipal Academy of Higher Education, Goa Medical College

Language: English

Topic: Artificial Intelligence in Healthcare and Education

Subfield: Health Informatics

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: hybrid

OpenAlex ID: W7130834695