# Clinical AI in Radiology: Foundations, Trends, Applications, and Emerging Directions

## TL;DR

This paper reviews how AI is being used in radiology to improve image interpretation, workflow efficiency, and patient privacy while addressing challenges like usability and data sharing.

## Contribution

The paper introduces novel AI applications in radiology, including local LLMs for report structuring, multimodal frameworks for cachexia detection, and privacy-preserving federated learning.

## Key findings

- Local large language models improve radiology report clarity and consistency without external resources.
- Multimodal AI combining imaging and clinical data enables early detection of cachexia in pancreatic cancer.
- Federated learning allows cross-institutional collaboration without sharing raw patient data.

## Abstract

Artificial intelligence (AI) is increasingly being explored in radiology to assist with image interpretation, organize clinical information, and improve workflow efficiency. This review summarizes the foundations of clinical AI in radiology and highlights current trends shaping its development and use in medical imaging. Several representative examples are presented, including locally deployed language models that help structure radiology reports, multimodal AI approaches for early detection of cachexia, federated learning systems that enable collaboration across institutions without sharing patient data, and automated methods for removing identifying information from radiology images and reports. Emerging AI-driven directions are also discussed, including tumor board decision support, clinical trial matching, radiology report quality assurance, and the development of an imaging complexity index. Together, these developments illustrate how AI is gradually being integrated into radiology practice while maintaining clinician oversight and protecting patient privacy.

Artificial intelligence (AI) is at the vanguard of transforming radiology in several ways, including augmenting diagnoses, improving workflows, and increasing operational efficiency. Several integration challenges, including concerns over privacy, clinical usability, and workflow compatibility, still remain. This review discusses the foundations and current trends of clinical AI in radiology to provide essential context for ongoing developments. To illustrate translational potential, we describe representative applications, including: (1) local deployment of large language models (LLMs) for restructuring and streamlining radiology reports, improving clarity and consistency without relying on external resources; (2) multimodal AI frameworks combining CT images, clinical data, laboratory biomarkers, and LLM-extracted features from clinical notes for early detection of cachexia in pancreatic cancer; (3) privacy-preserving federated learning (FL) infrastructure enabling collaborative AI model development across institutions without sharing raw patient data; and (4) an uncertainty-aware de-identification pipeline for removing Protected Health Information (PHI) from radiology images and clinical reports to support secure data analysis and sharing. We further discuss emerging opportunities for tumor board decision support, clinical trial matching, radiology report quality assurance, and the development of an imaging complexity index. Collectively, these applications highlight the importance of local deployment, multimodal reasoning, privacy preservation, and human-in-the-loop oversight in translating AI models from research to oncology radiology practice.