Artificial Intelligence in MSK Radiology: Current Applications, LLMs, Agentic AI, and Future Directions.
5 affiliations
- 1Department of RadiologyAIG HospitalsHyderabadIndia.
- 2College of Medicine and Dental Sciences, University of BirminghamBirminghamUnited Kingdom.
- 3Emergency and Trauma Radiology, Sunnybrook Health Sciences Centre, University of TorontoTorontoCanada.
- 4Department of Musculoskeletal RadiologyUniversity Hospitals of North Midlands, Stoke on TrentUnited Kingdom.
- 5Department of Musculoskeletal RadiologyRoyal Orthopaedic HospitalBirminghamUnited Kingdom.
Abstract
The intersection of musculoskeletal (MSK) radiology and artificial intelligence (AI) represents a profound technological evolution in modern medicine. Confronted with exponentially increasing imaging volumes, a shortage of subspecialized radiologists, and the anatomical complexity of osteoarticular pathologies, the radiological community increasingly relies on computational solutions. This comprehensive review delineates the trajectory of AI in MSK radiology, tracing the transition from rudimentary computer-aided diagnosis systems to the sophisticated deep learning networks, multimodal architectures, and large language models defining the present era. Contemporary applications span the entire imaging value chain. In diagnostics, AI algorithms automate fracture detection with high sensitivity comparable to human readers, classify bone tumors via high-throughput radiomics, and execute opportunistic screening for systemic conditions such as osteoporosis. In procedural contexts, AI substantially enhances MSK interventions through advanced needle tracking, thermal mapping, and robotic-assisted orthopaedic surgery. Operationally, AI optimizes workflows by accelerating magnetic resonance imaging acquisition times by up to 75%, intelligently triaging urgent cases, and drafting structured reports in half the traditional time. Looking forward, the paradigm is rapidly shifting toward agentic AI and digital twins, promising highly personalized patient care. Alongside these technological triumphs, this report critically examines the ethical, legal, and educational frameworks required to safely integrate these transformative tools into clinical practice, ensuring AI serves as an augmentative instrument rather than a replacement for human expertise.
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BibTeX
@article{chapala2026artificial,
title = {{Artificial Intelligence in MSK Radiology: Current Applications, LLMs, Agentic AI, and Future Directions.}},
author = {Shashank Chapala and Jonathan Gibson and Praveen Chinniah and Vivek Kiyawat and Rajesh Botchu},
journal = {The Indian journal of radiology \& imaging},
year = {2026},
month = jul,
doi = {10.1055/s-0046-1825800},
url = {https://europepmc.org/article/MED/42921646},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Artificial Intelligence in MSK Radiology: Current Applications, LLMs, Agentic AI, and Future Directions.
AU - Shashank Chapala
AU - Jonathan Gibson
AU - Praveen Chinniah
AU - Vivek Kiyawat
AU - Rajesh Botchu
PY - 2026
DA - 2026/07/20
JO - The Indian journal of radiology & imaging
DO - 10.1055/s-0046-1825800
UR - https://europepmc.org/article/MED/42921646
AB - The intersection of musculoskeletal (MSK) radiology and artificial intelligence (AI) represents a profound technological evolution in modern medicine. Confronted with exponentially increasing imaging volumes, a shortage of subspecialized radiologists, and the anatomical complexity of osteoarticular pathologies, the radiological community increasingly relies on computational solutions. This comprehensive review delineates the trajectory of AI in MSK radiology, tracing the transition from rudimentary computer-aided diagnosis systems to the sophisticated deep learning networks, multimodal architectures, and large language models defining the present era. Contemporary applications span the entire imaging value chain. In diagnostics, AI algorithms automate fracture detection with high sensitivity comparable to human readers, classify bone tumors via high-throughput radiomics, and execute opportunistic screening for systemic conditions such as osteoporosis. In procedural contexts, AI substantially enhances MSK interventions through advanced needle tracking, thermal mapping, and robotic-assisted orthopaedic surgery. Operationally, AI optimizes workflows by accelerating magnetic resonance imaging acquisition times by up to 75%, intelligently triaging urgent cases, and drafting structured reports in half the traditional time. Looking forward, the paradigm is rapidly shifting toward agentic AI and digital twins, promising highly personalized patient care. Alongside these technological triumphs, this report critically examines the ethical, legal, and educational frameworks required to safely integrate these transformative tools into clinical practice, ensuring AI serves as an augmentative instrument rather than a replacement for human expertise.
SN - 0971-3026
SN - 1998-3808
ER -