Noting that the conventional approaches clinicians use to choose patients’ prosthetic devices “often rely on subjective clinical judgment and static protocols, frequently overlooking individualized patient factors,” a team of researchers developed a clinical decision-making support system that uses artificial intelligence (AI) to deliver prosthesis recommendations.
The framework dynamically analyzes patient-specific parameters, such as amputation level, mobility classification, comorbidities, weight, and biomechanical characteristics to generate recommendations aligned with established clinical guidelines and uses evidence-based reasoning supported by an explanation module from large language models (LLMs).
Five lower-limb prosthesis users with complex comorbidity profiles and five clinicians evaluated the ProsthetiX-AI system and reported high ratings for accuracy and usability.
“A core innovation of the system lies in its ability to transparently justify outputs by retrieving peer-reviewed evidence, including mobility classification standards and weight-based component selection criteria,” according to the study’s authors, who concluded that the model can augment clinical decision-making, address challenges in adopting AI in healthcare, and provide a foundation for patient-centered decision support in resource-constrained environments.
The study, “ProsthetiX-AI: An LLM-based clinical decision support system for evidence-based prosthetic recommendations,” was published in Health Information Science and Systems.
