AI in Medicine

Neuromuscular diseases are biologically diverse, clinically heterogeneous, and often difficult to diagnose and treat, highlighting the need for computational tools that can help resolve overlapping phenotypes and support timely, mechanism-informed interventions. This narrative review synthesizes recent advances in artificial intelligence (AI) and machine learning applied to neuromuscular diseases across diagnosis, outcome modeling, biomarker development, and therapeutics. AI-based approaches may assist clinical and genetic diagnosis from phenotypic data; however, early phenotype-driven tools have seen limited clinician adoption due to modest accuracy, usability challenges, and poor workflow integration. Electrophysiological studies remain central to diagnosing neuromuscular diseases, and AI shows promise for accurate classification of electrophysiological signals. Predictive models for disease outcome and progression—particularly in amyotrophic lateral sclerosis—are under active investigation, but most remain at an early stage of development and are not yet ready for routine clinical use. Digital biomarkers derived from imaging, gait, voice, and wearable sensors are emerging, with MRI-based quantification of muscle fat replacement representing the most mature and widely accepted application to date. Efforts to apply AI to therapeutic discovery, including drug repurposing and optimization of gene-based therapies, are ongoing but have thus far yielded limited clinical translation. Persistent barriers to broader adoption include disease rarity, data scarcity, heterogeneous acquisition protocols, inconsistent terminology, limited external validation, insufficient model explainability, and lack of seamless integration into clinical workflows. Addressing these challenges is essential to moving AI tools from the laboratory into clinical practice. We conclude with a practical checklist of considerations intended to guide the development and adoption of AI tools in neuromuscular disease care.

Within the framework of the ongoing development of application of Machine Learning models in Medicine and Physical Therapy, the development of accurate prognosis algorithms for postoperative patients during the rehabilitation phase remains an area requiring further refinement. This paper examines hybrid Deep Learning models that integrate Convolution Neural Networks, Long Short-Term Memory and Gated Recurrent Unit networks, as well as genetic algorithm optimization for feature selection for predicting the time needed for a patient to rehabilitate. Patient data included features like age, passive range of available movements (preoperative and postoperative) and total rehabilitation time. Genetic Algorithm optimization for feature selection indicated that 4 out of the 16 available features are adequate for predicting rehabilitation time. Hybrid Deep Learning models achieved a Root Mean Squared Error (RMSE) of 12 days (less than 0.4 months) in rehabilitation time prediction, demonstrating good performance on a relatively small dataset of 120 patients.

The increasing challenges of healthcare systems demand innovative approaches to resource optimization, particularly for hospitals operating under economic and operational constraints. This study investigates the organizational and managerial factors influencing scale efficiency in 127 Italian hospitals, leveraging advanced machine learning (ML) techniques to identify key determinants of efficiency. A multi-level framework was developed, integrating Principal Component Analysis (PCA), Data Envelopment Analysis (DEA), and K-Means clustering to assess the interplay between energy costs, staff composition, and medical equipment across hospital levels. To enhance predictive capabilities, a classification model based on K-Nearest Neighbors (K-NN) was implemented, demonstrating high performance in distinguishing efficiency classes and confirming the importance of targeted resource management strategies. Additionally, the use of LIME (Local Interpretable Model-agnostic Explanations) provided actionable insights into the contribution of individual variables, enabling a deeper understanding of their impact on operational efficiency. In conclusion, this research highlights the importance of an integrated approach to support decision-makers in managing hospital resources, offering innovative tools to optimize efficiency and ensure the economic and operational sustainability of the healthcare system.