Background/Objectives: Artificial intelligence is increasingly used in medicine, particularly in orthopedics, to support medical image interpretation, diagnosis, clinical decision-making, and treatment selection. Its integration into clinical practice depends largely on doctors’ perceptions and attitudes toward this technology. This study aimed to identify the main factors associated with Romanian orthopedic doctors’ attitudes toward AI and their intention to use it in clinical practice. Methods: The study included 166 doctors specializing in Orthopedics and Traumatology in Romania. Data were collected through an online questionnaire using non-probability snowball sampling. The conceptual model examined the associations between five factors and doctors’ attitudes toward AI: perceived usefulness, perceived ease of use, trust in AI, digital competencies, and perceived advantages. The relationship between attitude and intention to use AI was also analyzed. The model was tested using structural equation modeling with WarpPLS 8.0. Results: All factors were positively and significantly associated with doctors’ attitudes toward AI. Perceived usefulness showed the largest association with attitude (β = 0.27), followed by digital competencies (β = 0.22). The model explained 83% of the variance in attitude. Attitude was positively associated with intention to use AI (β = 0.65), with the model explaining 43% of the variance in intention. The model showed a GoF of 0.707 and an AFVIF of 2.645. Conclusions: The findings may support healthcare managers in developing digitalization strategies, structured digital competency training, and clear institutional guidelines for the safe and effective use of AI in clinical practice.

Background: The quantification of Dexamethasone Sodium Phosphate (DSP) in injectable formulations is significantly hindered by the spectral overlap of the stabilizer creatinine within the UV region. This study aims to develop a green first-order derivative (D¹) spectrophotometric method to resolve this analytical challenge. Methods: Distilled water was utilized as a sustainable solvent, aligning with green chemistry principles. To ensure high specificity, a matrix-matching calibration strategy with a constant 1:2 (w/w) DSP:creatinine mass ratio across the entire concentration range was employed. DSP was determined using the zero-crossing technique, measuring the D¹ amplitude at λ_ZC ≅ 231.3 nm, where the creatinine contribution is nullified. Results: Linearity was established for DSP concentrations between 4.0–16.0 μg/mL (R² > 0.99). Method validation, as per ICH Q2 (R1) guidelines (International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use), demonstrated excellent accuracy (mean recovery of 99.85%) and precision (RSD < 2%). Conclusions: The proposed method offers a rapid, cost-effective, and eco-friendly alternative for the routine quality control of DSP injectables, eliminating the necessity for complex chromatographic separation techniques.