Background Glabellar neuromodulator injections are frequently guided by skin surface contraction patterns, assuming that visible rhytids reflect underlying muscular anatomy. However, emerging anatomical and imaging evidence has questioned the clinical relevance of pattern-based treatment adaptations. Objective To prospectively evaluate whether different glabellar contraction patterns influence clinical and patient-reported outcomes when a standardized, anatomy-based, FDA-approved 5-point injection algorithm is applied. Methods This prospective, multicenter clinical study enrolled 125 patients treated across five independent centers. Patients were classified into five previously described glabellar contraction patterns (V-shape, U-shape, converging arrows, omega, and inverted omega) based on standardized photographs during maximal frowning. All patients received neuromodulator injections following the same standardized 5-point technique using either onabotulinumtoxinA or abobotulinumtoxinA with dose equivalence. Clinical outcomes were assessed using the Glabellar Line Severity Scale (GLSS) at baseline, 15 days, and 90 days, while patient-reported outcomes were evaluated with the Global Aesthetic Improvement Scale (GAIS) at 90 days. Statistical analyses included non-parametric group comparisons and multivariate ordinal logistic regression. Results A total of 119 patients completed the study. A significant improvement in GLSS was observed at 15 days (p < 0.001), followed by partial regression at 90 days (p < 0.001 vs. baseline). No statistically significant differences in GLSS or GAIS were identified between contraction pattern groups at any time point (all p > 0.05). Multivariate analysis demonstrated that age and body mass index significantly influenced treatment outcomes, whereas glabellar contraction patterns and Fitzpatrick skin type did not. Mild, transient local adverse events were observed and resolved spontaneously. Conclusion Glabellar contraction patterns do not significantly affect clinical or patient-reported outcomes when neuromodulator injections are performed using a standardized, anatomy-based 5-point technique. These findings support prioritizing muscular anatomy and patient-specific biological factors over surface rhytid patterns in glabellar neuromodulation.
Artificial intelligence (AI) is rapidly transforming medical research and scholarly publishing, reshaping how scientific knowledge is produced, evaluated, and disseminated. Initially developed as a decision-support tool, AI has evolved into a complex ecosystem encompassing machine learning, deep learning, and large language models, with applications spanning data analysis, diagnostic support, evidence synthesis, manuscript preparation, peer review, and post-publication analytics. These technologies offer substantial benefits, including accelerated research workflows, improved analytical precision, enhanced reproducibility, and expanded access to scientific communication, particularly for early-career investigators and non-native English authors. However, the integration of generative AI introduces significant challenges. Persistent risks include algorithmic bias, hallucinated or misattributed citations, erosion of authorship accountability, confidentiality concerns, and the potential degradation of peer review integrity. As AI-generated outputs increasingly resemble human scholarly work, longstanding norms surrounding authorship, transparency, and responsibility are being reexamined. In response, editorial organizations, journals, and global health authorities have begun to establish governance frameworks emphasizing disclosure, human verification, and ethical boundaries for AI use. This narrative review synthesizes current evidence on the evolution and applications of AI in medical research and publishing, critically examines associated risks and ethical dilemmas, and reviews emerging regulatory and editorial guidance. Finally, it outlines future directions centered on explainable and auditable AI, standardized AI literacy, and hybrid human-AI workflows. Ensuring that AI remains a tool for augmentation rather than replacement will be essential to preserving trust, rigor, and integrity in medical scholarship as these technologies become increasingly embedded in the scientific enterprise.
