Botulinum toxin (BoNT) injection is widely used in aesthetic dermatology, plastic surgery, neurology, and rehabilitation, but treatment still depends on the clinician's experience, anatomical judgment, and iterative dose adjustment. Artificial intelligence (AI) may support BoNT practice through facial analysis, anatomical mapping, treatment simulation, response prediction, image-guided injection, and objective outcome assessment. Current evidence includes chatbot-assisted planning, deep learning analysis of facial expression, magnetic resonance imaging-based prediction of dystonia response, multimodal machine learning for spasticity, computational diffusion modeling, and AI-assisted ultrasound interpretation. This mini review, based on PubMed-indexed and related peer-reviewed literature, summarizes representative AI applications across the BoNT treatment pathway and emphasizes a cautious clinical framework in which AI supports, but does not replace, physician judgment. Evidence remains preliminary and heterogeneous, with limitations related to small sample sizes, retrospective designs, and limited external validation. However, this mini-review synthesizes the most recent and relevant high-quality studies and provides a timely, structured overview of emerging AI applications to support clinical decision-making in BoNT practice. Future translation should prioritize prospective validation, formulation-aware dose modeling, transparent governance, and physician-supervised implementation.
Introduction and background
Botulinum toxin (BoNT) injection is a globally significant, multi-billion dollar, minimally invasive procedure used in aesthetic medicine for dynamic wrinkles and facial contouring, and in neurology and rehabilitation for conditions like focal dystonia and spasticity. Despite its broad adoption, BoNT injection remains operator-dependent, with outcomes influenced by complex factors like anatomy, product formulation, and injection technique. Artificial intelligence (AI), encompassing computer vision, deep learning, and large language models, is being explored to enhance BoNT care by quantifying facial features, supporting treatment planning, assisting image interpretation, simulating dose-response relationships, and standardizing outcomes. However, these tools are still in early stages and require physician supervision due to existing validation gaps, potential biases, privacy concerns, and unclear regulatory pathways.
Search strategy and scope of review
This narrative mini-review followed PRISMA-informed principles to ensure transparency and reproducibility, though it was not a full systematic review due to the broad and evolving nature of AI applications in BoNT therapy. Literature searches were conducted in PubMed, Scopus, Embase, and Web of Science from January 2018 to May 2026, using various combinations of terms related to artificial intelligence and botulinum toxin. Conference abstracts, preprints, non-English studies, case reports, and nonclinical AI studies were excluded to focus on high-quality, peer-reviewed clinical evidence.
Core AI technologies relevant to botulinum toxin injection
The most pertinent AI technologies for BoNT treatment include computer vision for automating landmark detection, wrinkle segmentation, and asymmetry measurement, alongside convolutional neural networks (CNNs) and transformer-based models for standardized pre- and post-treatment comparisons. Prediction models integrate structural MRI, gait kinematics, ultrasound features, and clinical scores to estimate BoNT response. Image-guided AI, specifically AI-enhanced ultrasound, aims to improve interpretation by segmenting vessels, nerves, and muscle layers. Large language models (LLMs) can aid in guideline synthesis, patient education, and preliminary treatment plan generation, though expert review is vital to mitigate risks like 'hallucinations' or unsafe recommendations.
AI in injection planning and personalized protocol development
AI offers an intuitive application in BoNT injection planning by leveraging baseline photographs to identify landmarks, quantify asymmetry, estimate wrinkle severity, and generate candidate treatment plans. These AI-generated outputs can improve documentation and communication but should not replace the clinician's anatomical examination, informed consent process, or professional judgment. Comparative studies have shown that advanced language-vision models can produce more complete and personalized facial injection plans, yet some outputs still contain inappropriate or high-risk suggestions, underscoring the necessity of physician oversight. AI-assisted planning tasks include landmark detection, wrinkle/symmetry quantification, candidate injection-point generation, and dose/volume simulation, each with specific values, limitations, and physician roles.
AI in efficacy prediction and patient screening
BoNT response prediction through AI could minimize trial-and-error treatments, reduce costs, and optimize retreatment decisions. Examples include DystoniaBoTXNet, which uses brain MRI and clinical variables to predict BoNT efficacy in isolated dystonia, and multimodal machine learning approaches combining clinical and ultrasound features to predict response in post-stroke spasticity. Gait rehabilitation studies also use AI to predict post-treatment trajectories. A significant challenge is that BoNT formulations are not interchangeable, with variations in units, characteristics, dosing conventions, diffusion behavior, and duration of action. Therefore, AI models trained on one product or indication may not safely generalize to others, necessitating formulation-aware dose modeling and careful validation.
AI in injection guidance, simulation, and outcome assessment
AI can support both procedural and post-procedural phases of BoNT care. In guidance, AI-enhanced ultrasound could assist in identifying anatomical structures like vessels, nerves, and muscle borders, though specific BoNT validation remains limited. Simulation models can estimate BoNT diffusion based on factors like volume, concentration, and tissue properties, potentially reducing off-target effects, but these require prospective clinical correlation. Outcome assessment is a more established application, where automated analysis of photographs or videos quantifies wrinkle reduction, symmetry, movement, and emotional expression before and after treatment. This objective assessment can improve reproducibility, particularly in conditions like facial palsy, but still needs full correlation with patient-centered outcomes.
Commercial AI software and patient-facing simulation tools
The aesthetic market increasingly offers commercial AI simulation platforms that provide photo-based previews of BoNT or filler outcomes. While these tools can facilitate communication and manage patient expectations, most are not independently validated as medical decision aids and should not be presented as definitive predictors of individual results. Their use also introduces concerns regarding unrealistic patient expectations, privacy of facial images, the commercialization of clinical decision-making, and potential biases inherent in aesthetic datasets, especially concerning diverse facial anatomies and skin tones.
Challenges and limitations
Translating AI into routine BoNT therapy faces significant limitations, including small sample sizes, retrospective designs, and a lack of external validation across diverse populations and BoNT products in most current studies. Simulation studies provide useful hypotheses but cannot alone establish safety or efficacy. Dataset representativeness is a critical issue in facial-analysis AI, as training data often underrepresent certain demographics (e.g., Asian facial anatomy, male aesthetics, older patients, darker skin tones), leading to potential biases and inequitable recommendations. Unresolved challenges also include explainability (understanding AI's reasoning), accountability for adverse events, patient privacy, and clear regulatory classification for AI software as a medical device (SaMD). Overfitting in predictive models due to limited datasets highlights the need for larger, more diverse datasets and longitudinal follow-up.
Future directions
The most realistic near-term application for AI in BoNT therapy involves physician-supervised precision care, not autonomous injection. Future priorities include establishing standardized facial and ultrasound datasets, validating landmark and wrinkle analysis, developing formulation-aware dose simulation, implementing objective outcome measurement, and conducting prospective multicenter studies. Addressing socioeconomic, image acquisition, and device variability biases is crucial for robust model development, potentially through user-specific portable imaging and advanced software filters. While longer-term concepts like multimodal monitoring, robotic assistance, and 3D printed microneedle arrays are being explored, they remain investigational. Responsible AI translation also necessitates Explainable AI (XAI) frameworks and adherence to established reporting standards (e.g., CONSORT-AI, TRIPOD+AI) to build clinician trust and ensure scientific credibility, moving AI from exploratory tools to reliable clinical systems.
Conclusions
Artificial intelligence holds significant potential to enhance Botulinum Toxin therapy by improving assessment, planning, prediction, procedural guidance, simulation, documentation, and outcome measurement. Its most immediate and impactful role is as a physician-supervised tool that standardizes facial and functional evaluations, deepens anatomical understanding, supports dose planning based on formulation, and provides objective before-and-after treatment assessments. However, before AI can be routinely integrated into clinical practice, it requires extensive prospective multicenter validation, external testing across diverse patient populations, transparent reporting of methods and results, robust privacy protection measures, generation of explainable outputs, and clear regulatory classification.
