MULTIMODAL ARTIFICIAL INTELLIGENCE FOR PREDICTING HAIR FOLLICLE SURVIVAL AND AESTHETIC OUTCOMES FOLLOWING HAIR TRANSPLANTATION SURGERY

Background: Artificial intelligence (AI) has considerable potential to support outcome assessment in hair transplantation; however, its clinical reliability depends on the availability of complete, standardized, and appropriately linked patient-level data. Despite growing interest in AI-assisted aesthetic and clinical assessment, the readiness of currently reported hair-transplantation data for predictive and multimodal modelling remains insufficiently characterized. Objective: This study aimed to quantitatively assess the completeness, consistency, and multimodal readiness of literature-derived hair-transplantation data, with particular emphasis on hair survival, patient satisfaction, follow-up reporting, and the availability of clinical and trichoscopic imaging features. Methods: A descriptive data-readiness assessment was conducted using 36 literature-derived records extracted from three published studies. An additional 1,000 simulated records were maintained separately for technical testing and excluded from all clinical summaries. Data completeness, variable availability, outcome distributions, and reporting inconsistencies were examined using descriptive statistics. No missing values were imputed, and no predictive model was trained or validated. The analysis therefore focused on characterizing the strengths and limitations of the available data for future AI development. Results: Numerical hair-survival values were available for 26 of the 36 literature-derived records (72.2%). Among these records, mean hair survival was 85.30% (standard deviation [SD], 10.60), with a median of 85.90% and a range of 45.1%–96.8%. Satisfaction information was available for 27 records, comprising 17 classified as mostly satisfied, seven as very satisfied, and three as dissatisfied. Although 24 of these 27 records carried favorable satisfaction labels (88.9%), differences in reporting and the absence of a standardized assessment instrument limit direct clinical interpretation. Follow-up information was recorded for 27 records, but some entries represented study-level reporting intervals rather than verified patient-specific assessments. Most importantly, no original clinical or trichoscopic images were available in the analyzed dataset, and key imaging-derived features including vascularity, fibrosis, and trichoscopic characteristics were absent. These limitations restrict outcome comparability and prevent the construction of a genuinely linked multimodal dataset from the available records. Conclusion: This study identifies a substantial data-readiness gap between the potential of AI in hair transplantation and the structure of currently available literature-derived data. Although reported hair-survival values provide a preliminary descriptive benchmark, incomplete outcome reporting, inconsistent assessment definitions, uncertain patient-level follow-up alignment, and absent linked imaging data constrain robust predictive modelling. Progress toward clinically credible AI systems will require standardized outcome definitions, source-verified patient-level data, harmonized follow-up schedules, and prospectively linked clinical and trichoscopic images. The findings provide a practical basis for designing future datasets and validation studies before predictive AI claims can be justified.