Artificial intelligence for optimizing medical laser procedure parameters: a data pipeline, performance metrics, and safety-first framework

Abstract

Medical laser procedures require careful selection and control of parameters such as wavelength, pulse duration, fluence, spot size, repetition rate, scanning pattern, and exposure time to balance efficacy, tissue selectivity, and safety.Artificial intelligence (AI), including machine learning and control-oriented learning, is increasingly positioned to support quality management, automatic tuning, and real-time feedback during laserbased interventions.This review synthesizes the role of AI across the total product life cycle for laser parameter optimization, emphasizing end-to-end data pipelines, clinically meaningful performance metrics, and layered safety mechanisms, rather than device-centric feature descriptions.We propose a structured framework that links data provenance, model objectives, uncertainty handling, and deployment monitoring to risk management processes and clinical evaluation standards.Key considerations include multimodal data integration from device telemetry and sensing, definition of optimization targets that reflect both outcomes and adverse events, calibration and robustness testing under domain shift, and implementation of safety constraints with hard limits and human override.Regulatory-aligned practices such as Good Machine Learning Practice and transparency principles, as well as reporting and bias-assessment guidelines for clinical AI, are mapped to the laser workflow to support reproducibility and safer translation.

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