Protecting medical images in AI‑driven healthcare workflows is challenging because conventional watermarks are destroyed by routine deep learning operations such as classification, segmentation, or denoising. In this paper, a novel framework called AI‑Pipeline Persistent Latent Watermarking (APPL‑WM) is proposed. A binary watermark is embedded into multi‑scale latent features extracted by a hybrid encoder, and an invertible neural network guarantees perfect reversibility of the original image. A differentiable simulator of a realistic medical AI pipeline (CNN classifier, U‑Net segmenter, diffusion denoiser, and JPEG compression) is employed during training to enforce robustness. Experimental results on three medical imaging datasets show that the proposed method achieves high imperceptibility (PSNR of 42.35 dB, SSIM of 0.991) and retains 94.22 %-bit accuracy after the full AI pipeline, significantly outperforming state‑of‑the‑art methods (which drop below 72 %). The framework provides a robust and reversible solution for copyright protection and integrity verification of medical images processed by AI systems.