**4. Challenges and perspectives**

In conclusion, most current studies consider hydrogels to be ideal candidates for synthetic wound dressings because of their 3D structure and high water content similar to skin, which ensures a moist environment for wounds [96, 97]. A wide variety of polymers have been used alone or in blends to create hydrogels designed for biomedical applications, with a focus on wound healing and less scarring [98–101]. In addition, most of the current research shows that non-invasive optical technology DRS and OCT may be useful for research related to abnormal wound healing. Although artificial intelligence has only realized the diagnosis of skin diseases [49, 52–55, 102], it will be gradually applied to the management of wound healing in the future.

Although the application of multifunctional composite hydrogels has obvious advantages, most of them are in the basic research stage of animal experiments, and there is still a lack of large-scale clinical studies to prove their efficacy and safety. In order to prevent and reduce the occurrence of adverse reactions, the indications and correct operation methods of each material hydrogel should be strictly mastered, and a comprehensive analysis of individuals should be carried out to remove adverse factors. We still have a long way to go in clinical application of wounds [103].

Dayong Yang et al. prepared persistent luminescent nanoparticles (PLNPs) containing a hydrogel (PL-gel) for targeted, sustained, and autofluorescence-free tumor metastasis imaging [104]. Professor Yu Lin's team develop a tri-modal bioimaging technique, they longitudinally and non-invasively track the degradation behavior of materials by designing and synthesizing thermosensitive hydrogels containing macromolecular fluorescent probes and magnetic resonance imaging (MRI) contrast agents, utilizing the collaborative application of optical techniques such as ultrasound, fluorescence, and MRI [105]. At present, the combination of hydrogel and optical technology mainly focuses on the tracking function. There are relatively few studies on the monitoring function of hydrogel in vivo efficacy, and there is still a lot of room for improvement.

The past few years have witnessed many changes in the fields of ML and Computer Science. Following this long progress, one may see many exciting developments in the next few years, but there are challenges before it can become more robust and be widely adopted in the clinic. AI is constrained by a lack of high quality, high volume, longitudinal, outcomes data [106]. Even the same image modality on the same disease site, the parameters of the imaging setting and protocols might be different in different clinical settings [91, 107]. But we believe that AI can play an important role in medical imaging and disease diagnosis when we master how to organize and preprocess data generated from different institutions and can encourage more sharing of image data.

Although there are relatively few studies on the use of deep learning in skin OCT and DRS imaging, current research on disease diagnosis systems combining AI and OCT leads us to believe that optical technology can be fully integrated with AI for wound healing monitoring [108]. This not only helps us to better detect the recovery of wounds treated with hydrogel dressings, but also more accurately evaluate the effectiveness of hydrogel treatment. In addition, AI based on optical images also helps to determine the type and depth of wounds, and can better design corresponding hydrogel wound dressings. Finally, we also believe that more and more new and safe wound dressings will be developed and applied with the aid of AI and optical imaging technology.
