###### Abstract
Today, however, SWIR cameras are available, albeit with some restrictions, to researchers and the general public, and as a result, the applications of SWIR imaging are expanding in the biomedical field, especially in clinical settings [4; 8; 9; 10; 11; 12; 13; 14; 15]. The clinical SWIR images and data, however, are inherently multifactorial and non-linear, which makes their interpretation highly complex [16]. Consequently, the need for real-time and accurate processing methods for SWIR clinical results are increasingly imperative [17]. Machine learning (ML)--as a disruptive technology--holds the potential to revolutionize the biomedical sector by providing efficient solutions for processing of the multifactorial SWIR clinical data [18]. ML encompasses models and algorithms that iteratively enhance their performance through a learning process by interpreting relationships found in large amounts of data [19]. In the context of personalized medicine, ML algorithms can sift through vast and diverse patient data, including diagnostic imaging, medical histories, and genetic profiles, to identify intricate patterns, associations, and correlations that may be beyond the human capacity to discern. This analytical tool has great potential to empower healthcare professionals to precisely predict disease risks, recommend personalized treatment regimens, and even anticipate adverse events, all tailored to an individual's unique biological makeup. Particularly, when combined with ML techniques, SWIR imaging has the potential to provide highly detailed and patient-specific information about tissues and disease states. This technique can enable the creation of bioinstruments that are tailored to individual patients' needs and conditions. For example, SWIR imaging could be used to analyze specific biomarkers or tissue characteristics in a patient's body, and ML algorithms could then process these data to make treatment recommendations or assist in the design of personalized medical devices or instruments [20]. Furthermore, in the field of personalized medicine, ML can enhance and optimize the performance of biomedical devices, particularly imaging  
Figure 1: SWIR vs. NIR. Fluorescent images taken from a mouse head with a long pass (LP) filter in the (**A**) NIR range, and (**B**) SWIR range. Higher contrast with more details from vasculature can be seen in the SWIR image. Figure adapted with permission from [8]. instruments. Ultimately, ML has the potential to serve as the backbone that connects the dots between data-driven insights, cutting-edge technology, and the overarching goal of healthcare that revolves around the individual patient, ultimately shaping the future of medicine into a more personalized, precise, and effective endeavor. This review aims to delve into diverse applications of ML-empowered SWIR techniques in the domains of personalized medicine and the visionary concept of individualized bioinstruments, where these models can undertake invaluable tasks, such as aiding in clinical diagnosis and result interpretation, optimizing system efficiency, and facilitating quantitative measurements. This review is motivated by the transformative impact of personalized medicine in healthcare, aiming to tailor interventions based on individual genetic, molecular, and clinical profiles. Focusing on SWIR techniques in biomedical optics, the research explores SWIR's potential to enhance diagnostics, imaging, and therapeutic interventions by penetrating biological tissues with reduced attenuation. By integrating SWIR with machine learning, the study seeks unprecedented accuracy in disease detection and treatment guidance. Despite promising biomedical demonstrations, the clinical potential of SWIR remains underexplored. This paper demonstrates how the synergy between SWIR imaging and ML is reshaping biomedical research.