Eye-tracking has demonstrated significant potential for analyzing visual attention in medical research. Nevertheless, technical challenges persist in establishing robust and customizable study designs ensuring reproducible data collection in complex scenarios. In prior work, we proposed a vendor-agnostic eye-tracking platform for conducting studies in digital (neuro)pathology. However, the initial platform was validated in an expert-supervised setting and lacked systematic evaluation of interaction design, user onboarding, and data quality mechanisms. Additionally, it exhibited a high degree of specificity, being closely associated with a particular medical domain. In order to address these limitations, a comprehensive set of cross-domain requirements was elicited and incorporated into a requirement-driven redesign resulting in a refined modular eye-tracking platform that was subsequently implemented and empirically validated. The redesigned platform contributes configurable multi-class task handling, flexible study flow components, and integrated ground truth–based feedback mechanisms. The platform was evaluated with 14 physicians from dermatology performing multi-class wound classification under eye-tracking conditions. Quantitative fixation analyses with respect to labeled regions of interest were combined with standardized usability assessment using the System Usability Scale (SUS). Findings indicate the system's dependable performance in a clinical setting and its exceptional usability, as evidenced by an average SUS score of 83.75. The findings of this study indicate that the refined platform enables standardized, reproducible, and domain-agnostic eye-tracking studies while significantly lowering technical barriers for study execution in clinical research environments.