Background and Objectives: Clinical decision support systems (CDSS) can combine laboratory findings with additional clinical information to support diagnostic and therapeutic decision-making. This review characterizes the objectives of CDSS with relevance to laboratory medicine and examines the current evidence across six predefined domains: clinical maturity, type of evaluation, strength of evidence, integration potential, relevance to personalized medicine, and demonstrated medical benefit. Methods: PubMed was systematically searched for publications on CDSS with relevance to laboratory medicine published between 1 October 2019 and 31 August 2025. Publications without a specific laboratory-related component were excluded, and the detailed analysis was restricted to original publications. The 75 included publications were independently classified and assessed by the authors according to predefined criteria. Results were synthesized descriptively using absolute and relative frequencies across the predefined categories. Results: Publication activity showed an overall increase over the study period. The most common CDSS objectives were diagnostic support (25/75; 33%), therapy monitoring (14/75; 19%), and prognostic assessment (13/75; 17%). Only twelve publications (16%) investigated CDSS that provided specific recommendations for further diagnostic testing or therapy. Most evaluations were retrospective (31/75; 41%) or based on simulations and modelling (16/75; 21%); 13 publications (17%) used prospective non-randomized designs, and three (4%) described randomized designs, two of which were study protocols. In terms of clinical maturity, 45 CDSS were classified as experimental, six as prototypes, and 15 as clinically implemented. High integration potential was identified in 24 of the 75 publications (32%). Twenty publications (27%) met the predefined criteria for a positive effect on process or proxy parameters, and two met the criteria for clinical benefit; no publication demonstrated improvements in hard clinical endpoints. Only eight CDSS (11%) showed high relevance to personalized medicine. Conclusions: CDSS with relevance to laboratory medicine represent an active area of research. However, most systems remain at an early stage of development and have been insufficiently validated under real-world conditions. Sustainable clinical implementation will require prospective multicenter studies, patient-relevant endpoints, interoperable technical infrastructures, and stronger interdisciplinary involvement of laboratory medicine.