Effect of Advancements in Computer Technologies in Modern Healthcare Systems

Traditional digital healthcare primarily focuses on passive storage systems where the data is not used for making a clinical narrative or model new biological mechanics. To overcome these limitations, health informatics is adopting Large Language Models (LLMs) to automate clinical workflows along with quantum computing to device new biological semantics via faster simulations. However, there are limitations to using these new technologies in healthcare with respect to LLM hallucinations, algorithmic bias, quantum hardware availability and a lack of standardized protocol. This review evaluates the transition from foundational digital systems to advanced computational paradigms, analyzing their clinical utility, practical challenges, and integration requirements. The objective of this review is to evaluate the role of new technologies in computer science in modern healthcare systems. This review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA) guidelines, searching PubMed, IEEE Xplore, and Scopus databases to evaluate peer-reviewed literature published between 2018 and 2026. The review navigates through existing foundational systems like electronic health records, medical imaging, and telemedicine that help with digitizing patient information and improved clinical communication, while remaining largely as passive data repositories to emerging technologies like LLMs and quantum computing that acts as methods for synthesizing new information rather than merely storing it. The reviewed literature demonstrates that LLM effectively automate medical documentation, actively encode clinical knowledge but they pose a risk of algorithmic bias, causing clinical errors and hallucinated results along with data privacy concerns under healthcare regulations. Quantum computing on the other hand highlights strong theoretical capabilities in accelerating drug discovery and personalizing genomics. Though, scarcity of physical quantum hardware, limitations around scalable quantum algorithms, and premium pricing of quantum hardware, limit actual clinical deployments. Ultimately, medical science is in a translational phase. LLMs are computationally mature but require strict human oversight to prevent errors, while quantum computing remains unrefined for actual clinical deployments despite its promising theoretical capabilities. Bridging this gap requires establishing standardized validation methods that prioritize patient safety, clinical transparency, and data interoperability across healthcare platforms.