The rapid growth of multimodal data in the healthcare domain has created a pressing need for automated text summarization systems. With vast amounts of medical information available online, identifying relevant data has become increasingly challenging for healthcare professionals and researchers. To address this challenge, a method called Multimodal Summarization using Quantum-Inspired Genetic Algorithms (MSQIGA) has been developed, efficiently extracting crucial context from diverse multimodal inputs. This approach leverages a quantum-based genetic algorithm to process various forms of data, including text, images, and clinical notes, generating coherent and meaningful summaries. A key component of the MSQIGA framework is its objective function, which optimizes for relevance, redundancy, and coverage, ensuring that the generated summaries maintain high quality. By utilizing quantum principles, the search and optimization processes involved in summarization are enhanced. The summarization task is framed as a binary optimization problem, employing a modified quantum-inspired genetic algorithm to find the optimal solution for summarizing medical information. The MSQIGA method has been evaluated using publicly available healthcare datasets, such as MMCQS, MMIST-ccRCC, and MedPix 2.0. The evaluation metrics include ROUGE scores and quantum-based cosine similarity scores, both demonstrating the effectiveness of the algorithm in tackling multimodal summarization tasks within healthcare. The findings highlight the potential of quantum-inspired genetic algorithms to improve the summarization of multimodal healthcare data, offering a promising solution to the challenges posed by the increasing volume of medical information available online.