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@article{196265,
        author = {Ayush P. Saysikmal and Dr. Sunil R. Gupta and Dr. Gajendra R. Bamnote and Prathamesh M. Tikhade and Om A. Deshmukh and Sameer A. Belsare},
        title = {AI-Powered Symptom-Wise Medical Advisor Using Groq-Accelerated Multimodal Large Language Models},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {3091-3105},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196265},
        abstract = {The rapid evolution of Artificial Intelligence has put new opportunities on enhancing the preliminary system of healthcare assistance with the help of intelligent and interactive systems. Nonetheless, most of the existing AI-based medical advisory systems operate in a detached mode, either written text mode to analyse the symptoms or picture mode to make a diagnosis. The proposed solution to such problems is SYMPWISE, an AI-driven symptom-wise medical advisory system built on Groq-accelerated Large Language Models (LLMs) that facilitates multimodal thinking. The proposed system considers the textual description of the symptoms typed in by the user and the medical images, including a dermatological picture or a medical scan, through a single inference model. The suggested system employs inference that is fast with the help of Groq Language Processing Unit (LPUs), which allow the ability to generate responses in real-time with low computational latency. The proposed system will deploy a privacy-first architecture in order to guarantee user trust and regulatory adherence, end-to-end encryption, secure authentication mechanism, and controlled access to the sensitive medical information. The systematic PDF health reports derived through this system are also created automatically and can be distributed by the medical professionals to the proposed system to obtain further consultation. SYMPWISE is able to give contextual and interpretable medical advice by connecting visual evidence to stories of symptoms unlike the traditional symptom checkers or image analysis programmes. The proposed system is an effective advisory system at the early stage, though it is not a substitute of licensed medical professionals that enhances access to healthcare, particularly in the rural environment. The proposed architecture demonstrates the possibility of secure, scalable and multimodal AI systems to provide real-time assistance in healthcare, which is a significant advancement in AI-assisted medical advisory systems.},
        keywords = {Artificial Intelligence in Healthcare, Large Language Models, Multimodal Medical Analysis, Symptom Based Diagnosis, Data Privacy and Security},
        month = {April},
        }