Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
People are turning more and more to online symptom checkers and self-medicating, but that’s bringing up some real safety questions. In this paper, we introduce an AI healthcare assistant built with safety at the forefront—it’s designed to guide users on over-the-counter (OTC) medications and first-aid steps based on the symptoms they report. Here’s how it works: the system runs on a hybrid setup that mixes a rule-based engine with a large language model (LLM). This combo lets it deliver answers that make sense for the situation while still staying under tight control. A strict safety filter keeps the advice limited to OTC meds, and if a user’s symptoms look serious, an emergency detection module kicks in and prompts them to seek professional help. Tests show the assistant sticks to safety protocols and does a solid job flagging emergencies. This tool focuses on safe triage and makes basic healthcare advice more accessible, so it’s helpful for those first steps before seeing a doctor.
"AI Healthcare Assistant for Safe OTC Medication Guidance with Emergency Risk Detection", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 4, page no.b859-b864, April-2026, Available :http://www.ijrti.org/papers/IJRTI2604254.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator