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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: July 2026
Volume 11 | Issue 7
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| Paper Title: | Control Natural Language in healthcare |
| Authors Name: | OGOU ESSEHOU GBOKANLE KALIDE MARIUS |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_212109
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| Published Paper Id: | IJRTI2606039 |
| Published In: | Volume 11 Issue 6, June-2026 |
| DOI: | https://doi.org/10.56975/ijrti.v11i6.212109 |
| Abstract: | Abstract Healthcare communication is frequently impeded by the inherent complexity, technicality, and ambiguity of medical language, which often creates a significant barrier between healthcare professionals and patients. The extensive use of specialized terminology and clinical jargon can result in misunderstandings, reduced patient engagement, poor treatment adherence, and an increased risk of medical errors. These challenges are particularly pronounced in diverse healthcare settings where patients may have varying levels of health literacy, linguistic proficiency, or access to medical information. To address these issues, this report presents an Artificial Intelligence (AI)–based Controlled Natural Language (CNL) system designed to facilitate clear, accurate, and bidirectional communication between clinicians and patients. The proposed system enables the translation of clinician-oriented medical terminology into simplified, patient-friendly language while preserving the original clinical meaning. Conversely, it also converts patient-described symptoms, which are often expressed in informal or ambiguous terms, into structured and standardized medical expressions that can be easily interpreted by healthcare professionals. The system integrates advanced Natural Language Processing (NLP) techniques with transformer-based deep learning models to achieve high contextual understanding and semantic accuracy. In addition, speech-to-text and text-to-speech technologies are incorporated to support multimodal interaction, thereby improving accessibility for patients with limited literacy or visual impairments. By enforcing Controlled Natural Language constraints, the system reduces linguistic ambiguity and ensures consistency in medical communication. Experimental evaluation of the proposed solution demonstrates significant improvements in clarity, comprehension, and usability compared to conventional text-based communication approaches. The results indicate that AI-driven language control has strong potential to enhance patient safety, improve healthcare accessibility, and support more inclusive, efficient, and reliable digital healthcare communication systems. |
| Keywords: | NLP, HELTHCARE, MEDTRANS, PATIENT,CLINICIANT |
| Cite Article: | "Control Natural Language in healthcare ", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 6, page no.a380-a387, June-2026, Available :http://www.ijrti.org/papers/IJRTI2606039.pdf |
| Downloads: | 000158 |
| ISSN: |
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 |
| Publication Details: |
Published Paper ID: IJRTI2606039
Registration ID:212109
Published In: Volume 11 Issue 6, June-2026
DOI (Digital Object Identifier): https://doi.org/10.56975/ijrti.v11i6.212109
Page No: a380-a387 Country: Greater Noida, Uttar Pradesh, India Research Area: Computer Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2606039 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2606039 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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