Can AI Really Understand Medical Terminology Across Languages? A Deep Dive into Pharmacovigilance
- Sushma Dharani
- Nov 29, 2025
- 4 min read
In today's rapidly globalizing healthcare environment, patient safety data is generated across continents, cultures, and languages. Pharmacovigilance, the science of detecting, assessing, understanding, and preventing adverse effects of medicines, depends heavily on accurate interpretation of medical information. From adverse event reports submitted by physicians in Japan, to patient complaints recorded in regional dialects of India, to regulatory documents from Europe, medical terminology flows continuously across linguistic boundaries.
The central question is: Can artificial intelligence truly understand medical terminology across multiple languages, especially within the complex and high-stakes environment of pharmacovigilance?
This blog explores the challenges, current capabilities, limitations, and future of multilingual AI in medical terminology understanding, and how platforms like Tesserblu are transforming the landscape.
The Language Challenge in Pharmacovigilance
Pharmacovigilance teams collect and analyze massive volumes of information through Individual Case Safety Reports, literature monitoring, call center transcripts, social media, scientific publications, clinical trial data, and regulatory submissions. Much of this data is unstructured, context-dependent, and multilingual.
Some key challenges include:
Medical terminology variation across languages: Medical terms are often not direct translations. For example, a term like myocardial infarction may be referred to as heart attack in general patient language, while in Hindi, it may be described as dil ka daura. The nuance has huge implications for signal detection.
Synonyms and regional dialects: India alone has hundreds of languages and dialects. South India uses terms different from North India for symptoms like dizziness or stomach pain. AI must go beyond literal translation to capture meaning.
Patient-level language differs from clinical language: A patient may say burning sensation instead of gastric reflux. Multilingual AI must interpret such layperson expressions as legitimate safety signals.
Abbreviations and shorthand usage: Medical texts are filled with acronyms like SOB (shortness of breath), which could be misunderstood as a slang expression in general context.
Cultural differences in symptom reporting: People in some cultural groups may describe anxiety as tightness in chest or heaviness in mind, a meaning AI must interpret correctly.
These complexities make pharmacovigilance uniquely dependent on contextual understanding, something traditional translation tools cannot handle.
Can AI Actually Understand Medical Terminology?
Recent breakthroughs in natural language processing (NLP), machine learning, and large language models have significantly improved AI’s capability to understand medical language. Unlike rule-based translation systems that convert word-for-word, modern AI models are trained on vast biomedical datasets and real-world text corpora.
AI systems such as transformer-based models, clinical BERT variations, multilingual medical NLP models, and domain-specific AI engines can now:
Recognize medical terms within unstructured text: Extracting entities like drug names, adverse reactions, indications, and laboratory values automatically.
Interpret context: Differentiating between drug reaction terms like rash and unrelated uses like rash decision.
Cross-language semantic understanding: Mapping meaning rather than literal words, enabling equivalence between heartburn and acidity sensation.
Automate case processing workflows: Cleaning, annotating, coding in MedDRA, and summarizing multi-language reports.
Detect safety signals faster: Continuously learning from global pharmacovigilance databases.
This is a major leap from earlier systems, where AI only translated or extracted keywords without understanding the medical intent.
The Role of MedDRA in Multilingual Terminology Understanding
The Medical Dictionary for Regulatory Activities (MedDRA) is the global standard for coding medical terminology, and it exists in more than 20 languages. AI models trained with MedDRA improve consistency in:
Term coding and hierarchy interpretation
Standardizing terms reported differently in multiple languages
Supporting faster and accurate safety decision making
A key challenge is mapping colloquial expressions into MedDRA compliant terminology. For example:
Giddiness in Indian English translates to dizziness (PT: Dizziness)
Gas problem translates to Abdominal distension
AI models must link expressions to the correct Preferred Term for regulatory submissions. Modern AI engines do this using deep contextual learning.
Limitations of AI in Understanding Medical Terminology
Despite advancements, AI still has limitations:
Dependence on training datasets: If language data is insufficient, accuracy declines.
Difficulty with rare or culturally specific expressions: For example, regional idioms referring to mental stress.
Regulatory constraints; AI output cannot replace medically trained experts.
Interpretation risks: Misunderstanding a symptom description can lead to incorrect medical decisions.
Continuous model updating required: As medical terminology evolves and drug safety events emerge.
Therefore, AI should be used to augment human expertise, not replace it.
Opportunities for AI in Pharmacovigilance
The future of safety monitoring is increasingly data-centric and automation-driven. AI delivers value by:
Reducing manual case processing time
Enabling real-time safety surveillance
Improving literature and social listening coverage
Enhancing MedDRA coding accuracy
Supporting multi-country pharmacovigilance operations
Reducing human cognitive overload
Multilingual AI can transform PV operations by enabling consistent interpretation of reports from global markets, especially emerging regions where safety data is increasing rapidly.
How Tesserblu Can Help
Tesserblu, a next-generation pharmacovigilance automation platform, is designed to solve precisely these challenges. It leverages advanced AI and NLP models trained specifically on medical safety data, enabling scalable and accurate multilingual understanding of safety information.
Key capabilities include:
Multilingual AI processing for safety data from different regions and languages, including patient-level language interpretation.
Automated MedDRA coding with contextual understanding, ensuring consistency between clinical, scientific, and colloquial terms.
Semantic-based term mapping rather than literal translation, improving coding accuracy and minimizing manual correction.
AI-powered literature screening across global and regional language sources.
Efficient intake and case processing using intelligent structured-data extraction.
Support for regulatory-grade safety reporting ensuring compliance and audit readiness.
Tesserblu enables pharmacovigilance teams to improve productivity, reduce risk, and ensure high-quality safety evaluations with faster turnaround times.
The Future: Will AI Achieve Full Medical Language Intelligence?
The future points to increasingly sophisticated models that combine:
Domain-specific foundational medical LLMs
Cultural context learning
Real-time adaptive terminology models
Speech-to-text understanding for patient conversations
Automatic translation into MedDRA and regulatory standards
Integration with global safety databases
Human expertise will always be essential to validate and interpret critical decisions. However, AI will continue to expand its role as a powerful partner that accelerates pharmacovigilance activities.
Conclusion
So, can AI really understand medical terminology across languages in pharmacovigilance? The answer is: yes, significantly more than ever before, and continuously improving. While AI is not perfect, it has evolved far beyond basic translation to meaningful semantic comprehension of medical text. In a world where pharmaceutical companies must manage global safety data rapidly, multilingual AI has become indispensable for efficiency, accuracy, and regulatory compliance.
Platforms like Tesserblu are leading the way, enabling organizations to harness AI for improved MedDRA coding, automated case processing, intelligent literature monitoring, and multilingual understanding. As AI and language models mature, pharmacovigilance will become safer, faster, and more connected globally. Book a meeting if you are interested to discuss more.
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