AI in TCM Research & Education: What Practitioners and Students Need to Know

Artificial intelligence is no longer a distant concept for tech companies alone – it is becoming part of everyday clinical practice, research, and education in Traditional Chinese Medicine (TCM). The European TCM Association Research Task Force (ETCMA), recently hosted a special lecture with Dr Aram Akopyan founder and Dean of Academics at EIIHS exploring how AI can support safe, ethical and useful innovation in our field. This blog offers a short overview of the key messages from his talk. You will find the full lecture video embedded at the end.

From Buzzword to Practical Tools

When we say “AI”, we are not talking about a single magic machine. Dr Akopyan breaks it down into several working pieces that already affect TCM research and teaching:

• Machine Learning (ML) – models that recognise patterns in data, for example when screening clinical trials or de-duplicating references.

• Large Language Models (LLMs) – tools that can summarise, draft, rephrase and help structure literature reviews, case reports, teaching materials and more.

• Knowledge Graphs – ways of connecting herbs, compounds, targets and diseases that can inspire new hypotheses or support interactive learning.

• Computer Vision – image-based AI, such as tongue or pulse image analysis, that may support future diagnostic and teaching tools.

Importantly, Dr Akopyan distinguishes between:

• ANI (Artificial Narrow Intelligence) – task-specific tools we use now (for searching, screening, quiz-making, etc.).

• AGI (Artificial General Intelligence) – human-level flexible AI that could one day design full studies or curricula.

• ASI (Artificial Superintelligence) – beyond-human systems that are not relevant to day-to-day TCM practice today, but matter in long-term ethical discussions.

For practitioners and students, the focus is firmly on ANI: very practical, narrow tools that help us work faster and more systematically, while humans stay in charge.

Why AI Matters Now: Research & Real-World Practice

TCM research is facing an explosion of published studies, preprints and clinical data. Manually finding, screening and synthesising this information is becoming unrealistic. AI can help to:

• Map existing evidence on a topic (for example, acupuncture for primary dysmenorrhoea).

• Speed up search, screening, extraction and synthesis using platforms such as Litmaps, Consensus, Rayyan, ASReview and RevMan.

• Check the strength of key claims and citations with tools like scite and structured reference managers.

Dr Akopyan emphasises applied and pragmatic research – research done in real-world clinics, with real patients and comorbidities, measuring outcomes that actually matter to them (pain, function, quality of life, satisfaction, cost). AI can make it easier to:

• Build simple case registries and case series.

• Track patient-reported outcomes (PROMs).

• Turn everyday clinical work into publishable, transparent, practice-based evidence.

The core question becomes:

“What works here, now, for these patients?”

rather than only “Does it work in principle under ideal conditions?”

AI in the TCM Classroom

In education, AI supports the shift towards digital, flexible and lifelong learning. In his lecture, Dr Akopyan shows how carefully chosen tools can help rather than replace teachers:

• Creating reading lists and curated summaries for topics such as Zang-Fu patterns or point functions.

• Supporting social annotation and discussion of key texts so students can highlight questions and confusion in real time.

• Generating formative quizzes, self-tests and OSCE-style scenarios that give immediate feedback.

• Embedding checks and questions into video lectures (for example via Edpuzzle) to keep students engaged.

• Using safe, institution-scoped LLMs (such as QiBo, developed with EIIHS) for role-play, patient simulations, history-taking practice and case discussion.

Here too, the message is clear: AI amplifies good teaching, it does not replace educators.

Ethics, Safety and the EU AI Act

Alongside the opportunities, Dr Akopyan spends significant time on ethical and regulatory foundations. Drawing on the EU AI Act, GDPR and current publishing guidelines, he stresses several practical principles:

• Always keep human oversight of clinical, academic and grading decisions.

• De-identify patient and student data; never paste identifiable clinical information into public AI tools.

• Disclose AI use in manuscripts, presentations, teaching materials and student submissions.

• Check AI outputs for bias, fairness and accuracy, especially where people may be affected (admissions, assessments, clinical triage).

• Avoid banned or high-risk uses such as social scoring or untargeted biometric surveillance.

At EIIHS, these principles guide how we integrate AI into both our research training and our TCM curricula.

Watch the Full Lecture

This lecture is part of EIIHS’ ongoing work to support pragmatic, applied research and responsible use of AI in integrative medicine education.

If you are a practitioner, student, or educator wondering how to begin using AI safely and effectively, we invite you to watch the full presentation by Dr. Aram Akopyan below and reflect on how these tools might support your own practice, research projects and teaching.

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