The rapid development of artificial intelligence (AI) is transforming modern healthcare, including physiotherapy and osteopathy. AI-based systems are increasingly used for clinical decision support, movement analysis, rehabilitation planning, patient monitoring, and educational purposes. Machine learning algorithms, motion-capture technologies, wearable sensors, and large language models such as ChatGPT can generate rehabilitation protocols, analyze biomechanical parameters, and provide recommendations based on current scientific evidence. These advancements raise an important question: can AI realistically replace physiotherapists and osteopaths?

Although AI demonstrates considerable potential in supporting diagnostics, treatment planning, data analysis, and tele-rehabilitation, significant limitations still exist. Current AI systems lack genuine clinical understanding, contextual reasoning, emotional intelligence, and the ability to interpret subtle biopsychosocial factors influencing patient health. Furthermore, osteopathic and physiotherapeutic interventions frequently require direct manual interaction, tactile assessment, and dynamic adaptation during treatment sessions. AI-generated rehabilitation programs may also contain inaccuracies, fail to consider contraindications, or omit appropriate progression criteria.

The available evidence suggests that AI should currently be viewed as an advanced supportive tool rather than a replacement for healthcare professionals. The future of physiotherapy and osteopathy will likely involve hybrid models integrating artificial intelligence with human expertise. In this model, AI may enhance clinical efficiency, improve access to evidence-based recommendations, and support personalized rehabilitation, while the therapist remains responsible for clinical judgment, ethical decision-making, patient communication, and hands-on therapeutic intervention.

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Osteopathic practice has always relied on refined observation: the ability to listen, feel, and perceive subtle patterns linking structure, function, and health. Artificial intelligence (AI) is, in its own way, also an observer—capable of detecting complex patterns in data at scales beyond human capacity. This presentation explores how these two forms of perception can complement each other and how AI may augment, rather than replace, clinical intuition in osteopathic care.

Emerging technologies already make this future imaginable. A microphone in the consultation room could transcribe the clinical encounter, structure documentation, and flag potential red flags or diagnostic differentials in real time. Camera-based systems could analyse posture, symmetry, gait, and movement quality, supporting the practitioner’s visual assessment. Machine-learning models may integrate patient history, lifestyle factors, and longitudinal data to provide pattern recognition that enriches clinical reasoning.

These possibilities also raise fundamental questions: which aspects of osteopathic practice can be safely supported by automation, and which remain inherently human? How can therapeutic rapport, narrative competence, and touch-based communication be preserved in an increasingly digital clinical environment?

As health systems move from reactive treatment toward proactive and personalised care, manual medicine professions may experience renewed demand for practitioners skilled in both hands-on assessment and data-informed decision-making. Osteopathic education will therefore need to evolve, incorporating digital literacy, ethical awareness, and interdisciplinary collaboration.

This presentation argues that the future of osteopathy lies not in choosing between human and artificial intelligence, but in integrating both. When used thoughtfully, AI can enhance perceptual depth, support safer decision-making, and strengthen osteopathy’s commitment to holistic, prevention-oriented care.

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Abstract: Artificial Intelligence in osteopathic education, threat or opportunity?

At this moment, I can only write a brief summary on my topics:

• What’s the influence of a live-present teacher?
• You cannot learn to swim from the book, experience is necessary.
• In our profession, it is learning by doing.
• Osteopathy is for 75% craftsmanship, not only in techniques but also in philosophy.
• We have seen the results in education with the writing: by hand, typewriter, laptop.
• The same with maths: with a slide rule and a calculator.
• A good teacher adjusts his education to the group.
• There is a major threat of automorphism; treating Google or AI as human.
• Image Martin Luther King addressed the crowd with AI: ‘I have an artificial dream’.
• Osteopathy is learning by using all senses: hearing, visual, touching, but even smelling en sensing the gut feeling. You will always need teachers as an example.
• What happens with the group dynamics, if we allow AI tot take over a part?
• AI does not have a ‘view of live’ nor a ‘worldview’ That’s part of its design, gladly enough.
• AI can only use the Internet; a small part of our worldwide knowledge is shared on the Internet. Also, there is a lot of crap on the internet. Of course it is a question of ‘how to?’. But it will lead to a self-fulfilling prophecy. The same happened with Wikipedia and even Google Maps.
• I do not deny there is a good possibility for the administration and other parts. But writing a thesis by AI and letting it assess by AI, does not bring the student to the level we want to achieve.

So far my major topics. It will be more. We have one year left in the development of or the implementation of AI in Education.

I am therefore an opponent and would like to warn the implementation of AI in osteopathic Education.

Osteopathy is, par excellence, a profession in craftsmanship and in the context to complementary healthcare, there lies a great opportunity, when we stay mainly in use of humans in stead of AI.

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Educators are currently wrestling with the challenges and opportunities presented through developments in the use of artificial intelligence.

As the statutory regulator for osteopaths in the UK, the General Osteopathic Council wanted to ensure a proportionate response to the rapid advancements in AI, supporting educators and students while also providing clarity around regulatory requirements.

The regulatory approach to AI in osteopathic education is grounded in education and training standards as well as regulatory principles on AI use set by the UK government. The General Osteopathic Council worked with other regulators on a joint approach, providing clarity and consistency for education providers across different regulatory contexts.

A principles-based approach was deliberately chosen to ensure the framework remains relevant despite rapid technological developments, while also leaving enough space for education providers to make use of the opportunities AI brings.

The presentation will provide a model for best practice on how regulators can address the challenges of regulating the use of AI in osteopathic education while ensuring space for innovation. It will share the benefits of this approach, lessons learned from developing and implementing the framework, and insights into how regulators and educators can work together when implementing a principles-based model.

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Artificial intelligence (AI) tools are now embedded across the research lifecycle, from literature searching and analysis to writing and dissemination. In osteopathic and allied health education institutions, AI use by students, clinicians and researchers raises significant questions about research integrity, participant protection and institutional governance. Although UK and international guidance is expanding, most health sciences research ethics frameworks are not yet designed to systematically identify and assess AI-related risks, creating a widening governance gap.

This paper draws on a position paper produced by the Institutional Research Ethics Committee (IREC) at Health Sciences University (HSU), a specialist UK institution with research activity in osteopathy, musculoskeletal health, manual therapy and evidence synthesis, where generative AI tools are increasingly used. In response to proposals for an institutional AI-in-research policy, IREC developed and endorsed a tiered risk stratification framework to support proportionate ethics review of AI-assisted research.

The framework assesses AI-related risk across four dimensions: the nature of AI use, the sensitivity of data, the institutional governance status of the AI tool, and the vulnerability of the research population. Three tiers — lower, moderate and higher — map to escalating review requirements, from declaration-only fast-track routes to full committee review with data protection impact assessment consideration. The paper offers a transferable model for credible, risk-proportionate AI governance in research ethics.

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Clinical history-taking is a core competency in osteopathic education, yet it remains particularly challenging for students during their first real patient consultations. Despite prior training, students frequently adopt a rigid, checklist-driven approach, prioritizing data capture and documentation over patient-centered interaction.

This presentation reports on an educational innovation involving the implementation of an artificial intelligence-based clinical documentation tool in an osteopathic teaching clinic. Developed in partnership with Tandem Health, the software records and transcribes the consultation, structures clinically relevant information within the patient record, and captures key elements of therapeutic education and shared decision-making.

By relieving students of real-time note-taking and screen interaction, the tool aims to support a conversational interview style, fostering motivational interviewing skills and facilitating access to psychosocial dimensions of the patient narrative.

The presentation will report results from pilot implementation and wider use, focusing on perceived changes in relational presence, interview quality, narrative coherence and integration of therapeutic education, while also critically examining possible risks such as dependency on AI-assisted systems and reduced clinical autonomy.

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The integration of artificial intelligence (AI) in higher education is transforming learning processes and competency assessment across disciplines. In osteopathy, this evolution is raising concerns about the development of clinical reasoning.

Within the graduate osteopathy microprograms at Université du Québec à Montréal, an issue has emerged regarding students’ ability to critically and efficiently use AI in alignment with institutional expectations. Shared observations among lecturers and professors suggest that students often have difficulty using AI as a cognitive and critical tool.

While AI can facilitate access to information, its use often remains superficial for a significant proportion of learners. Students show limited ability to validate information, identify biases and integrate AI-generated content into coherent clinical reasoning.

In response, the microprograms emphasize active learning strategies including complex clinical case presentations, critical appraisal of scientific literature and simulated patient interactions. These approaches aim to position AI as a support for deeper cognitive processes rather than a substitute for reasoning.

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The increasing availability of generative AI in clinical learning environments presents both opportunities and risks for osteopathic education. While higher education students frequently engage with AI as a knowledge source, inadequate skills in evaluating reliability, contextual relevance and clinical safety may pose risks to future patient care.

At the presenter’s institution, illness scripts are used as a learning task to consolidate pathophysiological knowledge and support clinical reasoning. Over the last academic year, students were asked to include AI as one of the referenced sources when producing their scripts.

An educational evaluation explored how students engaged with AI during illness script construction. A questionnaire based on the SIFT critical appraisal framework was used to assess how students questioned, verified and contextualised AI-generated outputs.

The evaluation suggests that embedding AI within illness script tasks, with expectations for critique, can promote reflective and evidence-aware engagement with AI in pre-registration clinical education.

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Well-researched articles, built on months of careful study and supported by meticulous documentation, too often reach only a limited audience — or none at all. Yet those same high-quality works can serve as a powerful foundation. With the help of AI, they can be reimagined, bringing overlooked ideas out of obscurity and giving them new relevance and life.

Reframed in accessible formats such as an interview or conversation, these pieces can engage a broader audience beyond the academic sphere in an entertaining manner.

The presenter approached AI with skepticism, but in just one month observed two lesser-read articles transformed into engaging, conversational podcast episodes. These AI-driven adaptations preserved the rigor and factual integrity of the originals while reaching a significantly wider audience.

This presentation shows how professionals can use AI to expand the reach and impact of their ideas through thoughtful reimagining.

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Osteopaths often face barriers to engaging with current research due to time constraints, limited access and variable confidence with digital tools. Artificial intelligence offers the potential to make research evidence more accessible and to strengthen both evidence-informed practice and digital capability within the profession.

A custom GPT model was created to generate concise, contextually appropriate summaries of osteopathy-related research papers. The custom instructions, a sample paper and its AI-generated summary were shared with members of the National Council for Osteopathic Research’s Practice-Based Research Network.

A complementary guide was published on the NCOR Research Skills Training Facebook page, including guidance on prompt engineering, instructions for creating a custom GPT research summariser, demonstrations using Notebook LM and links to resources on AI in teaching and workplace proficiency.

Providing structured, responsible guidance on AI applications may empower osteopaths to integrate research more effectively and build digital capability for evidence-informed practice.

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Artificial Intelligence (AI) is increasingly being incorporated into healthcare systems, primarily as a tool for data processing, diagnostic assistance, and predictive analytics. However, these applications often remain limited when confronted with the multidimensional and adaptive nature of osteopathic clinical reasoning.

Osteopathic medicine involves the interpretation of dynamic interactions between structure, function, fluid dynamics, neurofascial regulation, and self-regulatory biological mechanisms that frequently transcend linear biomedical models.

This work proposes a bio-informational framework in which AI functions not merely as a technological assistant, but as an extension of osteopathic reasoning capable of supporting the interpretation of complex systemic interactions within the human organism.

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This study explores the potential of artificial intelligence (AI), specifically ChatGPT-4, as a clinical interview assistant in osteopathic practice. Its objective is to determine whether AI can effectively support osteopaths and students in the formulation of diagnostic hypotheses during the anamnestic phase, while preserving the practitioner’s clinical autonomy.

Conducted as a simulated experimental study, the project compares two conditions: a standard anamnesis conducted alone, and a second carried out with the support of AI. Each participant — students in their 3rd, 4th, or 5th year of study, and practicing osteopaths — is asked to perform two clinical interviews on fictional cases.

The first is completed independently, the second with access to ChatGPT-4 through a standardized prompt designed to reflect real osteopathic training. Both cases are evaluated using a structured performance grid and self-assessment questionnaires.

The study quantitatively measures differences in diagnostic hypothesis quality, question relevance, and perceived case difficulty. It also analyzes whether AI assistance improves clinical reasoning efficiency or introduces bias or dependency. Results aim to contribute to the reflection on AI integration in clinical reasoning pedagogy, highlighting opportunities and limitations of human–machine collaboration in osteopathic education.

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OsteoAI is a purpose-built AI clinical assistant for osteopaths, manual therapists, and students. Built on a large language model with a curated osteopathic knowledge base using Retrieval-Augmented Generation (RAG), the system integrates five clinical modes: Clinical Reasoning, Differential Diagnosis, Treatment Planning, Case Simulation, and General Query.

Key features include a tissue-by-tissue differential diagnosis framework with likelihood scoring, structured clinical reasoning with collapsible sections, virtual patient case simulation with five-category performance scoring, bilingual support in English and Finnish, and a user contribution system allowing practitioners to submit anonymised clinical cases for knowledge base integration.

OsteoAI supports clinical reasoning development through systematic tissue analysis, red flag screening, and viscerosomatic connection identification, mirroring reasoning processes taught in osteopathic curricula. The Case Simulation mode provides a low-stakes environment for practising clinical decision-making with structured feedback.

The platform deliberately positions AI as a reasoning support tool rather than a decision-maker, fostering critical appraisal as an inherent part of its use. OsteoAI demonstrates that profession-specific AI tools can meaningfully support osteopathic clinical reasoning and education, while raising important questions about AI integration into curricula. Even advanced AI can hallucinate, making clinical judgment irreplaceable.

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European osteopathy remains one of the few healthcare disciplines without a shared nomenclature for its clinical observations. In Maîtriser l’Examen Clinique en ostéopathie, P. Gadet proposes to mobilise the International Classification of Diseases, 11th revision (ICD-11), in force since 2022, to standardise somatic dysfunctions by region and to feed an inter-practitioner database for research and economic-evaluation purposes.

In parallel, the launch of Anthropic’s ICD-10 connector on 11 January 2026 and the emergence of the Model Context Protocol (MCP) as an open standard open a new technical avenue for operationalising this ambition.

This perspective communication confronts P. Gadet’s proposal with the current ecosystem of MCP connectors in healthcare, recent peer-reviewed literature on AI in osteopathy and the European regulatory framework, including GDPR, the EU AI Act and MDR.

Anthropic’s connector is centred on US billing and covers neither ICD-11 nor European variants. No official ICD-11 connector exists to date. Building an independent European connector, designated here as OsteoMCP-ICD, appears technically feasible and strategically timely.

The project opens concrete perspectives for observational research, interprofessional communication and teaching, provided that the ICD-11 nomenclature is explicitly articulated with the specific grammar of osteopathic reasoning. The 10th OsEAN Open Forum constitutes a strategic window to engage the European profession in this direction.

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This poster presents the use of artificial intelligence in brainstorming as a teaching technology in practical clinical classes at the V. L. Andrianov Institute of Osteopathic Medicine.

Brainstorming in osteopathic education is described as a psychological and pedagogical technique for working with an audience using the resources of collective thinking. As a result of this work, a new intellectual product is created, for example a clinical task modelled or recreated by participants from their osteopathic practice experience.

Brainstorming is used in clinically focused practical classes for senior students, with the aim of developing students’ osteopathic clinical thinking. This form of clinical thinking enables future osteopaths to conduct osteopathic diagnostics and choose subsequent treatment for a patient.

As a teaching technology, brainstorming has a component structure consisting of a goal, content, methods of working with students, and a result expressed in the modelling or recreation of clinical osteopathic tasks and objectives. Its use is guided by teaching principles based on osteopathic philosophy and osteopathic treatment.

These principles include understanding the significance of the patient’s age, psychophysiological and social status; active participation in collective discussion of clinical problems and tasks; attention to psychosocial and functional characteristics of the patient’s body; and the unity of clinical osteopathic thinking, the diagnostic process and the choice of treatment.

Artificial intelligence can be used by the instructor as a resource in this process, for example in modelling clinical problems. However, the effective use of AI depends on the professional formulation of objectives by a teacher with relevant competencies and knowledge in osteopathy.

A critical examination of AI-generated solutions reveals important limitations, including a lack of a global systemic view of the patient and proposed diagnostic or treatment elements that may be inconsistent with osteopathic examination findings. The use of AI-generated clinical problems in student training is therefore only possible when they are critically analysed by a competent instructor.

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