Generative AI overview

Understanding generative AI today

Generative AI refers to artificial intelligence systems capable of producing human-like content, including text, images, audio, and even software code. These systems have rapidly evolved in recent years, with tools like ChatGPT, Claude, and Gemini becoming household names. In professional contexts, including physiotherapy, these systems can assist with various tasks across clinical practice, education, and research.

How generative AI works

Generative AI works by processing vast amounts of data to recognise patterns and generate new content based on those patterns. Modern systems use neural networks trained on billions of examples from books, websites, and other sources. It's crucial to understand that whilst these systems can produce remarkably human-like results, they don't truly "understand" in the way humans do. They are essentially very sophisticated pattern recognition and text prediction tools that excel at identifying relationships between concepts, and generating coherent responses to increasingly complex questions.

Current capabilities and limitations

Today's generative AI systems can engage in sophisticated conversations, answer questions, summarise lengthy documents, translate languages, and create educational content. They can process information rapidly and maintain context across extended interactions. However, they also have significant limitations: they can produce plausible-sounding but incorrect information, lack real-world experience, and cannot verify the accuracy of their outputs independently.

For physiotherapists and healthcare professionals, generative AI can serve as a powerful assistant, supporting human capabilities across a range of professional contexts. It can help with tasks such as summarising research papers, generating patient education materials, drafting correspondence, or even assisting with clinical reasoning exercises. However, it's essential to remember that the output of these systems should always be critically evaluated by trained professionals and content experts. Content experts are those (not necessarily physiotherapists) who have specialised knowledge and extensive experience in a specific domain.

The broader AI landscape

Whilst this document focuses on generative AI, it's worth pointing out that the field of artificial intelligence encompasses many other technologies and research domains. Computer vision systems can analyse medical images and movement patterns. Predictive AI uses historical data to forecast outcomes, such as patient recovery trajectories or risk of readmission. Natural language processing helps extract insights from clinical notes and research papers. Machine learning algorithms can identify patterns in large datasets, supporting evidence-based practice and personalised treatment approaches. Robotic systems use AI to assist with rehabilitation exercises and gait training. Each of these technologies serves different purposes and requires different considerations for implementation in healthcare settings. And none of them is discussed here.

Conclusion

Generative AI represents a significant technological advancement that is already beginning to shape how healthcare professionals work and learn. As these tools become more sophisticated and accessible, physiotherapists and other healthcare stakeholders will need to develop skills in using them effectively whilst maintaining professional standards and critical thinking. By understanding both its capabilities and limitations, healthcare professionals can harness this technology to enhance patient care, streamline administrative tasks, and support evidence-based practice, whilst ensuring that human judgement remains at the centre of clinical decision-making.