Healthcare has traditionally lagged behind other industries in adopting cutting-edge technology, but the rise of generative AI (GenAI) and large language models (LLMs) has sparked a wave of interest among clinicians and healthcare providers (HCPs).
While tools like natural language processing (NLP) and ‘traditional’ machine learning have already proven valuable in areas like medical dictation and radiology, LLMs – especially multimodal models – are creating a new level of excitement. With intuitive interfaces, the ability to handle vast and complex unstructured data and impressive versatility, these models offer potential that goes beyond established technologies, although concerns about accuracy and ‘hallucinations’ remain. Interest is growing among clinicians; in fact, recent surveys show that about 20-30% of physicians in the UK and US are already using some form of GenAI at least once a week. This article highlights the clinical use cases where GenAI is gaining the most traction, from in-person care to digital patient interactions.
The emergence of GenAI in healthcare
Before the arrival of more recent LLM-based AI solutions such as ChatGPT, healthcare providers and clinicians primarily used natural language processing and machine/deep learning tools. For example, tools like Nuance Dragon Speech Recognition support dictation, note summarisation and structured data capture.
Other established machine/deep learning tools are aimed at specific areas of clinical decision-making (eg, RapidAI for stroke care, Blackford for radiology, and Ada for primary care and rare diseases) and population health applications (eg, identifying patients suitable for prevention/trials/specific treatments).
LLMs such as those from OpenAI and others offer significantly broader potential and use case applicability, at least in theory, thanks to their improved user interface, text-based context understanding and ability to deal with large and unstructured data sets. On the other hand, accuracy and hallucination concerns remain high despite improvements in the quality of models used, whether via specialisation or overall architecture (eg, Retrieval Augmented Generation, which adds specific knowledge to the AI model; or output constraints).
GenAI vs previous machine/deep learning approaches
What are the specific differences between these AI approaches and do these really matter?
GenAI utilises LLMs and so-called transformer models to generate human-like text and responses. It excels in language understanding and generation tasks, making it suitable for conversational agents and content creation. In healthcare, language capabilities are very important, as information is often conveyed in text form, eg, in spoken dialogue between the patient and physician, and stored in unstructured medical notes in electronic medical records. The most prominent example is OpenAI’s ChatGPT, which (as we noted above) is already heavily used in healthcare settings.
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