Large language models, such as ChatGPT, can replicate human language in a convincing and human-like way, thanks to their deep learning capabilities. They are already widely used in content marketing, customer services, and various business applications. As per Nature Medicine, one of the essential benefits of language models in healthcare is their ability to improve communication between patients and healthcare workers.
Effective communication is crucial for building strong relationships, which, in turn, can improve patient outcomes in a wide range of conditions. Language models could assist patients in communicating with healthcare professionals by providing accessible language and reducing the chances of miscommunication. This could improve compliance with medical prescriptions and enhance patient satisfaction.
Moreover, language models like ChatGPT could facilitate health interventions relying on non-professional peers’ communication. A language model trained to rewrite the text in a more empathic way has already shown positive results in peer-to-peer mental health support systems, highlighting the potential of using human-artificial intelligence collaboration to improve a range of community-based health tasks that rely on peer- or self-administered therapy.
In addition, ChatGPT and other language models could play an important role in personalized medicine approaches, especially for patients with language impairments or neurodegenerative conditions. Patients with neurodegenerative conditions may lose their ability to communicate through spoken language, worsening their social isolation and accelerating the degenerative process. Language models could help these patients expand their vocabulary or comprehend information more efficiently by complementing language with other media or reducing the complexity of the input they receive.
However, the use of language models in healthcare also poses significant challenges. The majority of healthcare organizations do not currently use DL-based language models. Before they can meet acceptable clinical performance and repeatability requirements, DL-based language models will need considerable training on expert annotations for specific clinical applications. Despite early attempts to use these models in clinical diagnoses without further training, the performance of algorithms has been shown to be inferior to that of practicing physicians.
As individuals rely on ChatGPT and other complicated conversational models for medical guidance, ethical and safety concerns grow. Some people may attempt a chatbot instead of contacting a human doctor to figure out what’s wrong with them or what therapy they require. This could lead to potentially dangerous uses, such as bypassing expert medical advice. Therefore, it is essential to put safeguards in place to protect against such risks, such as an automated warning triggered by queries about medical advice or terms to remind users that the model outputs do not constitute or replace expert clinical consultation.
In conclusion, while the potential of language models in healthcare is significant, it is essential to approach their implementation with caution and care. A constructive and alert regulatory environment, with all stakeholders involved and engaged, is critical to ensure that these models are developed, trained, and evaluated for specific clinical tasks effectively and safely. If developed and deployed responsibly and ethically, DL-based language models could have a transformative impact on healthcare, augmenting and improving the quality of care and patients’ lives.
