Large Language Models in Healthcare: A New Frontier

The development from PLMs to LLMs. GPT-3 marks a significant milestone in the transition from PLMs to LLMs, signaling the beginning of a new era. Source

Overview

The recent surge of interest in Artificial Intelligence (AI), especially Large Language Models (LLMs), promises to reshape numerous sectors, including healthcare. A recent study titled "A Survey of Large Language Models for Healthcare" by Kai He et al. offers an expansive overview of the evolving relationship between LLMs and healthcare applications. In the spirit of elucidating their findings and emphasizing the transformative potential of LLMs in medicine, this article distills the study's key insights.

1. The Evolution from PLMs to LLMs

Historically, Pretrained Language Models (PLMs) like BERT and RoBERTa were central in Natural Language Processing (NLP) tasks, from sentiment analysis to machine translation. Yet, the rise of LLMs like OpenAI's GPT-3 and GPT-4 and Google's Med-PaLM 2 signals a shift. These models, larger and more capable, can autonomously function and outperform their predecessors in diverse tasks, including medical exams. Notably, Med-PaLM 2 is the first model to attain an "expert" level on the US Medical Licensing Examination-style questions, boasting an accuracy rate exceeding 85%.

2. The Potential of LLMs in Healthcare

The healthcare realm is witnessing an influx of tailored LLMs such as HuatuoGPT, Med-PaLM 2, and Visual Med-Alpaca. These LLMs are customized to cater to the specific nuances of the medical field. For instance, HuatuoGPT emphasizes proactive questioning for patients rather than mere passive responses. On the other hand, Visual Med-Alpaca integrates with "visual experts" to perform tasks ranging from radiological image interpretation to addressing intricate clinical questions.

3. Technical and Ethical Considerations

The journey from PLMs to LLMs isn't solely about scale but also involves transitions from discriminative AI approaches to generative ones and from model-centric to data-centric methodologies. But as LLMs gain prominence in healthcare, concerns regarding fairness, accountability, transparency, and ethics arise. The deployment of these models in sensitive areas like medicine mandates rigorous consideration of these ethical dimensions.

4. Applications and Limitations

Initial applications of PLMs and LLMs in healthcare targeted foundational tasks, such as medical Named Entity Recognition (NER) and Text Classification (TC). However, with the advancement of models and availability of diverse datasets, more practical applications are emerging, including online medical consultation systems.

5. Towards a Collaborative Future

While the study by Kai He et al. provides a comprehensive analysis of LLMs in healthcare, it also underscores the need for collaboration between computer scientists and medical professionals. For effective integration of LLMs in healthcare, a holistic understanding that combines technological prowess with clinical expertise is paramount.

Conclusion

The realm of healthcare stands on the brink of a revolution, powered by Large Language Models. As we traverse this transformative era, collaboration, understanding, and ethical considerations will be pivotal. The recent study by Kai He and his team serves as a compass, guiding us through the intricacies of LLMs in healthcare. As we look ahead, the harmonization of AI and healthcare promises a future where diagnostics, treatments, and patient care reach unprecedented heights of accuracy and efficiency. Source