Large Language Model Policy and Practice

Thoughts and a light framework around Generative AI in an Academic Medical Center

1 Overview

Large language models, like ChatGPT, have garnered significant interest due to their human-like language generation and immense natural language processing capabilities. These models offer opportunities to revolutionize healthcare by enhancing clinical decision-making, patient care, and medical research. However, implementing them also poses technical, ethical, legal, and social challenges.

1.1 Technical Challenges and Opportunities

Developing and implementing large language models entail considerable computational power for training and inference. These models demand extensive data and computational resources, but recent advancements in deep learning frameworks and cloud computing have facilitated their large-scale deployment.

Bias in language models is another technical challenge. Models trained on biased data can produce biased outcomes, potentially leading to incorrect clinical decisions or reinforcing health disparities. Researchers have proposed various techniques to mitigate bias, such as data augmentation, adversarial training, and fairness constraints.

1.2 Ethical Challenges and Opportunities

Implementing large language models in healthcare raises ethical concerns like patient privacy, informed consent, and fairness. Models require vast amounts of data, including personal health information, which can compromise patient privacy and data protection. Patients may also be unaware of how their data is used or may not have provided informed consent.

Conversely, large language models present ethical opportunities. They can generate natural language explanations for clinical decisions, improving transparency and trust between patients and providers. Furthermore, these models can identify and address health disparities by analyzing large-scale data and developing targeted interventions.

1.4 Social Challenges and Opportunities

Social challenges involve potential job displacement and exacerbation of healthcare disparities. Language models could automate healthcare jobs and, if biased, reinforce existing disparities, particularly in marginalized communities.

However, social opportunities also emerge, such as improving healthcare accessibility for at-risk or disadvantaged populations and enhancing healthcare service quality through personalized treatment recommendations and identifying areas for improvement in healthcare delivery or clinical operations.

1.5 Use in Medical Education

Large language models can also play a significant role in medical education settings. They can assist in developing personalized learning pathways, providing instant feedback on complex clinical scenarios, and facilitating access to a wealth of medical knowledge. By incorporating these models into medical curricula, educators can enhance the learning experience and better prepare future healthcare professionals.

2 Conclusion

Implementing large language models in healthcare presents various challenges and opportunities. With careful consideration and mitigation of these challenges, these models have the potential to transform healthcare delivery and improve patient outcomes. It is crucial for healthcare organizations to weigh the risks and benefits of implementation and prioritize ethical and responsible use.

As healthcare providers increasingly depend on large language models, ensuring transparency, explainability, and unbiased models is critical. Researchers and developers must collaborate with healthcare providers and patients to align language model development and implementation with ethical principles and patient needs.

In summary, the use large language models in healthcare is a complex and rapidly evolving landscape. By addressing the technical, ethical, legal, and social implications, healthcare organizations can harness their full potential to enhance patient outcomes, medical research, business operations, and education.