The potential of large language models (LLMs) in revolutionizing healthcare is undeniable, but concerns about transparency and control loom large.
The release of OpenAI’s ChatGPT in November 2022 has sparked excitement in the healthcare industry, with the potential for LLMs like GPT-4 and Google’s Med-PaLM to transform the way healthcare is delivered. These advanced language models offer possibilities such as generating clinical notes, assisting with diagnoses, and streamlining administrative tasks. However, as healthcare institutions rush to adopt these off-the-shelf models, there is a growing concern about ceding control to corporate interests and the potential risks to patient care, privacy, and safety.
The Rush to Deploy Proprietary LLMs
Healthcare institutions and technology companies are eager to integrate LLMs into healthcare systems. Microsoft has begun discussions with Epic, a major provider of electronic health records software, to explore integration options. Google has also announced partnerships with healthcare organizations like the Mayo Clinic. Amazon Web Services has launched HealthScribe, an AI clinical documentation service. Despite the enthusiasm, there is a risk of relying on proprietary LLMs that lack transparency and can be modified or discontinued without notice, compromising patient care.
The Power of Collaboration
Healthcare systems possess a valuable asset: vast repositories of clinical data. By collaborating with technology companies, academic researchers, and other stakeholders, healthcare institutions can develop open-source LLMs tailored specifically for healthcare needs. These models can be transparently evaluated and fine-tuned to incorporate locally held data, ensuring privacy compliance. Initiatives by organizations like the US Department of Health and Human Services and the UK National Health Service demonstrate a commitment to safely implementing AI in healthcare.
Promise and Pitfalls of LLMs
LLMs have shown impressive capabilities in the medical domain, passing medical tests and generating clinical notes preferred by clinicians. However, challenges arise concerning the generation of false outputs, potential data leaks, and exacerbation of biases. Evaluating the safety and accuracy of LLMs remains a complex task, as their performance on question-answering tasks may not reflect real-world usefulness. These challenges must be addressed to ensure the safe deployment of LLMs in healthcare settings.
The Need for Transparency and Accountability
The closed nature of proprietary LLMs raises concerns about accountability and transparency. Users often lack knowledge about the exact model or method used, the training data, and potential modifications. OpenAI’s commitment to making versions of its LLMs available for three months is a step towards transparency, but other providers’ practices remain unclear. The bankruptcy of Babylon Health highlights the risks of relying on profit-driven companies for critical healthcare services.
Conclusion:
To ensure the responsible integration of LLMs into healthcare, a transparent and inclusive approach is needed. Collaboration between healthcare institutions, researchers, clinicians, patients, and technology companies can lead to the development of open-source LLMs tailored for healthcare. By pooling resources and expertise, these stakeholders can address challenges related to privacy, equity, and safety. An open consortium-led approach would promote reliability, robustness, and collective evaluation of LLMs, ultimately enhancing patient care and maintaining accountability in the field of medicine.
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