In recent years, we have witnessed great progress and excitement around large-scale language models (LLMs) such as ChatGPT and GPT-4. These cutting-edge models have the potential to transform industries that can be used for drug discovery, clinical trial analysis, improved diagnostics, and personalized patient care, especially in regulated areas such as healthcare and life sciences.
Although these LLMs are promising, they must adhere to certain principles in order to fully integrate into the regulated industry. At John Snow Labs, we have identified three core principles that underlie our approach to integrating LLM into our products and solutions. This blog post explores each of these principles in more detail and provides concrete examples to illustrate their importance.
1. No BS principle
Under the No-BS principle, it is unacceptable for an LLM to hallucinate or produce results without explaining why. This can be dangerous in any industry, but it’s especially important in highly regulated areas such as healthcare, as different professionals hold different standards of what is acceptable.
For example, positive results from a single clinical trial may be sufficient to consider an experimental treatment or an extension trial, but changing the standard of care for all patients with a particular disease is not enough. To avoid misunderstandings and to ensure the safety of all involved, LLMs must provide results supported by valid data and cite sources. This allows human users to review information and make informed decisions.
Additionally, LLMs should strive for methodological transparency and demonstrate how they arrived at certain conclusions. For example, when making a diagnosis, the LLM should provide not only the most probable disease, but also the symptoms and findings that led to that conclusion. This level of explainability helps build trust between users and artificial intelligence (AI) systems, ultimately leading to better outcomes.
2. No sharing principle
Under the No Data Sharing Principle, it is important that organizations do not need to share sensitive data (such as confidential or personal information) in order to use these advanced technologies. Businesses need to be able to run software within their own firewalls, under full control over security and privacy, in compliance with country-specific data residency laws, and without sending data outside their network.
This does not mean that organizations should abandon the benefits of cloud computing. Instead, software can be one-click deployed in public or private clouds, managed, and scaled accordingly. However, deployments can be done within your organization’s own Virtual Private Cloud (VPC) and data never leaves your network. Essentially, the user should be able to enjoy the benefits of her LLM without compromising data or intellectual property.
To illustrate this principle in action, consider a pharmaceutical company that uses LLM to analyze proprietary data on new drug candidates. Businesses must ensure that sensitive information is kept confidential and protected from potential competitors. By deploying LLM within his own VPC, businesses can benefit from AI insights without the risk of exposing valuable data.
3. Principle of no test gaps
Based on the no test gaps principle, it is unacceptable that LLMs are not comprehensively tested with a reproducible test suite prior to deployment. All aspects that affect performance should be tested: accuracy, fairness, robustness, toxicity, representation, bias, truthfulness, freshness, efficiency. This means providers have to prove that their model is safe and effective.
To achieve this, the tests themselves must be published, human-readable, executable using open-source software, and independently verifiable. Metrics are not always perfect, but they should be transparent and usable across a comprehensive risk management framework. Providers should be able to show their customers or regulators the test suites that were used to validate each version of the model.
A practical example of the no-test-gap principle can be found in the development of LLMs for diagnosing medical conditions based on patient symptoms. Healthcare providers should extensively test model accuracy considering various demographic factors, potential biases and rare disease prevalence. Additionally, model robustness should be evaluated to ensure that the model maintains effectiveness in the face of incomplete or noisy data. Finally, you should test the fairness of your model to ensure that it does not discriminate against specific groups or populations.
By making these tests public and verifiable, customers and regulators can be confident in the safety and effectiveness of LLM, while holding providers accountable for model performance.
In summary, three key principles (no BS, no data sharing, no test gaps) should be followed when integrating large language models into regulated industries. Adhering to these principles will help create a world where LLMs are accountable, private and accountable, ultimately ensuring safe and effective use of LLMs in critical areas such as healthcare and life sciences. will be
As the age of AI advances, the road ahead is filled with exciting opportunities and challenges that must be addressed. By maintaining a strong commitment to the principles of accountability, privacy and responsibility, we can ensure that the integration of LLMs into regulated industries is beneficial and safe. This will enable us to harness the power of AI for greater good, while protecting the interests of individuals and organizations alike.
