Large language models (LLMs) are a type of artificial intelligence (AI) that can be used to generate text, translate languages, produce different kinds of content, and answer people’s questions.
LLMs have great potential to transform many industries, healthcare included. “Potential healthcare applications of LLMs span patient care, research and workflow optimization (clinical and nonclinical) use cases. In the future, appropriately trained LLMs deployed with other automation tooling will be embedded within healthcare applications to enhance, complement, replace or generate new natural language functions.” explained Gartner® in its research report, Quick Answer: What Healthcare Provider CIOs Need to Know About LLM Applications Such as ChatGPT.
In the meantime, trust in LLMs from those both delivering and receiving medical care is critical. “As interest rises and the technology matures, delivering value through adoption of ChatGPT and other solutions leveraging LLMs relies on gaining and maintaining the trust of clinicians and patients alike.” Gartner® said.
Using Large Language Models in Healthcare
LLMs have the potential to revolutionize healthcare by improving patient outcomes, driving improved research, and streamlining outdated processes.
LLMs can be used for a variety of tasks in healthcare, including:
- Improving patient care: LLMs can generate patient summaries, answer clinical questions, and provide decision support to clinicians.
- Supporting research: LLMs can analyze large datasets, identify patterns and trends, and develop new treatments and therapies.
- Optimizing workflows: LLMs automate tasks, such as scheduling appointments and answering patient questions.
However, it is important to be aware of the limitations and risks associated with these models before they are widely adopted. Although LLMs have the potential to revolutionize healthcare, there are limitations and risks to consider. A few areas of focus worth paying close attention to include:
- Privacy: LLMs can be used to generate text that contains sensitive patient information. Therefore, it is important to ensure these models are used in a secure manner and that patient privacy is protected.
- Bias: LLMs are trained on data created by humans. Therefore, they can reflect biases that exist in the real world. It is important to be aware of these biases and to take steps to mitigate them.
- Accuracy: LLMs are not always accurate. Therefore, their outputs should always be reviewed by a human expert before being used in a clinical setting.
- Understanding: It can be difficult to understand how LLMs generate their outputs. This can make it challenging to identify and address potential biases or errors.
- Recency: LLMs are trained on historical data. This means their outputs may not reflect the latest medical knowledge. Therefore, is important to ensure LLMs are updated regularly with the latest information.
Overall, LLMs have the potential to be a valuable tool for healthcare providers. However, it is critical to recognize and acknowledge ongoing limitations and risks associated with these models before they are widely adopted.
How SAI360 Helps Protect Healthcare Data
SAI360 helps healthcare organizations manage compliance risks associated with their data. Its solutions span:
- Data privacy and security: SAI360 helps healthcare organizations protect sensitive data by providing robust data encryption, access controls, and monitoring capabilities
- Compliance management: SAI360 streamlines compliance management by providing a centralized platform for managing regulatory requirements, policies, and procedures
- Risk management: SAI360 enables healthcare organizations to identify and assess risks associated with their data, including potential privacy breaches and cybersecurity threats.
- Incident management: SAI360 provides incident management tools in the event of a data breach or other security incident to help organizations respond quickly and effectively.
By leveraging SAI360’s advanced features, healthcare organizations can proactively identify and address potential compliance risks, ensuring that patient data remains secure and confidential.
Wondering about how ChatGPT and LLMs may affect your organization? Click here to access Gartner® research report: Quick Answer: What Healthcare Provider CIOs Need to Know About LLM Applications Such as ChatGPT.
Gartner, Quick Answer: What Healthcare Provider CIOs Need to Know About LLM Applications Such as ChatGPT, 27 March 2023, Sharon Hakkennes
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