From LLMs for clinical decision support, to best-in-class medical chatbots, healthcare is paving the way for applied generative AI.
There’s been no shortage of new tools, claims, and ideas about what generative AI can, cannot, and should not do over the past year. And despite the hype, there are only a handful of successful real-world enterprise projects applying the technology. The healthcare industry is the exception, with a breadth of generative AI use cases under its belt.
From using large language models (LLMs) for clinical decision support, patient journey trajectories, and efficient medical documentation, to enabling physicians to build best-in-class medical chatbots, healthcare is making major strides in getting generative AI into production and showing immediate value. So, what can other practitioners take from healthcare’s best practices and lessons learned in applied AI?
Here are 4 lessons from applications of AI in healthcare.
Patient journey trajectories
Many traditional LLMs only consider a patient’s diagnosis and age. But what if that was expanded to several multimodal records, such as demographics, clinical characteristics, vital signs, smoking status, past procedures, medications, and laboratory tests? By unifying these features, a far more comprehensive view of the patient is created, and thus, the potential for a more comprehensive treatment plan.
Additional data can significantly improve model performance for various downstream tasks, like disease progression prediction and subtyping in different diseases. Given the additional features and interpretability, LLMs can then help physicians make more informed decisions about disease trajectories, diagnoses, and risk factors of various diseases. It’s easy to see how this approach could be applied to a customer journey for marketers, or risk assessment for insurance or financial companies—the potential is endless.
Improvements to medical chatbots
Combining structured—electronic health records, prescriptions—and unstructured data—clinical notes, medical images, and PDFs—to create a complete view of a patient is critical. This data can then be used to provide a user-friendly interface, such as a chatbot to gather information about a patient or identify a cohort of patients who can be candidates for a clinical trial, population health, or research efforts. It sounds straightforward, but lest not forget privacy and data restrictions that make this challenging for healthcare and other high-compliance environments.
In order to get the most out of a chatbot and meet regulatory requirements, healthcare users must find solutions that enable them to shift noisy clinical data to a natural language interface that can answer questions automatically. At scale, and with full privacy, to boot. Since this cannot be achieved by simply applying LLM or RAG LLM solutions, it starts with a healthcare-specific data pre-processing pipeline. Other high-compliance industries like law and finance can take a page from healthcare’s book by preparing their data privately, at scale, on commodity hardware, using other models to query it.
Democratizing generative AI
AI is only as useful as the data scientists and IT professionals behind enterprise-grade use cases—until now. No-code solutions are emerging, specifically designed for the most common healthcare use cases. The most notable being, using LLMs to bootstrap task-specific models. Essentially, this enables domain experts to start with a set of prompts and provide feedback to improve accuracy beyond what prompt engineering can provide. The LLMs can then train small, fine-tuned models for that specific task.
This approach gets AI into the hands of domain experts, results in higher-accuracy models than what LLMs can deliver on their own, and can be run cheaply at scale. This is particularly useful for high-compliance enterprises, given no data sharing is required and zero-shot prompts and LLMs can be deployed behind an organization’s firewall. A full range of security controls, including role-based access, data versioning, and full audit trails, can be built in, and make it simple for even novice AI users to keep track of changes, as well as continue to improve models over time.
Addressing challenges and ethical considerations
Ensuring the reliability and explainability of AI-generated outputs is crucial to maintaining patient safety and trust in the healthcare system. Moreover, addressing inherent biases is essential for equitable access to AI-driven healthcare solutions for all patient populations. Collaborative efforts between clinicians, data scientists, ethicists, and regulatory bodies are necessary to establish guidelines for the responsible deployment of AI in healthcare and beyond.
It’s for these reasons The Coalition for Health AI (CHAI) was established. CHAI is a non-profit organization tasked with developing concrete guidelines and criteria for responsibly developing and deploying AI applications in healthcare. Working with the US government and healthcare community, CHAI creates a safe environment to deploy generative AI applications in healthcare, covering specific risks and best practices to consider when building products and systems that are fair, equitable, and unbiased. Groups like CHAI could be replicated in any industry to ensure the safe and effective use of AI.
Healthcare is on the bleeding edge of generative AI, defined by a new era of precision medicine, personalized treatments, and improvements that will lead to better outcomes and quality of life. But this didn’t happen overnight; the integration of generative AI in healthcare has been done thoughtfully, addressing technical challenges, ethical considerations, and regulatory frameworks along the way. Other industries can learn a great deal from healthcare’s commitment to AI-driven innovations that benefit patients and society as a whole.
The above areas will be a focus of this year’s Healthcare NLP Summit, a free, virtual community event being held from April 2-3, highlighting real-world use cases of the technology.
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