Figure 2. Chart-constrained LLM recommendations
progress notes, laboratory values and radiological reports to assist clinicians in information synthesis.
Moreover, there may be a future in which LLM capabilities expand to include clinical management suggestions. We demonstrate one example use case in the setting of a common IR consult for acute gastrointestinal hemorrhage (Figure 2), wherein each additional note and lab value updates the LLM recommendations to a care team. Finally, LLMs could also support the advocacy of healthcare professionals and patients alike, through assistance in drafting appeals for denied preauthorization requests or contesting insurance claims deemed medically necessary.
Postprocedure care AI has previously been used, though not widely clinically implemented, for surveillance of complications. Similar deployment of LLM chatbots would be feasible; however, LLMs can state and perpetuate medical inaccuracies.10 As such, LLM-based postprocedure chatbots may still be far from clinical use. However, plug-in tools are currently being developed to restrict LLM information access to a specific source(s), which may improve chatbot accuracy.
LLM integration into electronic medical health records may become a reality with the recent announcement of co-innovation between OpenAI and Epic Healthcare Systems.
Ease of communication A common cause of stress for patients is their inability to interpret radiological reports. Patients are notified when their radiological report is uploaded to their chart even before their physician has had the chance to follow up and elaborate on the findings. LLMs show great utility in this space, with the ability to simplify the radiologist’s medical jargon to a patient-friendly report. LLMs can effectively translate radiological reports to that of an 8th-grade reading level or below.11
specialty-specific radiological report summarizations for referring providers.
Limitations Data privacy
Although OpenAI has taken steps to promote data privacy and website security, they do not follow HIPAA- compliant practices.12
OpenAI’s current
privacy policy admits collecting uncensored personal data submitted to GPT services and using the data to train its LLMs. In its current state, GPT is not ready to be integrated into hospital systems and poses a threat to personal health information. However, OpenAI is compliant with the California Consumer Privacy Act and EU’s General Data Protection Regulations demonstrating their commitment to data security and hopefully integrating future HIPAA- compliant practices.13
Given
the accessibility of medical documents in patient charts, LLM simplification of radiology reports potentially empowers patients to understand their conditions better, reduces patient anxiety and decreases unscheduled calls from patients. Similarly, LLMs can generate
GPT hallucinations A current shortcoming of GPT is the production of hallucinations. Hallucinations are GPT-produced responses that seem entirely reasonable but are fabricated—a limitation of being trained on a dataset that runs up until September 2021. GPT does not actually know what is right and wrong; it simply relays information that it was trained on. Fortunately, LLM frameworks such as LangChain
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