Use Cases: Improving clinician and admin efficiency is the primary focus area for LLMs currently

Healthcare tools address a broad range of use-cases, from pharmaceutical research to patient care and administrative tasks. Healthcare has long relied on Machine Learning to improve medical imaging, treatment prediction, and drug development. LLMs specifically (which are the focus of this article), are driving a new wave of innovation in the space, by optimizing effort-intensive processes that involve unstructured language-oriented tasks.

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LLM adoption is the most prominent in provider tools (tools that support clinicians and administrators in delivering patient care)

LLMs can boost productivity for healthcare providers by handling the bulk of language-related tasks: The healthcare industry is inundated with labour-intensive tasks across stages of clinical interaction, from scheduling to recording patient data to billing processes and follow-ups on treatment adherence. LLMs are particularly well-suited to streamline these processes due to their ability to process, interpret and act on large volumes of text. They automate tasks such as extracting key information from documents, categorizing and summarizing patient data, and even generating coherent narratives from unstructured clinical notes. Moreover, LLMs can streamline communication workflows, such as scheduling and patient engagement, by interpreting and responding to inquiries or instructions. This reduces the administrative burden on healthcare providers, enabling them to dedicate more time and attention to direct patient care.
AI adoption/innovation in certain provider tools is relatively easier (compared to patient tools) due to lower regulatory constraints: Most provider tools using LLMs do not directly interact with patient treatment, rather provide insights, process support to clinicians, thereby requiring relatively less regulatory oversight or FDA approvals. This is a major factor in provider tools emerging as a early adopter of LLMs.

Early adoption of LLMs is being seen in tools for documentation, clinical decision-making, administrative workflows, and patient engagement

Documentation (Medical Scribing, Medical Coding): Doctor-patient consultations create substantial manual work, especially in transcribing and coding these discussions into electronic health records (EHR). LLMs can transform physician notes and interactions into structured EHRs, and collate these notes, lab panels, and imaging to determine the right diagnosis codes. Tools like Deepscribe, Suki, Athelas, Abridge are already being used by providers across the industry, and claim to have reduced documentation time by 75-90%
Clinical Decision-making (Care decision making, research & diagnostic support): Clinical decision-making requires healthcare providers to navigate through extensive medical literature and patient data to derive actionable medical insights for diagnoses and treatments. LLMs streamline this task by processing this information, generating key insights for diagnosis and treatment, and offering real-time updates and symptom-based diagnostic suggestions (of course, physician oversight on these suggestions is key). Notable LLM applications include Navina AI 's creation of comprehensive patient profiles from unstructured data,Glass Health 's AI-enhanced notebook for doctors, and Path AI's use of Generative AI in disease diagnosis via pathology slide analysis.
Admin Workflows (revenue cycle ops, prior authorization): Administrative workflows in healthcare are notoriously paperwork-intensive, often mired in manual data entry, complex documentation, claims processes and reviews. LLMs are capable of automating form filling, streamline billing processes, and manage prior authorizations – reducing the manual effort required. Banjo Health's tool ‘Composer’ employs LLM-powered span recognition to identify clauses in clinical criteria documents and uses Generative AI to convert these into questions for clinical reviewers, accelerating the prior authorization process by up to 95%. Basys.ai is a recent entrant into the prior auth space and uses Gen AI to encode payer policies and guidelines, allowing automatic matching of requests with a policy requirements.
Patient Engagement: Patient engagement involves effort-intensive tasks like data collection, patient triage, communication, treatment adherence and follow-ups, often leading to significant workloads for both patients and administrators. LLMs are capable of reducing this workload for patients and administrators. Notable Health has introduced an AI assistant for scheduling, bill pay and patient navigation. HealthNote has a chatbot which automates patient interactions pre and post appointments.

Data-Driven VC Landscape 2025

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