AI Scribe vs ChatGPT vs Claude for Clinical Notes: How They Work and When to Use Each?

AI Scribe vs ChatGPT vs Claude and Popular LLMs for Clinical Notes: How They Work

Large language models (LLMs) such as GPT‑4 (ChatGPT) and Claude have made it possible to automatically generate clinical notes from text or audio, raising an important question for clinicians: Is a dedicated AI medical scribe necessary, or can a general‑purpose chatbot handle clinical documentation?

This article explains how AI scribes, ChatGPT, Claude, and other LLM‑based tools work for clinical notes, outlines their strengths and limitations, and describes where dedicated platforms like s10.ai fit in the broader ecosystem.

AI medical scribe vs general LLM: what is being compared?

When people search for “AI scribe vs ChatGPT” or “Claude vs AI medical scribe,” they are usually comparing two different categories:

• General‑purpose LLM chat tools such as the public ChatGPT and Claude interfaces
• Purpose‑built AI medical scribes that use LLMs alongside speech recognition, clinical NLP, and EHR integration

AI medical scribes are designed specifically to listen to or ingest clinician–patient interactions and generate structured documentation such as SOAP notes, H&P notes, and discharge summaries, often with built‑in EHR workflows and privacy safeguards. General LLM tools, by contrast, are text‑centric interfaces that can support note drafting and summarization but do not by themselves provide a full documentation system.

How LLMs generate clinical notes from conversations

Most LLM‑based documentation tools, whether general or healthcare‑specific, follow a similar high‑level workflow.

1. Audio capture and transcription
   • Audio from in‑person or virtual visits is captured via microphone, mobile app, or telehealth platform.
   • Automatic speech recognition (ASR) converts the audio into text, ideally with medical vocabulary and speaker separation.
2. Pre‑processing and structuring
   • The transcript is segmented into turns and enriched with entities such as problems, medications, and lab values using NLP models.
   • Units, dates, and drug names may be normalized to improve downstream accuracy.
3. LLM prompt and note generation
   • An LLM such as GPT‑4 or Claude is prompted with instructions (for example, “Generate a SOAP note from this encounter transcript”).
   • The model produces a structured note with sections such as HPI, ROS, Exam, Assessment, and Plan.
4. Human review and editing
   • Clinicians review and adjust the generated note before finalizing it, due to known risks of omissions and hallucinations.
5. Insertion into the EHR
   • In dedicated AI scribe platforms, the finalized note is inserted into the EHR via APIs, standards such as SMART on FHIR, or workflow agents.

This pipeline highlights that the LLM is one component among several; quality, safety, and efficiency depend on how all layers are orchestrated.

Evidence on GPT‑4 and clinical text quality

A 2024 study in the Journal of Medical Internet Research evaluated ChatGPT‑4 for generating SOAP notes from physician–patient encounter transcripts. The model produced reasonably structured notes, but performance varied across cases and sections, with errors ranging from omissions to incorrect statements. The authors concluded that GPT‑4 was promising for assistance but required human oversight.

Another evaluation of GPT‑4’s performance on multilingual medical notes reported approximately 79% agreement with physicians for information extraction, but highlighted inference errors, extraction mistakes, and hallucinations in the remaining cases. These findings suggest that LLMs can add value for documentation but should be embedded in workflows where clinicians remain responsible for verification.

ChatGPT in clinical documentation: uses and boundaries

Potential uses in a controlled setting

In settings where PHI is not involved, the public ChatGPT interface can help with:

• Drafting or rephrasing example notes, educational materials, and patient‑friendly explanations
• Designing documentation templates and checklists
• Prototyping prompts and workflows that may later be implemented inside governed systems

These use cases treat ChatGPT as a drafting and ideation tool, with the understanding that content will be reviewed and edited by clinicians.

Privacy and compliance considerations

The public ChatGPT service is not marketed as HIPAA‑compliant and does not currently offer a Business Associate Agreement (BAA). Experts therefore caution against entering protected health information (PHI) into this interface because of:

• Potential non‑compliance with HIPAA and institutional privacy policies
• Uncertainty about data retention and how information may be used to improve models
• Challenges in demonstrating proper safeguards during audits or investigations

Even partial de‑identification may not be sufficient if combinations of dates, locations, or clinical details could re‑identify a person, so many organizations restrict ChatGPT to non‑PHI use.

Hallucinations and reliability

As with other LLMs, ChatGPT can generate fluent but incorrect content. Commentaries in the clinical literature and health‑IT community stress the importance of human review and warn about automation bias, where users may over‑trust AI‑generated text. For this reason, ChatGPT is generally viewed as an adjunct for non‑critical tasks rather than a standalone clinical documentation solution.

Claude and Claude for Healthcare in documentation workflows

Claude is a family of LLMs developed by Anthropic with an emphasis on safety and long‑context reasoning, which can be helpful for processing lengthy clinical transcripts or complex medical records.

To address healthcare‑specific needs, Anthropic has introduced Claude for Healthcare, a HIPAA‑ready offering that can be deployed in governed environments and used for tasks such as documentation support, summarizing charts, and handling prior authorization narratives. These deployments are typically integrated into custom applications or partner platforms rather than used via consumer chat interfaces.

Early case studies describe Claude being integrated via services such as AWS Bedrock into systems that perform real‑time transcription, summarization, and documentation assistance under healthcare organizations’ security and compliance controls. However, independent head‑to‑head studies comparing Claude and GPT‑4 specifically for clinical documentation remain limited, and both require human validation in practice.

General LLM chat vs dedicated AI medical scribe platforms

From a workflow standpoint, it is helpful to distinguish between general LLM chat tools and AI medical scribe platforms.

Typical characteristics

• General LLM chat tools (e.g., public ChatGPT, consumer Claude):
  • Text‑centric interfaces used via a web or mobile client
  • No native clinical audio capture or EHR connectivity
  • Not intended for handling PHI without additional safeguards
• AI medical scribe platforms:
  • Combine ASR, LLMs, clinical NLP, and EHR integration to generate and insert notes
  • Offer ambient listening or structured intake of visit data
  • Are designed for HIPAA‑aligned deployments with security and governance capabilities

A number of vendors in the market implement AI scribes using one or more LLMs under the hood, together with domain‑specific logic and integration layers.

Regulatory and safety context

The U.S. FDA’s guidance on clinical decision support (CDS) and software functions underscores the need for transparency, human oversight, and the ability for clinicians to independently review and understand recommendations. Although many documentation tools are not themselves CDS under current definitions, similar principles apply:

• Clinicians should be able to see the basis for generated content.
• Automation should not replace clinical judgment.
• Organizations should monitor for systematic errors and address them through quality processes.

These considerations have encouraged health systems to favor platforms that provide audit trails, role‑based access control, and structured review workflows.

Where AI medical scribes like s10.ai fit

AI medical scribes such as s10.ai sit between general LLMs and the EHR, providing additional layers that are important for clinical use.

Common characteristics of this category include:

• Ambient or assisted capture: Recording or listening during visits and converting speech into structured notes using medical ASR and NLP.
• Template‑driven outputs: Generating documentation in standard formats such as SOAP, H&P, progress notes, and discharge summaries, with configuration for different specialties.
• EHR workflow support: Inserting notes into EHRs through integrations, robotic process automation (RPA), or UI‑level agents, often across a broad range of systems.
• Security and compliance: Operating in HIPAA‑aligned deployments with encryption, access control, and support for BAAs, as well as other frameworks like GDPR and SOC2 in some cases.

s10.ai is one example of such a platform, described in public sources as an autonomous AI‑enabled medical scribe clip‑on that works with many EHRs, using a combination of a medical knowledge layer and workflow automation. Like other AI scribes, it leverages AI models but presents them through a healthcare‑specific interface and governance framework rather than exposing a raw LLM chat box to clinicians.

Practical guidance: when to use which approach

Situations where general LLM chat tools can be useful

Subject to local policies and with PHI excluded, general LLM tools may be appropriate for:

• Drafting or refining generic educational content and documentation examples
• Exploring alternative ways of structuring notes for teaching or internal training
• Early experimentation with prompts and workflows that will later be implemented in governed tools

These uses treat the LLM as a brainstorming and drafting aid, not a system of record.

Situations where an AI medical scribe is more appropriate

For workflows involving actual patient encounters, PHI, and EHR documentation, healthcare organizations typically favor dedicated AI medical scribe platforms that:

• Are deployed within HIPAA‑aligned environments and accompanied by BAAs
• Provide ambient capture or assisted documentation features integrated into clinician workflows
• Offer traceability, access control, and auditability of generated content

Within this category, different products—including s10.ai—vary in areas such as supported EHRs, languages, specialization, pricing, and deployment options. Organizations usually evaluate these factors alongside internal risk and compliance requirements when selecting a solution.