From Carb Counting to Diagnosis: Real World Patient Uses and Attitudes Toward Large Language Models in Diabetes Management

This paper investigates how patients with diabetes currently utilize large language models (LLMs) for diverse self-management tasks, revealing that these tools serve as multifaceted aids for interpretation, decision-making, and emotional support while highlighting the need for safer integration into clinical ecosystems.

The Gist

Imagine living with diabetes is like trying to pilot a very complex, high-tech spaceship. You have to constantly check your fuel gauges (blood sugar), adjust your engines (insulin), plan your route (diet), and make sure the ship doesn't crash, all while the rules of space travel keep changing. It's exhausting, confusing, and sometimes you feel like you're flying blind.

This paper is a report card on how people are trying to use a new, super-smart co-pilot called Large Language Models (LLMs)—like ChatGPT, Gemini, or Copilot—to help them fly their diabetes spaceship.

Here is the breakdown of what the researchers found, explained simply:

1. The Problem: The Pilot is Overworked

Managing diabetes is a full-time job. Patients have to count carbs, check numbers, take meds, and worry about the future. They often feel overwhelmed and don't always have a doctor available 24/7 to answer questions like, "Can I eat this?" or "Why is my sugar high right now?"

2. The New Tool: The "Super-Intern"

Enter the LLM. Think of an LLM as a super-intelligent, 24/7 intern who has read every medical book, website, and diet plan in existence. It can chat with you, answer questions instantly, and look at your data.

The researchers went into online communities (like Reddit) where diabetes patients hang out and asked: "How are you using this new intern?" They looked at nearly 3,000 posts and comments to see what was actually happening in the real world.

3. What Patients Are Doing with Their "Intern"

The study found that patients are using these AI tools for nine different jobs, ranging from "light chores" to "heavy lifting."

• The Meal Planner (Most Popular): Just like asking a friend for dinner ideas, patients ask the AI, "What can I eat that won't spike my sugar?" or "Help me count the carbs in this pizza."
• The Data Detective: Patients upload charts of their blood sugar levels and ask the AI, "Why did my sugar go up last night?" The AI acts like a detective, looking for patterns in the numbers that a human might miss.
• The Translator: Medical jargon is like a foreign language. Patients use the AI to translate complex lab results (like "HbA1c") into plain English.
• The Emotional Cheerleader: Sometimes, patients just need to vent. They chat with the AI to feel less alone or to get a joke to lighten the mood.
• The Troubleshooter: If a medical device (like a continuous glucose monitor) starts beeping annoyingly, patients ask the AI, "Is this broken? How do I fix it?"
• The Second Opinion: Some patients are even asking the AI, "Based on my symptoms, do I have diabetes?" or "Is my doctor right about my diagnosis?"

4. The Good, The Bad, and The Dangerous

The researchers found that while the "intern" is helpful, it's not perfect.

• The Good: It's fast, it's always awake, and it makes patients feel less alone. It helps them understand their disease better and reduces the mental load of managing it.
• The Bad: The intern sometimes hallucinates. This means it confidently makes things up. One user asked for diet advice, and the AI suggested eating 6 eggs a day, which was the opposite of what their nutritionist said.
• The Dangerous: This is the big worry. Some patients are letting the AI make life-or-death decisions. They are asking the AI, "How much insulin should I take right now?" and then actually doing it.
  • The Analogy: Imagine asking your super-smart intern to adjust the nuclear reactor on your spaceship. If the intern gets the math wrong, the ship explodes. The AI is not a doctor; it doesn't have the legal license or the human judgment to make those calls.

5. The Big Lesson: Don't Fire the Pilot, Hire a Better Co-Pilot

The paper concludes that we can't stop patients from using these tools; they are too useful and too accessible. Instead, we need to figure out how to use them safely.

• For Patients: Treat the AI like a library, not a doctor. Use it to learn, to brainstorm, and to organize your data, but always double-check the big decisions with a real human professional.
• For Doctors: Don't ban the tool. Instead, ask your patients, "Are you using AI? What is it telling you?" and help them understand the difference between a helpful suggestion and a dangerous order.
• For Tech Companies: We need to build "guardrails." The AI should be programmed to say, "I am not a doctor. Please check with your healthcare team before changing your insulin."

The Bottom Line

Patients are already using AI to manage their diabetes, and they are doing it in creative, sometimes risky, ways. The goal isn't to stop them, but to make sure the "super-intern" is supervised by a human pilot so that the spaceship stays safe and on course.

Technical Summary

1. Problem Statement

Diabetes management is a cognitively demanding, time-intensive process requiring continuous glucose monitoring, dietary regulation, and lifestyle adaptation. While digital health tools (apps, wearables) are prevalent, there is a significant gap in empirical understanding regarding how patients utilize Large Language Models (LLMs) for diabetes care.

• The Gap: Existing research focuses on system performance metrics (accuracy, readability) of specialized chatbots, rather than organic, real-world patient behaviors with general-purpose LLMs (e.g., ChatGPT, Gemini).
• The Risk: Patients are increasingly delegating complex medical tasks (interpretation, dosing, diagnosis) to unregulated AI tools without clinical oversight, raising concerns about safety, data privacy, and the reliability of medical advice.

2. Methodology

The study employed a mixed-methods approach combining qualitative thematic analysis with LLM-assisted computational analysis to examine patient interactions on social media.

• Data Source: Publicly available posts and comments from Reddit diabetes-focused communities (e.g., r/diabetes, r/Type1Diabetes, r/prediabetes).
• Data Collection:
  • Timeframe: January 1, 2023 – January 29, 2026 (capturing the post-ChatGPT adoption era).
  • Keywords: LLM, GPT, Gemini, Grok, Copilot, AI, Large Language Model, ChatGPT.
  • Dataset: 852 original posts and 1,848 comments (Total: 2,700 text entries).
• Analysis Pipeline:
  1. Qualitative Coding: Three researchers independently coded 100 posts to develop a codebook for "Use Cases" and "Attitudes." Inter-rater reliability was calculated (Attitude: 0.77; Use Cases: 0.65).
  2. LLM-Assisted Scaling: Due to the dataset size, manual coding of all entries was infeasible. The researchers utilized Gemini-3.1-Pro (identified as the highest-performing model) to classify the remaining data.
  3. Model Validation: The LLM was trained on 115 manually annotated posts. Performance metrics (Precision, Recall, F1-score) were used to validate the model's classification of relevance, attitude, and specific use cases.
  4. Classification Tasks: The LLM classified data into three dimensions:
     • Relevance (Is the post about LLM use for diabetes?)
     • Attitude (Positive, Negative, Neutral)
     • Use Case (9 specific categories).

3. Key Contributions

• First Empirical Evidence of Organic Use: This is one of the first studies to map the real-world spectrum of how patients integrate general-purpose LLMs into their daily diabetes self-management, moving beyond controlled clinical trials.
• Taxonomy of Use Cases: The study establishes a comprehensive framework categorizing LLM interactions from low-risk informational support to high-risk therapeutic delegation.
• Risk Identification: It explicitly identifies "Therapeutic Delegation" as a critical risk domain where patients rely on LLMs for insulin dosing and clinical interpretation without professional oversight.
• Conceptual Mapping: The authors propose a conceptual model (Figure 1) mapping LLM use across disease trajectories (pre-diagnosis to long-term management) and meta-tasks (informational vs. therapeutic).

4. Key Results

A. Model Performance

• Gemini-3.1-Pro outperformed Gemini-2.5-Flash and GPT-5.3 in most categories, achieving F1-scores >0.75 for most use cases.
• The model successfully classified 1,590 relevant data points (623 posts + 967 comments).
• Performance was lower for granular sub-themes like "Emotional Support" and "Disease Management," leading the researchers to rely on the model primarily for broader categorization.

B. User Attitudes

• Neutral Dominance: 59.2% of posts were neutral (descriptive without explicit sentiment).
• Positive (21%): Users praised LLMs for reducing cognitive burden, generating meal plans, and simplifying complex data analysis (e.g., "It works absolutely fantastic... tells me how many units per carbs").
• Negative (19.9%): Criticisms focused on hallucinations, lack of clinical alignment (e.g., suggesting foods a nutritionist forbade), and unreliable medical advice.

C. Identified Use Cases (Ranked by Frequency)

1. Nutrition and Diet Support (25.9%): The most common use. Includes carb counting, meal planning, and dietary restriction queries.
2. Disease Management (22.5%): Practical guidance on lifestyle, symptom management, and behavioral strategies. Notably, some users shared CGM data to request insulin ratio optimization.
3. Diabetes Education & Self-Learning (18.3%): Explaining pathophysiology, mechanisms, and research progress.
4. Quantitative/Longitudinal PGHD Analysis (13%): Users uploading raw glucose, sleep, or activity data for trend analysis and visualization.
5. Clinical Interpretation (11.4%): Symptom checking, interpreting lab results (HbA1c), and seeking second opinions on diagnoses (e.g., questioning Type 1 vs. MODY).
6. Emotional Support (2.3%): Reflective conversations and seeking encouragement.
7. Healthcare Service Navigation (1.4%): Insurance coverage and device accessibility.
8. Medical Device Troubleshooting (1.3%): Resolving issues with CGMs or insulin pumps.

D. Critical Risks Observed

• Therapeutic Delegation: Users actively adjusting insulin basal rates and correction factors based on LLM suggestions.
• Diagnostic Framing: LLMs influencing patient perception of symptoms, sometimes causing unnecessary alarm or false reassurance.
• Data Privacy: Patients uploading sensitive Protected Health Information (PHI) and lab results to non-HIPAA compliant public LLM interfaces.

5. Significance and Implications

• Clinical Practice: Clinicians cannot ignore LLM use; they must proactively discuss it with patients to understand how AI is influencing their care decisions.
• Patient Education: Diabetes education programs must evolve to include AI literacy, teaching patients how to verify LLM outputs, recognize limitations, and understand that AI provides probabilistic patterns, not medical authorization.
• Policy & Regulation: The study argues for a tiered regulatory approach. Low-risk informational uses may not need strict regulation, but high-risk "therapeutic delegation" (insulin dosing) should be treated as a regulated medical device, requiring FDA review and liability frameworks.
• Future Design: Future AI health tools should incorporate "context-aware guardrails," explicitly communicate uncertainty, and clearly delineate the boundary between education and medical advice.

Conclusion: The paper concludes that while LLMs offer significant potential to reduce the burden of diabetes management, their current unregulated integration poses safety risks. The focus must shift from whether patients will use these tools to how healthcare systems can ensure their safe, equitable, and clinically validated integration.