Recovering Clinical Detail in AI-Generated Responses for Low Back Pain Through Prompt Design

This study demonstrates that the reduced clinical detail observed in AI-generated responses for low back pain when using simplified prompts is due to output constraints rather than a loss of knowledge, as clinician-directed rewriting successfully restores the omitted medical information.

The Gist

Imagine you have a super-smart librarian who knows every medical book in the world. This librarian is an Artificial Intelligence (AI) designed to help doctors and patients understand back pain.

The researchers in this paper wanted to answer a big question: If you ask this librarian to explain things simply (like a 4th grader), does it actually forget the complex medical details, or does it just choose to hide them?

Here is the story of what they found, explained with some everyday analogies.

The Experiment: Three Ways to Ask the Same Question

The team took 15 common questions about low back pain (like "When should I go to the ER?" or "What are the risks of this surgery?") and asked the AI the same question three different ways:

1. The "Doctor" Prompt: They asked the AI to speak like a specialist physician.
   • Analogy: Asking the librarian to write a report for a medical conference.
2. The "4th Grader" Prompt: They asked the AI to explain it to a 10-year-old, using very simple words.
   • Analogy: Asking the librarian to write a storybook for a child.
3. The "Upscale" Prompt: They took the simple storybook answer and asked the AI: "Okay, now rewrite this story for a doctor. Don't leave out the important stuff."
   • Analogy: Taking the children's story and asking the librarian to expand it back into a full medical textbook.

The Results: Did the AI "Forget"?

1. The Simple Version Lost the Details
When the AI spoke to the "4th grader," the answers were short and simple. But they were missing crucial safety warnings, like "Check for red flags" or "Be careful if you are on blood thinners."

• The Metaphor: It's like asking a chef to make a "simple sandwich" for a kid. The chef gives you bread and cheese. But if you asked the chef for a "gourmet meal," they would have included the special sauce, the rare cheese, and the safety note about the nut allergy. The chef didn't forget the sauce; they just followed the instruction to keep it simple.

2. The "Upscale" Version Got the Details Back
When the researchers took that simple answer and asked the AI to "rewrite this for a doctor," the AI suddenly remembered all the missing details! The accuracy went back up to the same level as if they had asked the doctor question in the first place.

• The Metaphor: It's like realizing the chef did have the gourmet ingredients in the kitchen all along. They just weren't using them because you told them to keep it simple. Once you asked for the fancy version, they pulled out the special sauce and the rare cheese immediately.

The Big Takeaway

The paper proves that the AI didn't lose its knowledge. It was just "mirroring" the style of the question.

• If you ask for simple: It gives you a simple, sometimes incomplete answer.
• If you ask for complex: It gives you a detailed, complete answer.

Why This Matters for You

This is a huge deal for how we use AI in healthcare:

• For Patients: If you use an AI to get medical advice, and you ask it to "explain it simply," you might miss out on important safety warnings. The AI isn't being "dumb"; it's just following your order to be simple.
• For Doctors: If a doctor uses AI to help plan a surgery, they need to make sure they ask in a professional way. If they ask too casually, the AI might leave out critical steps, which could be dangerous.

The Bottom Line

Think of the AI like a chameleon. It changes its colors (level of detail) based on the environment (the prompt you give it). It doesn't lose its ability to see; it just blends in with whatever you ask it to be.

The lesson: If you want the full picture, you have to ask for the full picture. If you ask for a simplified version, be aware that you might be missing the fine print.

Technical Summary

1. Problem Statement

Large Language Models (LLMs) are increasingly utilized in healthcare for clinical decision support and patient education. However, a critical tension exists between readability and clinical completeness:

• The Trade-off: To improve health literacy, medical information is often simplified (e.g., targeting a 4th-grade reading level). Prior research suggests this simplification leads to shorter, less accurate responses with omitted clinical details.
• The Knowledge Gap: It remains unclear whether these omissions represent a fundamental loss of knowledge within the LLM or merely a prompt-driven suppression of information. If the knowledge is lost, simplified AI tools are inherently unsafe for clinical contexts. If the knowledge is merely suppressed, it can be recovered through specific prompting strategies.
• Clinical Stakes: In interventional pain medicine (specifically low back pain), incomplete reasoning regarding red flags, differential diagnoses, or procedural contraindications can lead to unsafe patient management.

2. Methodology

The study employed a controlled comparative design using 15 standardized low back pain questions relevant to interventional pain practice. The questions were submitted to ChatGPT (GPT-5.0, November 2025) under three distinct conditions:

1. Professional Prompt (DP): A direct request for evidence-based, professional-level clinical reasoning suitable for physicians.
2. Fourth-Grade Prompt (D4): A request to answer the same questions at or below a 4th-grade US reading level, explicitly instructed not to compromise accuracy.
3. Upscaled Prompt (U4→MD): A two-step process where the model was first given the D4 response and then instructed to rewrite that specific response for a healthcare professional, expanding it to be clinically complete and referencing guidelines.

Evaluation Metrics:

• Accuracy: Rated by three independent physicians (two pain specialists, one EM resident) on a 0–2 ordinal scale (0: Incorrect, 1: Partially Correct, 2: Correct).
• Clinical Completeness: A binary consensus metric (agreement by ≥2 raters) assessing the presence of key elements: differential diagnosis, red flags, evidence-based management, and escalation criteria.
• Linguistic Measures: Word count and Flesch–Kincaid Grade Level (FKGL).
• Statistical Analysis: Paired t-tests compared conditions; Fleiss' kappa was used for inter-rater reliability.

3. Key Contributions

• Differentiation of Knowledge vs. Output: The study provides empirical evidence that reduced clinical detail in simplified prompts is a result of output constraints (linguistic mirroring) rather than a deficit in the model's internal knowledge base.
• Recovery Mechanism: It demonstrates that clinician-directed reframing (upscaling) can fully recover omitted clinical details from simplified responses, restoring accuracy to professional baselines.
• Methodological Framework: The study introduces a "rewrite" protocol to test the recoverability of information, moving beyond simple accuracy comparisons to analyze the retrievability of suppressed data.

4. Results

• Accuracy:
  • DP (Professional): Highest mean accuracy (1.76).
  • D4 (4th Grade): Significantly lower accuracy (1.33; p = 0.00086).
  • U4→MD (Upscaled): Accuracy returned to baseline (1.76), statistically indistinguishable from DP (p ≈ 1.00).
• Clinical Completeness:
  • DP: 80.0% of responses were clinically complete.
  • D4: Only 6.7% were complete (massive omission of red flags, contraindications, etc.).
  • U4→MD: 73.3% were complete, demonstrating successful recovery of missing content.
• Linguistic Complexity:
  • FKGL: D4 responses were significantly simpler (7.13) compared to DP (11.27). Upscaled responses were even more complex (13.63) than the original professional prompts.
  • Word Count: D4 responses were shorter (35.5 words) than DP (49.7 words). Upscaled responses were the longest (55.9 words).
• Reliability: Inter-rater reliability for absolute scores was low (Fleiss' κ = 0.17), likely due to the ordinal nature of the scale and category prevalence. However, trends were consistent across raters, validating the relative comparisons between conditions.

5. Significance and Implications

• Safety of Patient-Facing AI: The findings suggest that while simplified AI outputs are useful for patient education, they should not be used as the sole source of clinical information. The "loss" of detail is recoverable, implying that the model retains the necessary safety data but suppresses it based on the audience constraint.
• Prompt Engineering as a Clinical Tool: The study validates that prompt design is a critical variable in medical AI performance. Clinicians can use "upscaling" prompts to retrieve full clinical reasoning from models that have been constrained for patient readability.
• Workflow Integration: LLMs should be viewed as supplementary tools for clinicians rather than autonomous decision-makers. The ability to toggle between "patient explanation" and "clinical reasoning" modes allows for a dual-use workflow where safety is maintained through professional oversight.
• Future Research: The authors call for adaptive prompting strategies in real-world workflows and further investigation into how these dynamics vary across different medical specialties and model architectures.

Conclusion: The reduction in clinical detail observed in simplified LLM responses is a prompt-conditioned phenomenon, not a knowledge deficit. By employing clinician-directed upscaling, omitted critical information can be fully recovered, ensuring that AI tools remain safe and effective for both patient education and clinical decision support.