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Leveraging Generative AI for Advanced Clinical Reasoning: The Definitive Guide to Prompt Engineering and Clinical Frameworks; version 1.0; June 27, 2025
Imagine having an intelligent clinical assistant who is not just a passive database but a dynamic reasoning partner — an AI trained on the entire corpus of modern medicine, capable of understanding your nuanced clinical problems and generating insightful, evidence-based responses. But this assistant needs you to lead it with precise, sophisticated commands: your prompts are the keys that unlock its power.
In this detailed guide, you will learn how to transform simple AI queries into a robust clinical dialogue, incorporating structured clinical reasoning models, advanced prompt engineering, and safety practices — so that AI becomes your fellow-level clinical advisor rather than a generic knowledge engine.
1. The Prompting Hierarchy: Evolving from Simple Queries to Advanced Reasoning
Prompting AI is not just about asking a question; it’s about crafting a context-rich, logically structured command that guides the AI’s internal thought processes. We can conceptualize this progression as a hierarchy of sophistication, from simple to complex.
Tier 1: Zero-Shot Prompting — Basic, Context-Free Queries
Definition:
The AI is given a direct question with no patient data or context, relying solely on its pre-trained knowledge.
Example Prompt:
"What are common causes of pain after total knee replacement?"
Typical Response:
The AI might list infections, implant loosening, instability, stiffness, or complex regional pain syndrome (CRPS).
Advantages:
Fast, straightforward.
Useful for rapid refreshers or broad knowledge checks.
Limitations:
Very generic and broad; does not consider individual patient factors.
Lacks clinical prioritization — all causes get equal weight regardless of probability in your case.
May include irrelevant or outdated info due to lack of context.
Clinical Takeaway:
Zero-shot prompting is a starting point, akin to opening a textbook chapter. It is valuable for quickly recalling known facts but insufficient for guiding patient-specific reasoning.
Tier 2: Few-Shot Prompting — Embedding Patient Context
Definition:
Add a concise but relevant clinical vignette to guide the AI’s reasoning.
Example Prompt:
"A 67-year-old woman is 6 months post-left TKR, complaining of persistent pain. ESR is 28 mm/hr, CRP 5 mg/L, joint fluid cultures negative. She has a tense joint effusion but no redness or fever. What are the most likely causes of her pain?"
Typical Response:
The AI integrates lab values and clinical exam to prioritize causes: low-grade infection (culture-negative PJI), aseptic loosening, inflammation from ALVAL, neuropathic pain, or CRPS.
Advantages:
More tailored and clinically relevant output.
Filters out less likely causes given the clinical data.
Mimics early differential diagnosis formulation.
Limitations:
AI still does not explicitly show how it weighs data or why it prefers one diagnosis.
May miss subtleties if key details are omitted.
Clinical Takeaway:
Few-shot prompting is like discussing a case with a junior colleague—more useful but still requiring you to interrogate the reasoning process.
Tier 3: Chain-of-Thought Prompting — Encouraging Stepwise Reasoning
Definition:
Explicitly instruct the AI to “think aloud” by breaking down reasoning step-by-step before finalizing conclusions.
Example Prompt:
"List causes of culture-negative PJI. For each cause, explain pathophysiology, why standard cultures might fail, and then apply these to a patient who failed DAIR with negative cultures. Rate the likelihood (1-10) of each cause."
Typical Response:
Explains biofilm-forming bacteria evade detection because they adhere to implants in dormant states.
Describes atypical organisms like Propionibacterium acnes growing slowly, often missed on routine cultures.
Links these facts to the patient’s failed debridement and negative cultures.
Assigns confidence scores reflecting clinical probability.
Advantages:
Makes AI’s reasoning transparent, enabling you to follow and challenge it.
Builds trust and mitigates hallucination risk.
Approximates the clinical thought process of expert consultants.
Limitations:
Requires more complex prompts and patience for longer responses.
AI might still omit nuance if not asked carefully.
Clinical Takeaway:
Chain-of-thought prompting transforms AI into a thinking partner and helps you engage in reflective clinical reasoning.
Tier 4: Retrieval-Augmented Generation (RAG) — Anchoring to Authoritative Evidence
Definition:
Feed the AI with a trusted guideline or primary document, instructing it to use only this source to answer.
Example Prompt:
"Given the 2018 MSIS criteria for PJI pasted below, evaluate if this patient’s synovial fluid WBC of 3,000 cells/µL and 70% PMNs meet PJI criteria. Quote the guideline to support your conclusion."
Typical Response:
AI cites exact synovial fluid cutoffs and notes that the patient does or does not meet major/minor criteria, justifying with direct guideline language.
Advantages:
Dramatically reduces hallucination and outdated information risk.
Grounds reasoning in evidence-based medicine (EBM).
Increases clinician confidence in AI outputs.
Limitations:
Requires manual preparation of source texts.
AI might selectively quote or misinterpret if prompt is unclear.
Clinical Takeaway:
RAG prompting is mandatory for evidence-sensitive decisions, such as diagnosis confirmation and treatment planning, where accuracy is critical.
Tier 5: Iterative Prompting — Dynamic, Contextual Dialogue
Definition:
Engage the AI in a back-and-forth conversation, progressively refining the differential diagnosis, diagnostic plan, or treatment strategy as new information emerges.
Example Dialogue:
"Create a diagnostic workup for persistent pain after TKR."
"The patient has severe claustrophobia and cannot undergo MRI. Suggest alternative imaging modalities with pros and cons."
"Given negative cultures and equivocal labs, propose minimally invasive biopsy strategies."
Advantages:
Mimics real clinical decision-making, adapting to patient-specific constraints.
Allows testing alternative hypotheses and contingency plans.
Enables integration of multidisciplinary viewpoints (radiology, microbiology, surgery).
Limitations:
Time-intensive.
Requires clinician’s active engagement to steer conversation meaningfully.
Clinical Takeaway:
Iterative prompting simulates a consultation with a multidisciplinary team, adapting clinical decisions dynamically.
Tier 6: Meta-Prompting — Training the Trainer
Definition:
Ask the AI to evaluate your previous prompts and suggest improvements for clarity, completeness, or precision.
Example Prompt:
"Review the diagnostic prompt I gave earlier. How can I rephrase it to obtain more structured and clinically actionable answers?"
Typical Response:
AI suggests specifying patient age, symptoms, lab values, expected output format (list, table, differential with confidence scores), and defining the clinical role (e.g., orthopaedic specialist).
Advantages:
Helps develop your skills as a clinical prompt engineer.
Increases efficiency and accuracy of AI interactions.
Improves reproducibility of clinical AI workflows.
Limitations:
Requires iterative practice and acceptance of feedback.
Clinical Takeaway:
Meta-prompting empowers you to become an AI-savvy clinician, capable of extracting maximal clinical value.
2. Clinical Reasoning Frameworks: Mental Software for Diagnostic Mastery
Simply querying AI isn’t enough. Pairing AI with proven clinical reasoning models helps you systematize thought processes, reduce cognitive bias, and make reproducible decisions under uncertainty.
Clinical reasoning isn’t just a skill — it’s a complex cognitive architecture built on foundational mental models that help clinicians organize information, weigh evidence, identify uncertainties, and decide on the next steps. Think of these frameworks as the mental software that runs your diagnostic process, allowing you to transform scattered data points into coherent, actionable clinical decisions.
Each framework brings a unique lens:
Some sharpen your focus by targeting bottlenecks.
Others unlock innovation by resolving contradictions.
Some teach you to view the patient as a complex living system.
Still others simulate expert uncertainty and decision variability.
Together, they create a multi-dimensional toolkit that complements AI’s vast knowledge base, allowing you to harness generative models for true clinical mastery.
1. Theory of Constraints (TOC): Finding and Fixing the Diagnostic Bottleneck
Core Concept and Origins
Developed by Eliyahu M. Goldratt in the manufacturing industry, TOC identifies the “constraint” or weakest link that limits the performance of an entire system. In factories, this might be a slow machine; in business, a process bottleneck.
Clinical Translation
In clinical diagnosis and management, TOC helps you pinpoint the single biggest barrier limiting your ability to solve a patient’s problem. This might be an ambiguous lab test, a confusing imaging finding, or incomplete clinical history.
By concentrating your investigative energy on resolving this bottleneck, you can dramatically accelerate diagnosis and improve efficiency.
How TOC Works in Practice
Identify the Constraint: Ask, “What is the most uncertain or missing piece of information preventing diagnosis or treatment?”
Exploit the Constraint: Use existing resources to maximize understanding of this constraint (e.g., repeat targeted testing, detailed clinical exam).
Subordinate Everything Else: Temporarily halt less urgent tests or treatments until the constraint is addressed, preventing scattershot approaches.
Elevate the Constraint: Invest in advanced diagnostics (e.g., molecular assays, biopsy) only when necessary.
Repeat the Process: Once the first bottleneck is resolved, find the next constraint.
Example
For a patient with persistent pain after total knee replacement (TKR) and negative cultures:
The diagnostic constraint might be the inability to confirm infection despite suspicion.
The solution is targeted diagnostics such as alpha-defensin testing or synovial biopsy rather than broad, unfocused investigations.
This avoids unnecessary imaging or empiric antibiotic trials that do not address the core diagnostic issue.
Integration with AI
When using AI, instruct it to help identify the bottleneck explicitly, suggest tests or approaches that maximize the information gain at this point, and deprioritize irrelevant diagnostics. This can focus AI recommendations and reduce noise.
2. TRIZ (Theory of Inventive Problem Solving): Resolving Clinical Contradictions Creatively
Origins and Philosophy
TRIZ was created by Genrich Altshuller, a Soviet engineer, who studied patents and inventions to identify universal principles of problem-solving. TRIZ teaches that many problems arise from contradictions—requirements that seem impossible to satisfy simultaneously—and that innovative solutions resolve these contradictions without compromise. Since its inception in the Soviet Union in the 1960s, TRIZ has evolved into a systematic methodology for innovation. Its application begins with defining the problem and identifying contradictions, which can be technical (conflicting parameters) or physical (opposing requirements). Using tools like the contradiction matrix, TRIZ maps these contradictions to one or more of the 40 inventive principles, which provide strategies for generating solutions.
Applying TRIZ to Clinical Medicine
Clinical contradictions are everywhere:
The need for early diagnosis vs. risk of invasive testing.
Aggressive infection treatment vs. potential for toxicity or impaired healing.
Immobilization for healing vs. risk of stiffness and loss of function.
TRIZ offers structured methods to:
Separate contradictory requirements in time or space.
Use intermediaries or buffer systems.
Invert or transform the problem space to find new solutions.
TRIZ Principles Commonly Used in Medicine
Separation in Time: For example, staging surgeries instead of doing everything at once.
Separation in Space: Localize treatment to minimize systemic side effects.
Use of an Intermediary: Employ diagnostic arthroscopy before full revision surgery.
Principle of "Taking Out": Removing only infected tissues rather than entire implants.
Principle of “Dynamicity”: Modifying treatment based on evolving clinical response rather than fixed protocols.
Clinical Example
In a patient with suspected prosthetic joint infection (PJI), a full implant removal carries high morbidity but may be needed if infection is confirmed. TRIZ-inspired strategy might be:
First stage a minimally invasive biopsy (separation in time and space).
Use local antibiotic delivery instead of systemic high-dose therapy (taking out/inversion).
Adjust the plan dynamically based on biopsy results (dynamicity).
Integration with AI
Prompt the AI to identify clinical contradictions and suggest TRIZ-inspired innovative solutions, enabling creative, patient-specific management plans that balance risks and benefits.
3. Complex Adaptive Systems (CAS): Understanding the Patient as an Interconnected Ecosystem
Theoretical Foundations
CAS theory describes systems made up of multiple interacting agents or subsystems, where overall behaviour emerges unpredictably from these interactions. This applies to ecosystems, economies, and crucially, human physiology and pathology.
CAS in Clinical Reasoning
Patients are not just collections of symptoms and signs; they are dynamic, complex adaptive organisms influenced by genetic, immunological, psychological, social, and environmental factors.
Chronic pain, for example, emerges from complex neuro-immune interactions, not just structural damage.
Healing involves multiple subsystems adapting in parallel (vascular, inflammatory, neurological).
Psychological stressors can exacerbate physical symptoms via neuroendocrine pathways.
Practical Applications
Move beyond linear cause-effect models to appreciate feedback loops and emergent phenomena.
Anticipate that interventions may have unpredictable ripple effects (e.g., antibiotics affecting microbiome and immune status).
Design multidisciplinary interventions targeting multiple subsystems simultaneously (orthopaedic, pain medicine, psychology, rehabilitation).
Clinical Example
For persistent post-TKR pain, rather than focusing exclusively on implant infection or loosening, CAS reasoning encourages:
Assessing psychological factors such as depression or anxiety.
Considering central sensitization and neuropathic pain.
Coordinating care across orthopaedics, psychiatry, pain specialists, and physical therapists.
Integration with AI
Instruct AI to map interconnections across systems and propose integrated, multidisciplinary plans that address the biological, psychological, and social domains of illness.
4. SCRIPT Concordance Test (SCT): Modelling Clinical Reasoning Under Uncertainty
Background and Purpose
SCT is a validated educational tool to measure clinical reasoning by assessing how clinicians interpret ambiguous clinical information and weigh evidence, recognizing that expert clinicians often disagree due to genuine uncertainty.
SCT Methodology
Present a clinical scenario with new data or test results.
Ask how this new information influences diagnosis or management, rating agreement on a Likert scale.
Compare with a reference panel of experts to evaluate reasoning patterns.
Clinical Reasoning Insights
SCT helps you appreciate the nuances of probability shifts in clinical reasoning, rather than binary yes/no decisions.
Reflects real-world ambiguity where test results may be borderline or conflicting.
Trains clinicians to tolerate uncertainty and update decisions iteratively.
Clinical Example
In PJI workup, a marginally elevated synovial fluid WBC count may not definitively confirm infection. Using SCT principles:
Consider how a positive alpha-defensin test shifts probability from “unlikely” to “possible.”
Adjust treatment plans accordingly, weighing risks of overtreatment vs. undertreatment.
Integration with AI
Ask AI to simulate SCT-style reasoning, showing how adding or subtracting test results changes the differential diagnosis and management priorities with graded confidence levels.
5. Combining Frameworks: Synergistic Clinical Reasoning
Why Integrate Multiple Frameworks?
No single framework fully captures clinical complexity. Together, they provide:
TOC: Focus and efficiency.
TRIZ: Creative problem solving.
CAS: Holistic understanding.
SCT: Managing uncertainty.
How to Synthesize
Start with TOC to identify bottlenecks.
Use TRIZ to resolve contradictions around bottlenecks.
Apply CAS to understand wider systemic influences.
Use SCT to continuously update decisions as data evolves.
Practical Workflow Example
TOC: Identify the biggest unknown preventing diagnosis.
TRIZ: Design innovative diagnostics or treatments that circumvent risks.
CAS: Ensure multidisciplinary care to address all contributing factors.
SCT: Continuously reassess confidence and update plans as new results come in.
AI as a Framework-Driven Partner
Train AI prompts to explicitly incorporate these frameworks, e.g.:
"Using Theory of Constraints, identify the diagnostic bottleneck."
"Apply TRIZ principles to propose innovative management."
"Map patient factors as a Complex Adaptive System."
"Simulate expert uncertainty using SCRIPT Concordance."
This transforms AI from a fact-finder into a clinical reasoning collaborator, mirroring expert thought processes.
Below, you'll find two structured AI prompt templates per framework—complete with explanations—plus clinical case walkthroughs showing each one in action. Use these to guide AI in a way that mirrors expert-level reasoning.
1. Theory of Constraints (TOC)
🔧 AI Prompt Template
Act as an experienced clinician diagnosing a complex case.
Case:
- Patient: Mrs. X, 67, 6 months post-TKR.
- Symptoms: Persistent knee pain (VAS 8/10), failed DAIR, negative cultures, stable radiographic erosion.
Task:
1. Identify the single diagnostic bottleneck preventing clarity (the constraint).
2. Propose a tiered diagnostic plan that exploits this constraint and subordinates non-essential tests.
3. Once the bottleneck is addressed, outline steps to elevate treatment.
Format:
1) Constraint:
2) Diagnostic protocol:
• Phase 1:
• Phase 2:
3) Elevation plan:
4) Next constraint (if any):
Limit reasoning to 300 words.
🧠 Clinical Case Walkthrough – TOC
Constraint: Uncertainty about infection due to negative cultures.
Phase 1 (Exploit): Run alpha-defensin assay and PCR on synovial fluid.
Phase 2 (Subordinate): Defer MRI, CT, and panel bloodwork until results return.
Elevate -> Phase 3: If PJI confirmed: schedule arthroscopic biopsy with targeted sampling.
Next Constraint: Identifying causative organism for targeted therapy (resolved with biopsy).
2. TRIZ
🔧 AI Prompt Template
Act as a clinical innovator applying TRIZ.
Case summary: Mrs. X, persistent post-TKR pain; diagnostic dilemma — need tissue diagnosis vs. minimize harm.
1. Define the key contradiction in this scenario.
2. Choose 3 TRIZ principles to resolve it. See Annex 1
3. Propose three specific clinical solutions based on those principles.
Explain each step concisely.
🧠 Clinical Case Walkthrough – TRIZ
Contradiction: Need invasive biopsy for diagnosis but want to avoid surgery due to risk.
Principles & Solutions:
Separation in Time: Stepwise diagnostics—start with non-invasive biomarkers before biopsy.
Local Quality: Use ultrasound-guided needle biopsy rather than open arthrotomy.
Intermediary: Test synovial fluid with alpha-defensin before tissue is sampled.
3. Complex Adaptive Systems (CAS)
🔧 AI Prompt Template
Act as a systems-thinking clinician using Complex Adaptive Systems theory.
Patient: Mrs. X, severe post-TKR pain, mixed physical/anxiety symptoms.
Tasks:
1. Map interconnections (physical, inflammatory, psychological).
2. Explain how these interactions can perpetuate pain.
3. Propose a coordinated MDT management plan.
Return in bullet points.
🧠 Clinical Case Walkthrough – CAS
System Map:
Persistent inflammation ↔ Pain signalling ↔ Anxiety.
Anxiety ↔ Central sensitization ↔ More pain.
Pain and functional limitation ↔ Reduced activity ↔ Depression.
Team: Orthopaedics, ID, pain specialist, psychologist, physiotherapist.
Plan: Simultaneous synovial fluid diagnostics, pain-modulating meds, CBT, gentle rehab, anti-inflammatory regimen.
4. SCRIPT Concordance Test (SCT)
🔧 AI Prompt Template
Act as an expert panel for SCT reasoning.
Case base: Mrs. X as above.
Scenario A: Alpha-defensin test is positive.
Scenario B: Alpha-defensin is negative.
For each, rate (1–5) the likelihood of PJI and appropriate next steps, with justification.
🧠 Clinical Case Walkthrough – SCT
Scenario A: Positive token increases likelihood to 4/5 → next: schedule biopsy and targeted antibiotic regimen.
Scenario B: Negative → PJI unlikely. Next: start CRPS evaluation and targeted rehab over surgery.
✅ Combined Master Prompt (All Frameworks)
You are a multi-framework clinical reasoning AI.
Patient: Mrs. X, 6 months post-TKR, persistent pain (VAS 8/10), negative cultures, failed DAIR, stable erosion.
Tasks:
1. TOC: Identify diagnostic constraint; design tiered plan.
2. TRIZ: Define contradiction; pick 2 inventive principles; suggest solutions.
3. CAS: Map interactions; propose MDT plan.
4. SCT: Scenario A (α-defensin +) / B (–): rate PJI likelihood (1–5) and propose next steps.
Return each section clearly labelled.
Why This Works
TOC ensures focus and smart resource use.
TRIZ empowers creative, low-risk solutions to dilemmas.
CAS promotes holistic, multidisciplinary care.
SCT tests decision-making under uncertainty.
By using these prompts and structures, you can use AI to model best-practice clinical reasoning—deep, nuanced, and adaptable. Let me know if you'd like to refine or expand these for other clinical scenarios!
Summary
Clinical reasoning frameworks are your mental operating system, enabling you to navigate the complexity of medicine with rigor, creativity, and humility. By deeply integrating TOC, TRIZ, CAS, and SCT into your practice — especially when augmented by generative AI — you elevate diagnosis and management from guesswork to evidence-driven mastery.
4. Safety and Best Practices Checklist
Protect Patient Privacy: Never input identifiable info into public AI.
Demand Transparency: Ask for chain-of-thought explanations and references.
Validate & Crosscheck: Use AI as an adjunct, not sole decision-maker. Confirm with guidelines and colleagues.
Avoid Overreliance: AI can hallucinate; watch for confident but unsupported answers.
Iterate & Improve: Use meta-prompting to refine your queries for clarity and utility.
Final Words
Generative AI is a revolutionary clinical reasoning partner — transforming raw medical knowledge into patient-centred wisdom when guided by expertly engineered prompts and structured reasoning frameworks. It empowers clinicians to think deeper, reason clearer, and act more confidently in complex diagnostic and therapeutic scenarios.
Annex 1: To choose 3 TRIZ principles to resolve a clinical contradiction, you follow a structured method that mirrors how engineers solve design problems — but apply it to medicine.
Let me walk you through exactly how to do it, step-by-step, with a live example, and then show you how to generalize this skill.
🔧 How to Choose TRIZ Principles in Clinical Contexts
Step 1: Define the Clinical Contradiction Clearly
This is the heart of TRIZ. A contradiction is when two necessary actions conflict with each other. For example:
"To diagnose periprosthetic joint infection (PJI), I need to get tissue, which requires surgery. But surgery is invasive and risky when the diagnosis is still uncertain."
So:
Action A (want): Perform surgery to get a sample.
Action B (avoid): Do not expose patient to unnecessary harm.
That’s your technical contradiction.
Step 2: Translate the Medical Problem into TRIZ Terms
Ask yourself: “What’s improving?” and “What’s worsening?”
Improving Feature: Diagnostic certainty (I want better accuracy).
Worsening Feature: Invasiveness / patient risk.
In TRIZ, improving one thing while worsening another is the classic contradiction.
Step 3: Use the TRIZ Contradiction Matrix (Optional but Powerful)
TRIZ has a matrix that maps which of the 40 principles work best for a given contradiction. While originally designed for engineering, it can still inspire clinical strategies.
In our case:
Improving “Measurement Accuracy” (TRIZ #28)
Worsening “Harm to the System” or “Complexity” (#22 or #36)
Consulting the matrix (adapted to medicine), common principles that help resolve this include:
Contradiction
Suggested TRIZ Principles
Better accuracy vs. higher risk
1, 10, 28, 35
But let’s now go deeper by choosing three relevant principles and applying them directly.
✅ Three TRIZ Principles Applied to Mrs. X (Post-TKR Diagnostic Dilemma)
Principle #3 – Local Quality
“Make each part of a system function optimally for its specific context.”
Clinical Use: Instead of a full surgical debridement, perform a targeted ultrasound-guided synovial biopsy focused only on the high-suspicion area. You're not treating the whole joint, just probing the suspect zone.
Principle #10 – Preliminary Action
“Perform required changes or testing in advance to reduce later risk.”
Clinical Use: Before doing an invasive procedure, screen with high-yield biomarkers like alpha-defensin or next-gen sequencing on synovial fluid. If they suggest infection, you justify surgery better.
Principle #24 – Intermediary
“Use an intermediary system to transfer or mediate action.”
Clinical Use: If the problem is “I need tissue but can’t cut open the joint,” use an intermediary diagnostic agent (e.g., nuclear medicine tracer or MARS-MRI) to visualize infection without cutting.
🧠 Recap of Application to the Case
TRIZ Principle
Clinical Translation
Practical Step
Local Quality (3)
Focus only on suspicious areas
Ultrasound-guided synovial biopsy
Preliminary Action (10)
Run non-invasive tests first
Alpha-defensin, CRP/ESR, PCR
Intermediary (24)
Insert a tool between the patient and full surgery
Nuclear scan or MARS-MRI instead of open surgery
🧪 Prompt You Can Use With AI:
Act as a TRIZ-trained clinical strategist.
Case: Mrs. X, 6 months post-TKR, persistent pain, failed DAIR, negative cultures.
1. Define the core contradiction in needing tissue diagnosis but avoiding surgery.
2. Apply 3 TRIZ principles (Local Quality, Preliminary Action, Intermediary) to resolve it.
3. Propose one real-world diagnostic strategy per principle.
📦 Bonus: Use These TRIZ Principles Often in Clinical Medicine
Here are a few TRIZ principles that frequently resolve medical contradictions:
TRIZ Principle
Common Clinical Application Example
1. Segmentation
Break a problem into smaller pieces (multi-phase treatment)
10. Preliminary Action
Use tests or prophylaxis before the main treatment
24. Intermediary
Use contrast agents, AI analysis, or imaging as go-betweens
28. Mechanics Substitution
Replace surgery with chemical/pharmacological means
35. Parameter Change
Adjust dose/frequency rather than switching drug
3. Local Quality
Treat only the diseased part (e.g., focal therapy in oncology)
See also:
