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REVIEW 4 major objections 5 minor 47 references

The Optimization Paradox in Clinical AI Multi-Agent Systems

T0 review · 4 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read Building a clinical AI from its best parts made it diagnose worse.

desk verdict Worth reading: the component-optimized 'Best of Breed' system underperforms most multi-agent systems on held-out diagnosis, but the headline gap leans on a cherry-picked baseline and an untested retriever assumption. read the letter →

arxiv 2506.06574 v2 pith:HP5B4GG2 submitted 2025-06-06 cs.AI cs.MA

Suhana Bedi , Iddah Mlauzi , Daniel Shin , Sanmi Koyejo , Nigam H. Shah This is my paper · ORCID
classification cs.AIcs.MA
keywords multi-agentsystemsclinicaldecisionsupportoptimizationparadoxbestofbreedcomponent-levelevaluationdiagnosticaccuracylargelanguagemodelsend-to-endvalidation
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper sets out to show that optimizing the individual components of a clinical AI system can hurt the system as a whole. It compares single-agent systems, multi-agent systems, and a Best of Breed system assembled from the individually top-scoring agents on 2,400 real emergency cases for four abdominal conditions. The Best of Breed system scored highest on process metrics like lab interpretation accuracy (85.5%) yet delivered significantly lower diagnostic accuracy (67.7%) than a top multi-agent system (77.4%). The paper attributes this gap to incompatibility between agents and breakdowns in information flow, not to weak components, and argues that clinical AI must be validated end-to-end rather than by component metrics alone.

What carries the argument

The central mechanism is the multi-agent decomposition of clinical diagnosis into three stages: information gathering, information interpretation, and differential diagnosis, coordinated by a sequential orchestrator with explicit handoffs. The Best of Breed construction selects the top single agent for each stage on a development set and then observes its behavior when combined, revealing that process metrics such as coverage-to-test ratio and lab interpretation accuracy do not track diagnostic outcomes. The paper introduces an error taxonomy distinguishing insufficient information gathering, unauthorized test ordering, and hallucinated test results, and uses it to attribute the performance gap to agent compatibility rather than component deficiencies.

What would settle it

Re-run the Best of Breed system on the same 1,190 test cases but replace the natural-language retriever with a perfect oracle that always returns exactly the requested labs and imaging reports; if the diagnostic accuracy gap against the top multi-agent system narrows or disappears, then the Optimization Paradox is really a retrieval artifact, not evidence of agent incompatibility.

Watch

Extended reading notes

Core claim

The paper's central claim is the Optimization Paradox: a multi-agent clinical diagnostic system constructed from the best-performing component for each task can underperform systems made of less individually capable agents. In the authors' experiments, the Best of Breed system, built from GPT-4o for information gathering, GPT-4.1 for lab interpretation, and Gemini-2.0-Flash for differential diagnosis, achieved 85.5% interpretation accuracy but only 67.65% diagnostic accuracy, versus 77.39% for the top multi-agent system, a drop of 9.75 percentage points (McNemar's test, p < 0.0001). Error analysis attributes the failure to information-flow breakdowns during agent handoffs: the diagnosis agent received incomplete data (7.06% of cases), attempted unauthorized test ordering (13.87%), and hallucinated test results (13.87%, a 33-fold higher rate than the top system). The authors conclude that component-level metrics cannot predict or validate system-level clinical performance, and that end-to-end evaluation against clinical outcomes is necessary.

Load-bearing premise

The paper assumes the retriever that fetches patient data from records is 100% accurate on the held-out test cases, based only on its perfect performance on a preliminary set; if retrieval ever fails on the test set, those failures would be misclassified as the information-gathering and hallucination errors that drive the central conclusion.

Editorial extensions

If this is right

  • If the Optimization Paradox holds, then component-level validation of clinical AI systems is insufficient and can be dangerously misleading.
  • A heterogeneous mix of model backbones in a multi-agent system may degrade diagnostic accuracy even when the same backbones perform well individually.
  • Process metrics like guideline coverage and lab interpretation accuracy should not be used as proxies for diagnostic correctness in deployment decisions.
  • Regulatory approval and hospital procurement of modular AI systems should require system-level performance evidence against clinical outcomes.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A testable design principle emerging from these results is that handoff interfaces between agents should be standardized, lossless, and backbone-independent, since the paper's error analysis suggests formatting and communication mismatches drive the failures.
  • The same paradox likely applies outside clinical settings: any modular system where upstream components are optimized independently may fail if the interface between modules is not jointly optimized with the downstream consumer.
  • One could extend the paper's approach by measuring, for each agent pair, the mutual information between what the upstream agent produces and what the downstream agent actually uses, to quantify compatibility directly.
  • A simpler retrospective test of the paper's attribution would be to rerun the Best of Breed system while keeping the same backbone for the interpreting and diagnosis agents, isolating whether the deficit comes from cross-backbone communication rather than from any single component.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper evaluates single-agent and multi-agent LLM systems for clinical diagnosis on 2,400 MIMIC-CDM abdominal pain cases, with a 20-case pilot, a 1,190-case development set for component selection, and a 1,190-case held-out test set. The authors construct a Best-of-Breed (BoB) system from the development-set winners for information gathering, interpretation, and differential diagnosis, and compare it against 8 single-agent and 26 multi-agent systems on the test set. They report that BoB achieves strong process metrics (e.g., 85.5% lab interpretation accuracy) but significantly lower diagnostic accuracy (67.65%) than a top-performing multi-agent system (77.39%), which they attribute to an 'Optimization Paradox' arising from agent incompatibility and information-flow breakdowns rather than individual component deficiencies.

Significance. If the core finding holds, the paper makes an important contribution to clinical AI evaluation: it would provide a concrete, real-patient-data demonstration that component-level metrics can diverge sharply from system-level diagnostic performance, supporting the need for end-to-end validation. The study's strengths include a sizable pathology-stratified dataset, a clean development/test split for component selection, evaluation across multiple model families, reported process and cost metrics beyond accuracy, and publicly released code. The central claim, however, rests on a test-set-selected comparator and an unvalidated retriever component, both of which currently weaken the causal interpretation that the paradox stems from fundamental agent incompatibility.

major comments (4)
  1. [§3.2.2 and Table 6] The headline comparison of 67.65% vs. 77.39% selects the 77.39% system as the maximum-accuracy multi-agent system on the same held-out test set used for the comparison. Choosing the best of 25 systems on the test set and then applying McNemar's test to that selected pair does not account for the selection process, so the reported p<0.0001 and the 9.75% gap are inflated. The authors should either pre-specify the comparator (e.g., a development-set-selected system), compare BoB against the full distribution of multi-agent accuracies, or adjust for multiple comparisons; this is load-bearing for the central paradox claim.
  2. [§2.3 and Appendix 5.3] The Retriever LLM is credited with 100% retrieval accuracy based only on the 20-case pilot set, and no retrieval audit is reported on the 1,190 test cases. Because BoB requests substantially fewer tests per case (3.62 total vs. 6.47 for the top system), a single retriever miss is less likely to be masked by redundant data, so the observed failure categories—insufficient information gathering, unauthorized test ordering, and hallucinated test results—could be caused by retriever failures rather than by BoB's agent-incompatibility mechanism. Reporting test-set retrieval accuracy or a sensitivity analysis that treats retrieval misses as a separate error source is necessary to support the causal interpretation in §3.2.5.
  3. [§2.5.1 and §3.2.5] The claim that BoB's failures stem from 'fundamental agent compatibility issues rather than individual component deficiencies' is not uniquely supported by the data. The information-gathering agent was selected for the highest coverage-to-test ratio while maintaining coverage >0.5, a criterion that explicitly rewards low test counts. BoB's low request volume (3.62 tests vs. 6.47 for the top system) could therefore be a direct consequence of the selection metric, not of inter-agent incompatibility. A cross-over experiment—e.g., pairing BoB's diagnosis agent with the top system's information-gathering agent, and vice versa—would help separate component behavior from interaction effects.
  4. [§3.2.4] The backbone-composition analysis compares small groups (7 homogeneous, 12 mixed, 6 heterogeneous systems) after excluding one outlier, and the reported p-values do not survive the stated Bonferroni correction. The conclusion that heterogeneous systems face 'inherent diagnostic challenges' is therefore over-interpreted; this analysis should be presented as exploratory, with confidence intervals and an explicit caveat about the small sample sizes.
minor comments (5)
  1. [§2.5.2] The text says the Best of Breed system was constructed by 'selecting the next best performing LLMs for each component,' which contradicts §2.5.1 where the top-performing agents are selected; the intended construction rule should be clarified.
  2. [Abstract and §3.2.1] The abstract calls 85.5% 'excellent process metrics' and implies BoB had superior components, but Table 7 shows another multi-agent system (multi_gemini-flash_gpt_gpt) achieving 85.78% lab interpretation accuracy; the wording should be qualified.
  3. [Table 2] The table note states 'Bold indicates large or medium effect size,' but the table as rendered in the manuscript does not show bold markers; the formatting should be fixed so the note is meaningful.
  4. [Table 7] The row labeled 'gemini_claude' appears to be an inconsistent or incomplete agent-system identifier compared with the naming convention used in Table 6; please correct or explain.
  5. [§3.2.2] The word 'baseline' is used for the top-performing multi-agent system, which is not a baseline in the usual sense; consider using 'comparator' to avoid confusion.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper is an empirical evaluation with a legitimate development/test split; the Optimization Paradox is an observed result, not a definitional or self-citational artifact.

full rationale

The paper's claim chain contains no derived quantity that is equivalent to an input by construction. Phase 1 (§2.5.1) selects one agent per task using development-set metrics (coverage-to-test ratio, lab interpretation accuracy, diagnostic accuracy), and Phase 2 (§2.5.2) evaluates all systems on a held-out test set; BoB's 67.7% diagnostic accuracy is measured, not predicted from component scores. The paradox is not circular because BoB is, by definition, built from the top-scoring components, and its underperformance is an empirical outcome. The only load-bearing assumptions—that the Retriever LLM's 100% success on the 20-case pilot transfers to all 1,190 test cases (§2.3) and that comparing against the best-performing multi-agent system on the same test set is a fair baseline—are evaluation-bias or external-validity concerns, not cases where a result reduces to its inputs. Cited prior work (e.g., the MIMIC-CDM framework [11] and clinical guidelines) provides external benchmarks and criteria rather than self-referential support. The paper's own limitations section acknowledges lack of external validation and single-metric component selection, which further confirms that the findings are empirical claims open to falsification rather than tautologies.

Assumptions & free parameters 2 free parameters · 5 assumptions · 0 invented entities

The central claims rest on MIMIC-CDM labels and reference ranges, the retriever's transfer of 100% retrieval accuracy, the validity of the process metrics used to choose BoB components, and statistical assumptions. The BoB construction introduces hand-chosen thresholds and per-component selection criteria. No new physical entities are introduced.

free parameters (2)
  • BoB coverage selection threshold = 0.5
    Phase 1 (§2.5.1) requires coverage score >0.5 when choosing the information gathering agent by coverage-to-test ratio; this hand-chosen value affects which model becomes BoB.
  • Orchestrator turn limit = 10
    Appendix 5.3: the information gathering agent stops after 10 turns; this architectural choice bounds information flow and influences downstream diagnostic accuracy.
assumptions (5)
  • domain assumption MIMIC-CDM ground-truth primary diagnoses and lab reference ranges are correct.
    All accuracy metrics in §2.4 and results in §3 assume the recorded primary diagnosis and reference ranges are reliable labels for evaluation.
  • domain assumption The Retriever LLM (GPT-4o) has 100% retrieval accuracy on the full test set.
    Section 2.3 states 100% retrieval accuracy on the preliminary set; the analysis assumes this transfers to all 1,190 test cases and does not confound agent-level comparisons.
  • domain assumption The process metrics in §2.4.2 are valid operationalizations of clinical guideline adherence and component quality.
    BoB component selection and the process-versus-outcome comparison rely on coverage, coverage-to-test ratio, and lab interpretation accuracy as meaningful proxies for component excellence.
  • domain assumption Replacing diagnosis mentions with underscores in the de-identified records is sufficient to prevent trivial pattern matching.
    Section 2 states that diagnosis mentions are replaced with underscores; accurate differential diagnosis requires this masking to be effective, otherwise accuracy estimates are inflated.
  • domain assumption Standard statistical assumptions for McNemar's and Mann-Whitney U tests hold without multiplicity correction.
    Win rates and p-values in §3.2 are reported without adjustment for the many pairwise comparisons; Bonferroni correction is applied only in one backbone analysis.

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Cite this review

Pith. "Pith review of The Optimization Paradox in Clinical AI Multi-Agent Systems." pith.science (2026). https://pith.science/paper/HP5B4GG2

@misc{pith2026250606574,
  author       = {Pith},
  title        = {Pith review of: The Optimization Paradox in Clinical AI Multi-Agent Systems},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HP5B4GG2}},
  note         = {Machine review of arXiv:2506.06574}
}
read the original abstract

Multi-agent artificial intelligence systems are increasingly deployed in clinical settings, yet the relationship between component-level optimization and system-wide performance remains poorly understood. We evaluated this relationship using 2,400 real patient cases from the MIMIC-CDM dataset across four abdominal pathologies (appendicitis, pancreatitis, cholecystitis, diverticulitis), decomposing clinical diagnosis into information gathering, interpretation, and differential diagnosis. We evaluated single agent systems (one model performing all tasks) against multi-agent systems (specialized models for each task) using comprehensive metrics spanning diagnostic outcomes, process adherence, and cost efficiency. Our results reveal a paradox: while multi-agent systems generally outperformed single agents, the component-optimized or Best of Breed system with superior components and excellent process metrics (85.5% information accuracy) significantly underperformed in diagnostic accuracy (67.7% vs. 77.4% for a top multi-agent system). This finding underscores that successful integration of AI in healthcare requires not just component level optimization but also attention to information flow and compatibility between agents. Our findings highlight the need for end to end system validation rather than relying on component metrics alone.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

47 extracted references · 47 canonical work pages

  1. [1]

    Melton, Mingquan Lin, and Rui Zhang

    Shuang Zhou, Zidu Xu, Mian Zhang, Chunpu Xu, Yawen Guo, Zaifu Zhan, Sirui Ding, Jiashuo Wang, Kaishuai Xu, Yi Fang, Liqiao Xia, Jeremy Yeung, Daochen Zha, Genevieve B. Melton, Mingquan Lin, and Rui Zhang. Large language models for disease diagnosis: A scoping review, 2024

    work page 2024

  2. [2]

    Large language models for therapy recommendations across 3 clinical specialties: Comparative study.Journal of Medical Internet Research, 25:e49324, Oct 2023

    Theresa Isabelle Wilhelm, Jonas Roos, and Robert Kaczmarczyk. Large language models for therapy recommendations across 3 clinical specialties: Comparative study.Journal of Medical Internet Research, 25:e49324, Oct 2023

    work page 2023

  3. [3]

    Jin Rui Edmund Neo, Joon Sin Ser, and San San Tay. Use of large language model-based chatbots in managing the rehabilitation concerns and education needs of outpatient stroke survivors and caregivers.Frontiers in Digital Health, 6:1395501, May 2024

    work page 2024

  4. [4]

    Roumeliotis, and Manoj Karkee

    Ranjan Sapkota, Konstantinos I. Roumeliotis, and Manoj Karkee. Ai agents vs. agentic ai: A conceptual taxonomy, applications and challenges, 2025

    work page 2025

  5. [5]

    Sara Mahdavi, Christopher Semturs, Juraj Gottweis, Joelle Barral, Katherine Chou, Greg S

    Tao Tu, Mike Schaekermann, Anil Palepu, Khaled Saab, Jan Freyberg, Ryutaro Tanno, Amy Wang, Brenna Li, Mohamed Amin, Yong Cheng, Elahe Vedadi, Nenad Tomasev, Shekoofeh Azizi, Karan Singhal, Le Hou, Albert Webson, Kavita Kulkarni, S. Sara Mahdavi, Christopher Semturs, Juraj Gottweis, Joelle Barral, Katherine Chou, Greg S. Corrado, Yossi Matias, and Vivek N...

    work page 2025

  6. [6]

    Coordinated ai agents for advancing healthcare.Nature Biomedical Engineering, 2025

    Michael Moritz, Eric Topol, and Pranav Rajpurkar. Coordinated ai agents for advancing healthcare.Nature Biomedical Engineering, 2025. Commentary

    work page 2025

  7. [7]

    Tran, Daniel I Schlessinger, Shannon Wongvibulsin, Zhuo Ran Cai, Roxana Daneshjou, and Pranav Rajpurkar

    Shreya Johri, Jaehwan Jeong, Benjamin A. Tran, Daniel I Schlessinger, Shannon Wongvibulsin, Zhuo Ran Cai, Roxana Daneshjou, and Pranav Rajpurkar. CRAFT-MD: A conversational evaluation framework for comprehensive assessment of clinical LLMs. InAAAI 2024 Spring Symposium on Clinical Foundation Models, 2024

    work page 2024

  8. [8]

    Ai agents help explain other ai systems, January 2024

    Rachel Gordon. Ai agents help explain other ai systems, January 2024. MIT News, Massachusetts Institute of Technology

    work page 2024

Show all 47 references
  1. [9]

    Unlocking complex problem-solving with multi-agent collaboration on amazon bedrock

    Raphael Shu, Yi Zhang, Michelle Yuan, Nilaksh Das, and Monica Sunkara. Unlocking complex problem-solving with multi-agent collaboration on amazon bedrock. https://aws.amazon.com/blogs/machine-learning/unlocking-complex-problem-solving-with-multi-agent-collaboration-on- amazon-...

    2025

  2. [10]

    Lance B. Eliot. Multi-agent ai orchestration shaping up but here’s why it might not be fully shipshape.Forbes, November 2024. Innovation, AI

    2024

  3. [11]

    Evaluation and mitigation of the limitations of large language models in clinical decision-making

    Paul Hager, Friederike Jungmann, Robbie Holland, Kunal Bhagat, Inga Hubrecht, Manuel Knauer, Jakob Vielhauer, Marcus Makowski, Rickmer Braren, Georgios Kaissis, and Daniel Rueckert. Evaluation and mitigation of the limitations of large language models in clinical decision-maki...

    2024

  4. [12]

    Macaluso and Robert M

    Christopher R. Macaluso and Robert M. McNamara. Evaluation and management of acute abdominal pain in the emergency department.International Journal of General Medicine, 5:789–797, 2012

    2012

  5. [13]

    Diagnosis and treatment of acute appendicitis: 2020 update of the wses jerusalem guidelines.World Journal of Emergency Surgery, 15(1):27, 2020

    Salomone Di Saverio, Mauro Podda, Belinda De Simone, Marco Ceresoli, Goran Augustin, Antonio Gori, Luca Ansaloni, Marja Boermeester, Massimo Sartelli, Federico Coccolini, and Fausto Catena. Diagnosis and treatment of acute appendicitis: 2020 update of the wses jerusalem guidel...

    2020

  6. [14]

    Epidemiology and outcomes of acute abdominal pain in a large urban emergency department: retrospective analysis of 5,340 cases.Emergency and Critical Care Medicine, 4(19):1–8, 2016

    Gianfranco Cervellin, Riccardo Mora, Andrea Ticinesi, Tiziana Meschi, Ivan Comelli, Fausto Catena, and Giuseppe Lippi. Epidemiology and outcomes of acute abdominal pain in a large urban emergency department: retrospective analysis of 5,340 cases.Emergency and Critical Care Med...

    2016

  7. [15]

    van Breda Vriesman and Julien B

    Adriaan C. van Breda Vriesman and Julien B. C. M. Puylaert. Mimics of appendicitis: Alternative nonsurgical diagnoses with sonography and ct. AJR American Journal of Roentgenology, 186(4):1103–1112, 2006

    2006

  8. [16]

    Diagnosis and treatment of acute appendicitis: 2020 update of the wses jerusalem guidelines.World Journal of Emergency Surgery, 15:27, 2020

    Salomone Di Saverio, Mauro Podda, Belinda De Simone, Marco Ceresoli, Goran Augustin, Antonio Gori, Luca Ansaloni, Marja Boermeester, Massimo Sartelli, Federico Coccolini, and Fausto Catena. Diagnosis and treatment of acute appendicitis: 2020 update of the wses jerusalem guidel...

    2020

  9. [17]

    Gomes, Andrew W

    Michele Pisano, Nadia Allievi, Kurinchi Gurusamy, Giovanni Borzellino, Carlos A. Gomes, Andrew W. Kirkpatrick, and et al. 2020 world society of emergency surgery updated guidelines for the diagnosis and treatment of acute calculus cholecystitis.World Journal of Emergency Surge...

    2020

  10. [18]

    Hall, Patricia L

    John F. Hall, Patricia L. Roberts, Ramon Ricciardi, Thomas E. Read, Benjamin R. Davis, Paul W. Marcello, and Scott R. Steele. The american society of colon and rectal surgeons clinical practice guidelines for the treatment of left-sided colonic diverticulitis.Diseases of the C...

    2020

  11. [19]

    2019 wses guidelines for the management of severe acute pancreatitis.World Journal of Emergency Surgery, 14:27, 2019

    Ari Leppäniemi, Matti Tolonen, Antonio Tarasconi, Gabriele Anania, Helena Segovia-Lohse, Edoardo Gamberini, and et al. 2019 wses guidelines for the management of severe acute pancreatitis.World Journal of Emergency Surgery, 14:27, 2019

    2019

  12. [20]

    Signs and syndromes in acute appendicitis: A pathophysiologic approach.World Journal of Gastrointestinal Surgery, 14(7):727–730, 2022

    Steven H Yale, Halil Tekiner, and Elizabeth S Yale. Signs and syndromes in acute appendicitis: A pathophysiologic approach.World Journal of Gastrointestinal Surgery, 14(7):727–730, 2022

    2022

  13. [21]

    Eponymous signs of acute cholecystitis – a review.Saudi Medical Journal Students, 3(2):44–55, 2023

    Sajad Ahmad Salati, Khalid Alkhalifah, and Abdul Majeed Salem Almousa. Eponymous signs of acute cholecystitis – a review.Saudi Medical Journal Students, 3(2):44–55, 2023

    2023

  14. [22]

    Colonic diverticulosis is not associated with painful abdominal symptoms in a us population.Gastro Hep Advances, 1(1):15–22, 2022

    Anne F Peery, Temitope O Keku, Joseph A Galanko, and Robert S Sandler. Colonic diverticulosis is not associated with painful abdominal symptoms in a us population.Gastro Hep Advances, 1(1):15–22, 2022. Bedi et al

    2022

  15. [23]

    American college of gastroenterology guideline: management of acute pancreatitis

    Scott Tenner, John Baillie, John DeWitt, and Santhi Swaroop Vege. American college of gastroenterology guideline: management of acute pancreatitis. The American Journal of Gastroenterology, 108(9):1400–1415, 2013

    2013

  16. [24]

    test_name

    Centers for Medicare & Medicaid Services. CY 2025 Q2 Release: Added for April 2025. The update includes all changes identified in CR 13966. https://www.cms.gov/medicare/payment/clinical-laboratory-fee-schedule/clfs-quarterly-update-files, April 2025. File name: 25CLABQ2. Conta...

    2025

  17. [25]

    <most likely diagnosis>

  18. [26]

    <second most likely diagnosis>

  19. [27]

    <third most likely diagnosis>

  20. [28]

    <fourth most likely diagnosis>

  21. [29]

    Do NOT request any further actions or tools

    <fifth most likely diagnosis> Treatment: <detailed evidence-based treatment plan> **IMPORTANT: After providing FORMAT 2, your task is COMPLETE. Do NOT request any further actions or tools. FORMAT 2 is the FINAL output. Once you provide FORMAT 2, the conversation ENDS.** ------...

  22. [30]

    Lab Interpretation

    **Mandatory Lab Interpretation** •If the last message you received is a Tool output with lab data, you MUST include the “Lab Interpretation” JSON block. •If you omit it, your answer will be rejected and you will be asked to try again

  23. [35]

    Do NOT ask for any more information or tools after FORMAT 2

    **STOP AFTER FORMAT 2:** Once you have provided FORMAT 2 (Final Diagnosis and Treatment), you MUST stop. Do NOT ask for any more information or tools after FORMAT 2

  24. [36]

    WBC": {"value

    Stop asking for additional information when you are confident enough to provide FORMAT 2. ------------------------------------------------------------------------ EXAMPLES Lab Interpretation: { "WBC": {"value": 12.5, "interpretation": "high"}, "CRP": {"value": 5.0, "interpreta...

  25. [37]

    Do NOT mix elements from different formats

  26. [38]

    Action Input

    “Action Input” is **only** for naming new tests or imaging studies you want to order. Never place results or interpretations there

  27. [39]

    Action Input

    **Action Input Content:** The "Action Input" field should ONLY contain a comma-separated list of test names, imaging studies, or physical exam maneuvers. Do NOT include any thoughts, reasoning, interpretations, or other text in the "Action Input" field

  28. [40]

    Do NOT ask for any more information or tools after FORMAT 2

    **STOP AFTER FORMAT 2:** Once you have provided FORMAT 2, you MUST stop. Do NOT ask for any more information or tools after FORMAT 2

  29. [41]

    "" # INFORMATION INTERPRETATION AGENT INTERPRETATION_PROMPT =

    Stop asking for additional information when you are confident enough to provide FORMAT 2. """ # INFORMATION INTERPRETATION AGENT INTERPRETATION_PROMPT = """\ You are a medical-AI assistant helping a physician interpret laboratory results that have already been retrieved. **Alw...

  30. [42]

    Lab Interpretation

    **Mandatory Lab Interpretation** •If the last message you received is a Tool output with lab data, you MUST include the “Lab Interpretation” JSON block •If you omit it, your answer will be rejected and you will be asked to try again

  31. [43]

    high”, “normal

    JSON validity •The Lab Interpretation block must be valid JSON (double quotes, no trailing commas). •Include both the numeric value and the interpretation (“high”, “normal”, or “low”) for every test you mention

  32. [44]

    "" Fig. 5. Prompts used for the multi-agent systems. The Optimization Paradox in Clinical AI Multi-Agent Systems Retriever LLM: LABS_MATCHER_PROMPT =

    Do NOT mix elements from different formats. """ Fig. 5. Prompts used for the multi-agent systems. The Optimization Paradox in Clinical AI Multi-Agent Systems Retriever LLM: LABS_MATCHER_PROMPT = """ Available laboratory tests and their results: {available_tests}. Requested tes...

  33. [45]

    Action: Physical Examination Action Input: Abdominal tenderness, McBurney's point

    Information Gathering Agent: "Action: Physical Examination Action Input: Abdominal tenderness, McBurney's point"

  34. [46]

    RetrieveResults Tool: Returns physical exam findings

  35. [47]

    Action: Laboratory Tests Action Input: Complete blood count, C-reactive protein

    Information Gathering Agent: "Action: Laboratory Tests Action Input: Complete blood count, C-reactive protein"

  36. [48]

    RetrieveResults Tool: Returns lab values

  37. [49]

    WBC": {"value

    Information Interpretation Agent: Processes lab results as {"WBC": {"value": 15000, "interpretation": "high"}}

  38. [50]

    Action: done

    Information Gathering Agent: "Action: done"

  39. [51]

    Differential Diagnosis Agent: Generates final ranked diagnosis Fig. 7. Sequential processing of a patient case demonstrating the flow from information gathering through data retrieval, interpretation, and final diagnosis for acute abdominal pain. The system incorporated robust...

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Reviewed August 7, 2026 · model on record in the stance chip above.