Can Large Language Models Match the Conclusions of Systematic Reviews?

• URL: http://arxiv.org/abs/2505.22787v1
• Date: Wed, 28 May 2025 18:58:09 GMT
• Title: Can Large Language Models Match the Conclusions of Systematic Reviews?
• Abstract summary: We ask: Can large language models (LLMs) match the conclusions of systematic reviews written by clinical experts when given access to the same studies?<n>We benchmark 24 LLMs on MedEvidence, including reasoning, non-reasoning, medical specialist, and models across varying sizes (from 7B-700B).<n>We find that reasoning does not necessarily improve performance, larger models do not consistently yield greater gains, and knowledge-based fine-tuning degrades accuracy on MedEvidence.
• Score: 43.27182445778988
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Systematic reviews (SR), in which experts summarize and analyze evidence across individual studies to provide insights on a specialized topic, are a cornerstone for evidence-based clinical decision-making, research, and policy. Given the exponential growth of scientific articles, there is growing interest in using large language models (LLMs) to automate SR generation. However, the ability of LLMs to critically assess evidence and reason across multiple documents to provide recommendations at the same proficiency as domain experts remains poorly characterized. We therefore ask: Can LLMs match the conclusions of systematic reviews written by clinical experts when given access to the same studies? To explore this question, we present MedEvidence, a benchmark pairing findings from 100 SRs with the studies they are based on. We benchmark 24 LLMs on MedEvidence, including reasoning, non-reasoning, medical specialist, and models across varying sizes (from 7B-700B). Through our systematic evaluation, we find that reasoning does not necessarily improve performance, larger models do not consistently yield greater gains, and knowledge-based fine-tuning degrades accuracy on MedEvidence. Instead, most models exhibit similar behavior: performance tends to degrade as token length increases, their responses show overconfidence, and, contrary to human experts, all models show a lack of scientific skepticism toward low-quality findings. These results suggest that more work is still required before LLMs can reliably match the observations from expert-conducted SRs, even though these systems are already deployed and being used by clinicians. We release our codebase and benchmark to the broader research community to further investigate LLM-based SR systems.

Related papers

• Reinforcement Learning for Evidence-Seeking Diagnostic Reasoning with Large Language Models [35.9432019263596]
  We formalize medical diagnosis as an Iterative Evidence-Seeking Task.<n>We leverage Reinforcement Learning with Verifiable Rewards (RLVR) to elicit intrinsic reasoning within a closed-loop environment.<n>We introduce the Retrieval-Augmented Generation-based Examination Simulator (RAGES), a high-fidelity clinical oracle that provides realistic, knowledge-grounded follow-up evidence.
  arXiv  Detail & Related papers  (2026-07-03T05:43:00Z)
• IV Co-Scientist: Multi-Agent LLM Framework for Causal Instrumental Variable Discovery [61.15184885636171]
  In the presence of confounding between an endogenous variable and the outcome, instrumental variables (IVs) are used to isolate the causal effect of the endogenous variable.<n>We investigate whether large language models (LLMs) can aid in this task.<n>We introduce IV Co-Scientist, a multi-agent system that proposes, critiques, and refines IVs for a given treatment-outcome pair.
  arXiv  Detail & Related papers  (2026-02-08T12:28:29Z)
• FIRE-Bench: Evaluating Agents on the Rediscovery of Scientific Insights [63.32178443510396]
  We introduce FIRE-Bench (Full-cycle Insight Rediscovery Evaluation), a benchmark that evaluates agents through the rediscovery of established findings.<n>Even the strongest agents achieve limited rediscovery success (50 F1), exhibit high variance across runs, and display recurring failure modes in experimental design, execution, and evidence-based reasoning.
  arXiv  Detail & Related papers  (2026-02-02T23:21:13Z)
• MedTutor: A Retrieval-Augmented LLM System for Case-Based Medical Education [34.47698387053728]
  MedTutor is a system designed to augment resident training by automatically generating evidence-based educational content and multiple-choice questions from clinical case reports.<n>The system's architecture features a hybrid retrieval mechanism that queries a local knowledge base of medical textbooks and academic literature.<n>Three radiologists assessed the quality of outputs, finding them to be of high clinical and educational value.
  arXiv  Detail & Related papers  (2026-01-11T16:27:21Z)
• Evaluating Large Language Models in Scientific Discovery [91.732562776782]
  Large language models (LLMs) are increasingly applied to scientific research, yet prevailing science benchmarks probe decontextualized knowledge.<n>We introduce a scenario-grounded benchmark that evaluates LLMs across biology, chemistry, materials, and physics.<n>The framework assesses models at two levels: (i) question-level accuracy on scenario-tied items and (ii) project-level performance.
  arXiv  Detail & Related papers  (2025-12-17T16:20:03Z)
• Dr.Mi-Bench: A Modular-integrated Benchmark for Scientific Deep Research Agent [52.876617746453995]
  Dr.Mi-Bench is a Modular-integrated benchmark for scientific deep research (DR) agents.<n>Dr.Mi-Eval is a novel modular-integrated evaluation paradigm.
  arXiv  Detail & Related papers  (2025-11-30T17:16:47Z)
• LLM4SCREENLIT: Recommendations on Assessing the Performance of Large Language Models for Screening Literature in Systematic Reviews [2.2175470459999636]
  We identify problems with the use of traditional metrics for assessing the performance of Gen-AI tools for identifying relevant literature in systematic reviews.<n>Major weaknesses included a failure to use metrics that are robust to imbalanced data and do not directly indicate whether results are better than chance.<n>On the positive side, we extract good (evaluation) practices on which our recommendations for researchers and practitioners, as well as policymakers, are built.
  arXiv  Detail & Related papers  (2025-11-16T15:04:50Z)
• Beyond Overall Accuracy: A Psychometric Deep Dive into the Topic-Specific Medical Capabilities of 80 Large Language Models [6.362188639024662]
  We introduce textscMedIRT, a rigorous evaluation framework grounded in Item Response Theory (IRT)<n>We prospectively gathered fresh responses from 80 diverse Large Language Models (LLMs) on a balanced, 1,100-question USMLE-aligned benchmark.<n>We estimate LLM's latent model ability jointly with question difficulty and discrimination, yielding more stable and nuanced performance rankings than accuracy alone.
  arXiv  Detail & Related papers  (2025-09-29T02:06:13Z)
• Medical Reasoning in the Era of LLMs: A Systematic Review of Enhancement Techniques and Applications [59.721265428780946]
  Large Language Models (LLMs) in medicine have enabled impressive capabilities, yet a critical gap remains in their ability to perform systematic, transparent, and verifiable reasoning.<n>This paper provides the first systematic review of this emerging field.<n>We propose a taxonomy of reasoning enhancement techniques, categorized into training-time strategies and test-time mechanisms.
  arXiv  Detail & Related papers  (2025-08-01T14:41:31Z)
• Can AI Validate Science? Benchmarking LLMs for Accurate Scientific Claim $\ ightarrow$ Evidence Reasoning [6.043212666944194]
  We present CLAIM-BENCH, a benchmark for evaluating large language models' capabilities in scientific claim-evidence extraction and validation.<n>We show that closed-source models like GPT-4 and Claude consistently outperform open-source counterparts in precision and recall.<n> strategically designed three-pass and one-by-one prompting approaches significantly improve LLMs' abilities to accurately link dispersed evidence with claims.
  arXiv  Detail & Related papers  (2025-06-09T21:04:39Z)
• Towards Artificial Intelligence Research Assistant for Expert-Involved Learning [64.7438151207189]
  Large Language Models (LLMs) and Large Multi-Modal Models (LMMs) have emerged as transformative tools in scientific research.<n>We present textbfARtificial textbfIntelligence research assistant for textbfExpert-involved textbfLearning (ARIEL)
  arXiv  Detail & Related papers  (2025-05-03T14:21:48Z)
• Quantifying the Reasoning Abilities of LLMs on Real-world Clinical Cases [48.87360916431396]
  We introduce MedR-Bench, a benchmarking dataset of 1,453 structured patient cases, annotated with reasoning references.<n>We propose a framework encompassing three critical examination recommendation, diagnostic decision-making, and treatment planning, simulating the entire patient care journey.<n>Using this benchmark, we evaluate five state-of-the-art reasoning LLMs, including DeepSeek-R1, OpenAI-o3-mini, and Gemini-2.0-Flash Thinking, etc.
  arXiv  Detail & Related papers  (2025-03-06T18:35:39Z)
• EvidenceMap: Learning Evidence Analysis to Unleash the Power of Small Language Models for Biomedical Question Answering [29.70354593617791]
  We propose EvidenceMap to enable a tiny pre-trained language model to explicitly learn multiple aspects of biomedical evidence.<n>Our method, learning evidence analysis by fine-tuning a model with only 66M parameters, exceeds the RAG method with an 8B LLM by 19.9% and 5.7% in reference-based quality and accuracy, respectively.
  arXiv  Detail & Related papers  (2025-01-22T09:27:11Z)
• Limitations of Automatic Relevance Assessments with Large Language Models for Fair and Reliable Retrieval Evaluation [2.9180406633632523]
  Large language models (LLMs) are gaining much attention as tools for automatic relevance assessment.<n>Recent research has shown that LLM-based assessments yield high systems ranking correlation with human-made judgements.<n>We look at how LLM-generated judgements preserve ranking differences among top-performing systems and also how they preserve pairwise significance evaluation as human judgements.
  arXiv  Detail & Related papers  (2024-11-20T11:19:35Z)
• Comprehensive and Practical Evaluation of Retrieval-Augmented Generation Systems for Medical Question Answering [70.44269982045415]
  Retrieval-augmented generation (RAG) has emerged as a promising approach to enhance the performance of large language models (LLMs) We introduce Medical Retrieval-Augmented Generation Benchmark (MedRGB) that provides various supplementary elements to four medical QA datasets. Our experimental results reveals current models' limited ability to handle noise and misinformation in the retrieved documents.
  arXiv  Detail & Related papers  (2024-11-14T06:19:18Z)
• Do Large Language Models Exhibit Cognitive Dissonance? Studying the Difference Between Revealed Beliefs and Stated Answers [13.644277507363036]
  We introduce Revealed Belief, an evaluation framework that evaluates Large Language Models (LLMs) on tasks requiring reasoning under uncertainty.<n>Our findings suggest that while LLMs frequently state the correct answer, their Revealed Belief shows that they often allocate probability mass inconsistently, exhibit systematic biases, and often fail to update their beliefs appropriately when presented with new evidence.
  arXiv  Detail & Related papers  (2024-06-21T08:56:35Z)
• Discovery of the Hidden World with Large Language Models [95.58823685009727]
  This paper presents Causal representatiOn AssistanT (COAT) that introduces large language models (LLMs) to bridge the gap. LLMs are trained on massive observations of the world and have demonstrated great capability in extracting key information from unstructured data. COAT also adopts CDs to find causal relations among the identified variables as well as to provide feedback to LLMs to iteratively refine the proposed factors.
  arXiv  Detail & Related papers  (2024-02-06T12:18:54Z)
• Evaluating the Effectiveness of Retrieval-Augmented Large Language Models in Scientific Document Reasoning [0.0]
  Large Language Model (LLM) often provide seemingly plausible but not factual information, often referred to as hallucinations. Retrieval-augmented LLMs provide a non-parametric approach to solve these issues by retrieving relevant information from external data sources. We critically evaluate these models in their ability to perform in scientific document reasoning tasks.
  arXiv  Detail & Related papers  (2023-11-07T21:09:57Z)
• Exploring the Cognitive Knowledge Structure of Large Language Models: An Educational Diagnostic Assessment Approach [50.125704610228254]
  Large Language Models (LLMs) have not only exhibited exceptional performance across various tasks, but also demonstrated sparks of intelligence. Recent studies have focused on assessing their capabilities on human exams and revealed their impressive competence in different domains. We conduct an evaluation using MoocRadar, a meticulously annotated human test dataset based on Bloom taxonomy.
  arXiv  Detail & Related papers  (2023-10-12T09:55:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.