Paper Citation Record · LEDGER

Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation

As of 14 August 2026, this Paper Citation Record lists 63 of 63 outbound references and 2 inbound Pith citation observations for arXiv:2512.16189.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2512.16189 v4

Coverage vector

measured 63 of 63 reference resolution

Typed states for the displayed outbound observations.

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measured 65 of 65 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T10:40:04.488059Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-10T00:19:46.872557Z

Reference resolution

63 of 63 outbound references displayed

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External citation measurements

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Outbound references

Observation 137b9832-e447-426b-8290-3cd6d7da29f7 · outbound

This paper cites Roles and potential of large language models in healthcare: A comprehensive review,.

Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation Roles and potential of large language models in healthcare: A comprehensive review,

Reference 1

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Observation 052bd0b5-d94c-4e3f-8709-ec92cf2597ab · outbound

This paper cites Bioknowprompt: Incorporating imprecise knowledge into prompt-tuning verbalizer with biomedical text for relation extraction,.

Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation Bioknowprompt: Incorporating imprecise knowledge into prompt-tuning verbalizer with biomedical text for relation extraction,

Reference 2

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Observation 5f8304e6-db95-4125-8a0a-a8eeeca75a58 · outbound

This paper cites Medical hallucinations in foundation models and their impact on healthcare,.

Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation Medical hallucinations in foundation models and their impact on healthcare,

Reference 3

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Observation 3d92b693-de64-4f6b-8e2d-56052053ac58 · outbound

This paper cites Uncertainty-aware multi- criteria decision analysis for evaluation of explainable ar- tificial intelligence methods: A use case from the health- care domain,.

Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation Uncertainty-aware multi- criteria decision analysis for evaluation of explainable ar- tificial intelligence methods: A use case from the health- care domain,

Reference 4

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Observation 8a696284-8b2a-4c6d-9a47-bc42f263ade4 · outbound

This paper cites Mcd-ears: A multi- modal cross-domain expertise-aware recommender sys- tem for healthcare applications,.

Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation Mcd-ears: A multi- modal cross-domain expertise-aware recommender sys- tem for healthcare applications,

Reference 5

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Observation 2ac5969a-d385-4b85-9672-2c717f8da153 · outbound

This paper cites Scene generalization for biomedical fact verifi- cation via hierarchical mixture of experts,.

Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation Scene generalization for biomedical fact verifi- cation via hierarchical mixture of experts,

Reference 6

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Observation 1baf479b-02fa-4c8d-899a-31a1f43127ff · outbound

This paper cites A Survey of Hallucination in Large Foundation Models.

Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation A Survey of Hallucination in Large Foundation Models

Reference 7

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Observation 677347a8-c375-4836-8d5a-b5a7e72674d5 · outbound

This paper cites On Faithfulness and Factuality in Abstractive Summarization.

Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation On Faithfulness and Factuality in Abstractive Summarization

Reference 8

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This paper cites Prescrib- ing the right remedy: Mitigating hallucinations in large vision-language models via targeted instruction tuning,.

Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation Prescrib- ing the right remedy: Mitigating hallucinations in large vision-language models via targeted instruction tuning,

Reference 9

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Observation 57fc821c-e8a6-4c9e-944b-df5e7f32b103 · outbound

This paper cites Virtsi: A novel trust dynamics model enhancing artifi- cial intelligence collaboration with human users–insights from a chatgpt evaluation study,.

Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation Virtsi: A novel trust dynamics model enhancing artifi- cial intelligence collaboration with human users–insights from a chatgpt evaluation study,

Reference 10

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Observation 4c7ef403-d532-466b-b40a-93cbb6bffbd9 · outbound

This paper cites Med-HALT: Medical Domain Hallucination Test for Large Language Models.

Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation Med-HALT: Medical Domain Hallucination Test for Large Language Models

Reference 11

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Observation 7bc36fb3-e3c6-4fc4-ba1d-6d530a7e9bf7 · outbound

This paper cites Generative ai hallucinations in health- care: A challenge for prompt engineering and creativity,.

Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation Generative ai hallucinations in health- care: A challenge for prompt engineering and creativity,

Reference 12

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Observation c25de580-7472-4fbb-b8cc-e9038d1d2423 · outbound

This paper cites Embracing large lan- guage models for medical applications: opportunities and challenges,.

Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation Embracing large lan- guage models for medical applications: opportunities and challenges,

Reference 13

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Observation cb3f40a9-fbdc-4599-a86f-9c30bcc44a33 · outbound

This paper cites Large language models encode clinical knowledge,.

Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation Large language models encode clinical knowledge,

Reference 14

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Observation 0eeb3796-7b06-4d9d-ba52-b4cd1093069f · outbound

This paper cites ClinicalGPT: Large Language Models Finetuned with Diverse Medical Data and Comprehensive Evaluation.

Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation ClinicalGPT: Large Language Models Finetuned with Diverse Medical Data and Comprehensive Evaluation

Reference 15

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Observation a5e97f7e-aa29-4c70-b999-414d944b72ed · outbound

This paper cites Evaluating large language models on medical evidence summarization,.

Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation Evaluating large language models on medical evidence summarization,

Reference 16

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Observation c125e36a-b320-4e0d-a5f3-52f028f14c83 · outbound

This paper cites Opportunities, chal- lenges, and future directions of large language models, including chatgpt in medical education: a systematic scoping review,.

Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation Opportunities, chal- lenges, and future directions of large language models, including chatgpt in medical education: a systematic scoping review,

Reference 17

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Observation 3f1cece9-d080-412f-86aa-618fe93c6843 · outbound

This paper cites Trustworthy AI for Medicine: Continuous Hallucination Detection and Elimination with CHECK.

Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation Trustworthy AI for Medicine: Continuous Hallucination Detection and Elimination with CHECK

Reference 18

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Observation 74a09b21-612c-4e1c-b741-14e83e89a0fd · outbound

This paper cites Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.

Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

Reference 19

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Observation 3e6d87ee-8e83-40b7-97c0-47ab12921306 · outbound

This paper cites Retrieval augmentation reduces hallucination in conversation,.

Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation Retrieval augmentation reduces hallucination in conversation,

Reference 20

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Observation c16fa803-3383-410f-b2ea-cfb34fa400f0 · outbound

This paper cites Survey of halluci- nation in natural language generation,.

Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation Survey of halluci- nation in natural language generation,

Reference 21

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This paper cites Mimic-iii, a freely accessi- ble critical care database,.

Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation Mimic-iii, a freely accessi- ble critical care database,

Reference 23

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Observation ca5d83b0-ee18-40d4-8c49-8052969d1c96 · outbound

This paper cites Lora: Low-rank adaptation of large language models.

Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation Lora: Low-rank adaptation of large language models

Reference 24

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Observation a2f001fe-cc40-4c45-a31c-abdfdc673794 · outbound

This paper cites FactPICO: Factuality Evaluation for Plain Language Summarization of Medical Evidence.

Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation FactPICO: Factuality Evaluation for Plain Language Summarization of Medical Evidence

Reference 25

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Observation d5abf19a-ecf1-4ba9-8f69-d7309701251f · outbound

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Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation HealthFC: Verifying Health Claims with Evidence-Based Medical Fact-Checking

Reference 26

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Observation e7b751b0-2b0c-4fc3-8216-3f9125a70099 · outbound

This paper cites MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documents.

Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documents

Reference 27

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Observation 07a6996e-5842-410d-acc9-8a4f35aa81ca · outbound

This paper cites GraphCheck: Breaking Long-Term Text Barriers with Extracted Knowledge Graph-Powered Fact-Checking.

Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation GraphCheck: Breaking Long-Term Text Barriers with Extracted Knowledge Graph-Powered Fact-Checking

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-03T15:42:27.080073Z

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Observation 2ac1fe34-91f6-4f0e-8e12-eed8a79cba36 · outbound

This paper cites DOSSIER: Fact checking in electronic health records while preserving patient privacy,.

Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation DOSSIER: Fact checking in electronic health records while preserving patient privacy,

Reference 29

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Observation 4bcd3bbf-61a9-4006-bec4-987f5b6d38ae · outbound

This paper cites Brainllama at semeval-2024 task 6: Prompt- ing llama to detect hallucinations and related observ- able overgeneration mistakes,.

Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation Brainllama at semeval-2024 task 6: Prompt- ing llama to detect hallucinations and related observ- able overgeneration mistakes,

Reference 30

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Observation 8c59a1af-f1a8-4830-ac19-53fcae6d1129 · outbound

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Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation Gpt hallucination detection through prompt engineering,

Reference 31

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Observation 3f961a94-3131-4fc0-b049-da6b280c5f5a · outbound

This paper cites Factselfcheck: Fact-level black- box hallucination detection for llms,.

Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation Factselfcheck: Fact-level black- box hallucination detection for llms,

Reference 32

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Observation 427d36e9-b8ec-43bb-9b31-939748098fee · outbound

This paper cites Zero-knowledge llm hallucination detection and mitigation through fine-grained cross-model consistency,.

Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation Zero-knowledge llm hallucination detection and mitigation through fine-grained cross-model consistency,

Reference 33

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This paper cites Counterfactual Probing for Hallucination Detection and Mitigation in Large Language Models.

Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation Counterfactual Probing for Hallucination Detection and Mitigation in Large Language Models

Reference 34

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This paper cites Large language models in health care: Development, applications, and challenges,.

Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation Large language models in health care: Development, applications, and challenges,

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Observation 6396faf8-9ad8-4286-94ec-d08842517c3f · outbound

This paper cites Fact-and-Reflection (FaR) Improves Confidence Calibration of Large Language Models.

Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation Fact-and-Reflection (FaR) Improves Confidence Calibration of Large Language Models

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Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation Ethical and regulatory challenges of large language models in medicine,

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Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation A Data-Centric Approach To Generate Faithful and High Quality Patient Summaries with Large Language Models

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Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation Deploy llama 3 8b with vllm,

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Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation Benchmarking llm inference back- ends,

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Observation 90d2dc27-9a20-4d31-8360-85c83bb6d57a · outbound

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Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation Evidencemap: a three-level knowledge representation for medical evidence computation and comprehension,

Reference 41

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Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation Automated Fact Checking: Task formulations, methods and future directions

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Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation The perils and promises of fact-checking with large language models,

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Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation Scientific claim verification with fine- tuned nli models,

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Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation Large language models in medicine,

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Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models

Reference 46

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Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation Generative Large Language Models in Automated Fact-Checking: A Survey

Reference 47

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Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation A survey on hallucination in large language models: Prin- ciples, taxonomy, challenges, and open questions,

Reference 48

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Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation Overview of the mediqa-corr 2024 shared task on medical error detection and correction,

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Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation Bridg- ing pre-trained models to continual learning: A hyper- network based framework with parameter-efficient fine- tuning techniques,

Reference 50

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Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation Bioportal: ontologies and in- tegrated data resources at the click of a mouse,

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-03T15:42:28.935462Z

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Observation 241cb15a-6e5c-4ae2-b57a-bfc38133b430 · outbound

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Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation Improving natural language arguments’ identification by leveraging semantic similarity,

Reference 52

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Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation Negation scope detection for sentiment analysis: A re- inforcement learning framework for replicating human interpretations,

Reference 53

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Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation Mimic-iii, a freely accessible critical care database,

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-03T15:42:29.153925Z

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Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation Releasing claude instant 1.2,

Reference 55

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Observation ffd1c010-540b-4a31-b566-7a482a1b4923 · outbound

This paper cites Claude 2,.

Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation Claude 2,

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-03T15:42:29.275078Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34798110-d13b-4ca9-a55c-f30275ed9d0b · outbound

This paper cites Code Llama: Open Foundation Models for Code.

Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation Code Llama: Open Foundation Models for Code

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-03T15:42:29.345281Z

Source-reported events for the cited work

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Observation 790d07be-fbbe-468b-b54e-40f156fc9a91 · outbound

This paper cites MedAlpaca -- An Open-Source Collection of Medical Conversational AI Models and Training Data.

Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation MedAlpaca -- An Open-Source Collection of Medical Conversational AI Models and Training Data

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-03T15:42:29.445366Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 39a0885d-ba6c-4071-87ca-d8fe9da87780 · outbound

This paper cites Clinical Camel: An Open Expert-Level Medical Language Model with Dialogue-Based Knowledge Encoding.

Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation Clinical Camel: An Open Expert-Level Medical Language Model with Dialogue-Based Knowledge Encoding

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-03T15:42:29.515722Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c48740c4-73a2-48ab-803f-3c1e403cc12e · outbound

This paper cites Ehrsql: A practical text-to-sql benchmark for electronic health records,.

Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation Ehrsql: A practical text-to-sql benchmark for electronic health records,

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-03T15:42:29.574157Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 429a4952-31b6-4e5f-92f5-0b47ce9c74e4 · outbound

This paper cites Publicly Shareable Clinical Large Language Model Built on Synthetic Clinical Notes.

Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation Publicly Shareable Clinical Large Language Model Built on Synthetic Clinical Notes

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-03T15:42:29.656537Z

Source-reported events for the cited work

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Observation 7e64c5c5-b3a5-4973-b5e5-c0746c0eff90 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-03T15:42:29.744060Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4598ac43-b4e8-44fa-b21b-4aa0e35e306b · outbound

This paper cites [Online].

Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation [Online]

Reference 252

Resolution: unresolved; no resolver link, observed 2026-08-03T15:42:27.266237Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b06710ac-4c25-4cba-9ce2-ddba53f37bd9 · outbound

This paper cites 3784–3803.

Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation 3784–3803

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-03T15:42:26.474477Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 40e95701-5e12-4901-bd4c-bb77c1d5bdfe · inbound

Serialisation Strategy Matters: How FHIR Data Format Affects LLM Medication Reconciliation cites this paper.

Serialisation Strategy Matters: How FHIR Data Format Affects LLM Medication Reconciliation Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-26T02:03:06.972910Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 32ec17e5-e434-43a6-abbe-ae349aa6ac22 · inbound

Interpretable Language Model for Closed-Loop Type 1 Diabetes Control cites this paper.

Interpretable Language Model for Closed-Loop Type 1 Diabetes Control Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-02T10:40:04.488059Z

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Unavailable: canonical work link unavailable.