Mitigating Hallucinations in Healthcare LLMs with Granular Fact-Checking and Domain-Specific Adaptation
arXiv:2512.16189v3 Announce Type: replace Abstract: In healthcare, it is essential for any LLM-generated output to be reliable and accurate, particularly in cases involving decision-making and patient safety. However, the outputs are often unreliable in such critical areas due to the risk of hallucinated outputs from the LLMs. To address this issue, we propose a fact-checking module that operates independently of any LLM, along with a domain-specific summarization model designed to minimize hallucination rates. Our model is fine-tuned using Low-Rank Adaptation (LoRa) on the MIMIC III dataset a
The proliferation of LLMs into critical sectors like healthcare, coupled with persistent hallucination challenges, necessitates novel mitigation strategies as adoption accelerates.
Ensuring reliability and accuracy in AI-generated content is paramount for sectors with high stakes like healthcare, directly impacting patient safety and clinical decision-making.
The development of independent, granular fact-checking and domain-specific adaptation techniques provides a clearer path to making LLMs trustworthy for critical applications, shifting focus from raw generative power to verifiable outputs.
- · Healthcare AI solution providers
- · Patients
- · AI ethics and safety researchers
- · Developers of fact-checking technologies
- · Generic LLMs without robust verification
- · Healthcare providers relying solely on unverified AI outputs
- · Companies neglecting AI safety protocols
This research directly improves the safety and trustworthiness of AI applications in healthcare.
Increased trust in AI could accelerate adoption of LLMs in other high-stakes domains such as finance or legal sectors.
The methodology could establish a new standard for AI deployment, requiring independent verification modules for all critical applications, leading to a more regulated AI ecosystem.
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Read at arXiv cs.CL