## 4 Discussion
Evaluations show that Med-PaLM significantly improves the factual alignment with scientific consensus, reduces the rate of harmful responses, and achieves a high-level performance on expert medical benchmarks such as MedQA. These findings suggest that instruction prompt tuning is a promising methodology for improving the safety, reliability, and clinical utility of LLMs in healthcare applications [79]. In a related effort to enhance performance in the medical domain, PMC-LLaMA introduces a data-centric fine-tuning framework that adapts general-purpose language models for clinical applications. It is trained on a large corpus comprising 4.8 million biomedical academic papers and 30,000 medical textbooks, enabling it to develop a deeper understanding of domain-specific terminology and clinical reasoning. The fine-tuning process includes instruction tuning with 202 million tokens covering medical question answering, rationale generation, and multi-turn dialogs. It improves factual accuracy and aligns with medical standards through curated, domain-specific instruction data. Moreover, PMC-LLaMA emphasizes modeling complex conversational patterns and includes systematic ablation studies to assess the impact of training components. Despite its relatively compact size (13 billion parameters), the model outperforms larger systems such as ChatGPT on public benchmarks [80]. Ethical and privacy concerns surrounding the use of NLP models are increasingly critical. Issues regarding data privacy, informed consent, algorithmic bias, and the potential misuse of data raise serious questions about responsible implementation. LLMs pose security risks, enabling side-channel attacks, phishing, and malware creation. They can leak private data and spread misinformation. Vulnerabilities like data poisoning make them more exploitable. Tools like FraudGPT and WormGPT show their potential misuse in cybercrime [81; 82]. While LLMs have been criticized for their bias, hallucination, and misinformation, these concerns are often overstated or misunderstood. Bias exists in all human systems, and LLMs can actually help identify and reduce it when trained responsibly. Misinformation risks can be mitigated through fine-tuning, transparency, and ethical use [82]. To support LLM development without compromising privacy, data regulations should be more flexible using a tiered data risk model: public, internal, confidential, and restricted. Transparency, patient consent, and education on data rights are key. Addressing IP and data provenance requires licensed datasets, watermarking, and blockchains. Bias, hallucinations, and model reproducibility must be managed through evaluations, monitoring, and regulatory sandboxes [83]. In clinical environments where patient profiles and data acquisition methods continuously evolve, drift detection must be both proactive and robust to maintain AI model accuracy and fairness [84]. Modern systems use automated drift detection with ongoing performance checks, comparing metrics like accuracy and recall to set thresholds to flag drift [85]. Healthcare AI faces added challenges such as high-dimensional data, delayed ground truth, and clinical interventions. An effective re-validation requires coordination among clinicians, data scientists, and regulators to set performance standards. Tools like Shewhart and CUSUM charts help track changes and detect performance shifts [84]. Advanced drift detection techniques such as ensemble-based methods are effective in handling sudden, incremental, and recurring drift. These models adjust instance weights and parameters to maintain performance, as seen in triage systems where patient symptoms evolve over time [86]. Distinguishing between real and virtual concept drift is essential. Real drift changes the model's decision boundary, while virtual drift affects input distributions without altering the decision logic [87]. Meta-learning and reinforcement learning enhance re-validation by enabling adaptive update strategies.