Large Language Models (LLMs) have emerged as powerful tools in various domains, including healthcare, where their potential to encode clinical knowledge and assist in decisionmaking is being actively explored. Despite advancements, significant gaps remain in their application to medicine, including issues with comprehension, bias, and dynamic knowledge integration.

This paper identifies these challenges and proposes research directions for enhancing LLMs' alignment with clinical needs. Key areas include continual learning frameworks, fairness and equity mechanisms, and collaborative decisionsupport systems, aiming to bridge the gap between artificial intelligence and realworld medical practice.

Over the last decade, LLMs have transformed natural language processing (NLP), enabling advanced applications in diverse fields. In healthcare, the integration of LLMs promises to revolutionize clinical decisionmaking, knowledge dissemination, and patient interaction.

However, the complex and dynamic nature of the medical domain poses unique challenges. A recent study published in Nature highlights both the potential and limitations of LLMs like FlanPaLM and MedPaLM, particularly in medical questionanswering tasks.

This paper builds upon these findings, identifying research gaps and proposing actionable strategies to address them.

The MultiMedQA benchmark combines multiple datasets to evaluate the performance of LLMs on tasks ranging from professional medical exams to consumer queries. While models like MedPaLM have demonstrated nearhuman accuracy in specific domains, limitations persist.

• Reasoning Deficits:
  Current models excel in structured tasks but often falter in nuanced reasoning and complex decisionmaking.
• Safety Risks:
  Instances of harmful or misleading outputs underscore the need for rigorous evaluation frameworks.
• Bias and Equity:
  Disparities in performance across demographic groups highlight inherent biases in training datasets.
• Dynamic Knowledge:
  Static training methods are illsuited to a field where knowledge evolves rapidly.

Despite progress, several critical gaps hinder the deployment of LLMs in clinical settings:

• Dynamic Learning Systems:
  Existing models lack mechanisms for continuous knowledge integration, leaving them outdated as medical guidelines evolve.
• Explainability:
  Blackbox outputs limit trust and usability among clinicians.
• Bias Mitigation:
  Robust frameworks to identify and reduce demographic biases remain underdeveloped.
• Clinical Collaboration:
  Models are not optimized for seamless integration into clinician workflows.

To address these gaps, this paper outlines four primary research directions:

• Framework for Continual Learning:
  Develop modular architectures that enable LLMs to ingest and prioritize updates from medical literature. Explore transfer learning approaches to incorporate domainspecific advancements without retraining entire models.
• Explainable AI for Medicine:
  Design methods to generate interpretable outputs that align with clinical reasoning. Utilize visualization tools to enhance the transparency of model predictions.
• Bias Auditing and Equity Mechanisms:
  Create benchmarks to measure biases across demographic groups. Develop fairnessaware training pipelines that mitigate disparities in model performance.
• Collaborative DecisionSupport Systems:
  Prototype systems where LLMs act as assistants, augmenting rather than replacing clinicians. Implement realtime feedback loops to refine model outputs based on user interaction.

Research will leverage mixed methodologies, combining quantitative evaluations on benchmarks like MultiMedQA with qualitative feedback from clinical practitioners. Techniques such as instruction prompt tuning, selfconsistency strategies, and humanintheloop systems will be central to experimentation.

This research aims to:

• Establish adaptive LLM frameworks for realtime clinical applications.
• Develop robust evaluation metrics for safety, equity, and accuracy.
• Bridge the gap between AI capabilities and clinician requirements.
• Advance the state of the art in medical NLP.

The integration of LLMs into healthcare is a frontier ripe with potential and challenges. By addressing gaps in dynamic learning, equity, explainability, and collaboration, this research seeks to create robust and trustworthy AI systems tailored to clinical needs.

Through these contributions, it envisions a future where LLMs serve as invaluable tools in transforming healthcare delivery.