Operationalizing AI in Orthopaedic Practice: Governance, Integration, and Future Clinical Applications-Operational AI Slides

Operational AI Slides

Pdf Summary

This presentation by Dr. Travis Zack outlines the development and deployment of Artificial Intelligence (AI) tools in medicine, emphasizing their potential and challenges. AI methods include machine learning (ML), deep learning, and large language models (LLMs), which learn from diverse medical inputs—such as images, text, lab results, and biological signals—to generate outputs like diagnoses, predictions, or care plans. ML models vary from simple linear regressions to complex neural networks that automatically derive internal data representations.<br /><br />LLM development involves stages: pretraining on language data, alignment with human values, optional specialization for specific medical tasks, and productionization, which integrates LLMs safely into healthcare workflows via prompt engineering and system interfacing. Biomedical AI utilizes both structured (labs, medications) and unstructured data (clinical notes, imaging), often combining multiple data sources for richer insights. However, integrating multimodal data remains technically challenging, marking current frontiers in AI research exemplified by initiatives like Google DeepMind’s Gemini.<br /><br />Medical AI faces the “firehose” of rapidly changing and heterogeneous information, complicating generalizability and reliability. Tools like OpenEvidence aim to improve medical information retrieval by filtering based on relevance, recency, authority, and evidence strength to provide trustworthy answers from sources such as PubMed and clinical guidelines.<br /><br />AI applications span patient-facing tools (e.g., symptom checkers, education), clinician decision support (e.g., diagnosis, care coordination), operations (e.g., scheduling, quality improvement), and reimbursement. Adoption approaches include both integration within Electronic Health Records (EHR) and standalone external systems. Collaboration with organizations like the American Diabetes Association seeks to understand guideline usage and enhance evidence-based care.<br /><br />The talk stresses that medical practice must bridge evidence from controlled studies with individualized patient care, requiring both deep clinical expertise and real-world experience. A responsible AI pipeline—from conception, deidentified data sandboxing, governance, to EHR deployment—supports safe, effective translation of AI innovations into clinical use.

Keywords

Artificial Intelligence in Medicine

Machine Learning

Deep Learning

Large Language Models

Multimodal Data Integration

Medical AI Challenges

Healthcare AI Deployment

Clinical Decision Support

Biomedical Data Analysis

Responsible AI Pipeline