A new study uses machine learning to predict long-term pain after total knee arthroplasty, identifying high post-operative inflammatory markers—especially TARC—along with higher preoperative pain and longer tourniquet time as top predictors for persistent pain.

Another AI-driven study examined the top 200 Google “People Also Ask” queries for seven terms, compiling 1,400 question-website pairs to assess information quality and themes on regional anesthesia and anesthesia-related topics.

A second AI study investigates what patients search online about regional anesthesia, identifying common questions on risks, complications, medications, sedation, nerve block duration, and recovery, with quality varying by source.

Researchers aim to translate ML findings into personalized pain management, while noting that further work is needed before clinical application.

The findings were presented at the ASRA Annual Meeting as AI-driven studies on pain risk and anesthesia education, with potential to guide pre-surgical consultations.

Clinicians may use these insights to guide preoperative conversations, tailor patient education materials, and direct patients to reliable online resources, with plans to expand multilingual and accessible formats and continue AI-enabled research.

The press release frames these findings as guidance for anesthesiologists’ preoperative consultations to improve counseling and surgical planning.

Separate analyses mapped top search results, revealing 55% academic, 19% government, and 11% public/social sources, with government and academic sites generally more accurate than some medical-practice sites.

Analyzing data from 160 patients, the study tested four ML models and found XGBoost to be the most informative, with TARC consistently emerging as a key predictor across models.

Ultimately, the research seeks to improve patient outcomes by preemptively addressing questions and personalizing treatment decisions related to pain management and anesthesia.

The research integrates biological markers with clinical data to tailor pain management and reduce chronic pain after knee replacement, moving beyond known risk factors like sex, preexisting pain, and mental health conditions.

The work sits within Hospital for Special Surgery’s broader focus on musculoskeletal health, research, and education, highlighting the institution’s mission to advance patient care.