Peter Murphy Campbell

Background: Large language models (LLMs) have demonstrated increasing capabilities in generating clinically coherent and accurate responses to patient questions, in some cases outperforming physicians in terms of accuracy and empathy. However, little is known about how physicians across geographic regions and levels of clinical experience evaluate these artificial intelligence (AI)–generated responses compared to those authored by human clinicians. Objective: This study examined physician evaluations of LLM-generated versus physician-authored responses to real-world patient questions, comparing preference patterns across geographic regions and years in clinical practice. Methods: We conducted a cross-sectional online survey between March and May 2025 among licensed physicians recruited internationally. Participants reviewed anonymized medical responses from 2 LLMs (GPT-4.0 and Meta AI) and verified physicians to questions sourced from Reddit’s r/AskDocs forum. Each participant ranked 3 responses per question (1=most preferred; 3=least preferred) according to accuracy and responsiveness. Mean ranks, pairwise win proportions, and full rank distributions were analyzed descriptively and stratified by geographic region and years in practice. Results: Overall, LLM-generated responses were strongly preferred. GPT-4.0 achieved the best mean rank (1.63, SD 0.68; 95% CI 1.52-1.74), followed by Meta AI (1.83, SD 0.72; 95% CI 1.71-1.94), while verified physician-authored responses were least preferred (2.53, SD 0.76; 95% CI 2.40-2.65). In pairwise analyses, responses generated by GPT-4.0 won 78% (118/150) of the head-to-head comparisons versus physician-authored responses and 57% (86/150) versus Meta AI responses. Preference for GPT-4.0 was most pronounced in Africa (mean 1.59, SD 0.72), Asia (mean 1.91, SD 0.83), and North America (mean 1.55, SD 0.60), while Meta AI slightly led in Europe (mean 1.33, SD 0.57) and the Americas (mean 1.75). Across experience levels, physicians with less than 5 years in practice (28/52, 54%) ranked GPT-4.0 most favorably (mean 1.58, SD 0.63), followed by those with 10 to 15 years of experience (mean 1.56, SD 0.72). Even among physicians with more than 15 years in practice (9/52, 17%), AI-generated responses outperformed physician-authored responses (mean 1.75 vs 2.62). Across all subgroups, human-authored responses were ranked lowest. Conclusions: This exploratory study demonstrates that physicians across diverse regions and experience levels generally prefer LLM-generated responses to human-authored ones. The consistency of this finding across continents and practice durations underscores growing professional acceptance of AI as a viable tool for patient communication. These results suggest that modern LLMs, particularly GPT-4.0, may provide clinically acceptable, contextually relevant, and user-trusted health information, with potential to augment physician workflows and patient education.

Systematic review and meta-analysis. To perform a comprehensive meta-analysis comparing clinical outcomes of uniportal versus biportal endoscopic spine surgery across decompression procedures in patients with lumbar degenerative disease (LDD). Uniportal endoscopic spine surgery has been a widely adopted minimally invasive technique, whereas biportal endoscopy has recently emerged as a promising alternative with potential advantages in surgical outcomes. A systematic review and meta-analysis of comparative studies was conducted in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. PubMed, Embase, and Scopus databases were searched to identify relevant studies. Eleven studies encompassing 374 uniportal and 368 biportal patients were included. Outcomes analyzed were Oswestry Disability Index (ODI), Visual Analog Scale (VAS) scores for back and leg pain, complication rates, operative time, and length of hospital stay. Biportal endoscopic surgery was associated with a significantly lower ODI at 1-3 months and at final follow-up compared with uniportal surgery. However, uniportal discectomy demonstrated significantly shorter operative time and length of hospital stay than biportal discectomy. No significant differences were observed between approaches in terms of VAS scores, complication rates, or ODI at other time points. Both uniportal and biportal endoscopic spine surgeries yield comparable postoperative outcomes in LDD. Although biportal surgery showed a modest advantage in ODI improvement, it did not reach the minimal clinically important difference. Uniportal surgery demonstrated greater efficiency in terms of operative time and recovery, particularly for discectomy procedures.

Background: Large language models (LLMs) have demonstrated a unique ability to generate clinically accurate responses to patient questions, in some cases outperforming physicians. However, little is known about how physician evaluations of such responses vary globally and by years in clinical practice. Objective: This study builds on prior work by comparing LLM-generated and physician-authored responses to patient questions using two general-purpose LLMs in an international sample of physicians. Participants were asked to rank responses based on accuracy and responsiveness. Methods: We conducted a survey to assess physician preferences for AI- and human-generated responses to patient questions from the r/AskDocs subreddit. Participants reviewed anonymized answers from ChatGPT-4.0, Meta.AI, and a verified physician, ranking each from best (1) to worst (3). We summarized respondent characteristics descriptively. The primary outcome was the mean rank of each response type. Sensitivity analyses included pairwise win proportions and full rank distribution visualizations. Results: Fifty-two physicians completed the survey, most of whom were male (78.8%), aged 25–34 (53.8%), based in North America (48.1%) or Africa (25.0%), and over half (53.8%) had less than 5 years of clinical experience. Across all regions, ChatGPT-4.0 and Meta.AI responses were preferred over physician-authored responses, with ChatGPT-4.0 ranked highest in Africa, Asia, Asia Pacific, and North America, and Meta.AI slightly favored in Europe and the Americas. By years in practice, AI-generated responses consistently outperformed physician responses, with ChatGPT-4.0 most preferred among those with less than 15 years of experience and showing the greatest advantage in the 10– 15 year group. Conclusions: In our global sample, most physicians preferred LLM-generated responses over those written by human contributors. However, preferences varied by geographic region and years in clinical practice, suggesting that both cultural and experiential factors shape physician attitudes toward Artificial Intelligence (AI). These preliminary findings highlight the need for larger, adequately powered studies to assess statistically significant differences and interactions across subgroups. Such research is essential to inform context-specific strategies for integrating AI into patient-facing communication. Trial Registration: N/A

BACKGROUND Large language models (LLMs) have demonstrated increasing capabilities in generating clinically coherent and accurate responses to patient questions, in some cases outperforming physicians in terms of accuracy and empathy. However, little is known about how physicians across geographic regions and levels of clinical experience evaluate these artificial intelligence (AI)–generated responses compared to those authored by human clinicians. OBJECTIVE This study examined physician evaluations of LLM-generated versus physician-authored responses to real-world patient questions, comparing preference patterns across geographic regions and years in clinical practice. METHODS We conducted a cross-sectional online survey between March and May 2025 among licensed physicians recruited internationally. Participants reviewed anonymized medical responses from 2 LLMs (GPT-4.0 and Meta AI) and verified physicians to questions sourced from Reddit’s r/AskDocs forum. Each participant ranked 3 responses per question (1=most preferred; 3=least preferred) according to accuracy and responsiveness. Mean ranks, pairwise win proportions, and full rank distributions were analyzed descriptively and stratified by geographic region and years in practice. RESULTS Overall, LLM-generated responses were strongly preferred. GPT-4.0 achieved the best mean rank (1.63, SD 0.68; 95% CI 1.52-1.74), followed by Meta AI (1.83, SD 0.72; 95% CI 1.71-1.94), while verified physician-authored responses were least preferred (2.53, SD 0.76; 95% CI 2.40-2.65). In pairwise analyses, responses generated by GPT-4.0 won 78% (118/150) of the head-to-head comparisons versus physician-authored responses and 57% (86/150) versus Meta AI responses. Preference for GPT-4.0 was most pronounced in Africa (mean 1.59, SD 0.72), Asia (mean 1.91, SD 0.83), and North America (mean 1.55, SD 0.60), while Meta AI slightly led in Europe (mean 1.33, SD 0.57) and the Americas (mean 1.75). Across experience levels, physicians with less than 5 years in practice (28/52, 54%) ranked GPT-4.0 most favorably (mean 1.58, SD 0.63), followed by those with 10 to 15 years of experience (mean 1.56, SD 0.72). Even among physicians with more than 15 years in practice (9/52, 17%), AI-generated responses outperformed physician-authored responses (mean 1.75 vs 2.62). Across all subgroups, human-authored responses were ranked lowest. CONCLUSIONS This exploratory study demonstrates that physicians across diverse regions and experience levels generally prefer LLM-generated responses to human-authored ones. The consistency of this finding across continents and practice durations underscores growing professional acceptance of AI as a viable tool for patient communication. These results suggest that modern LLMs, particularly GPT-4.0, may provide clinically acceptable, contextually relevant, and user-trusted health information, with potential to augment physician workflows and patient education.

Background: Increasing overlap exists between surgeries performed by podiatrists and orthopaedic surgeons. Large-scale cost comparisons between the two are lacking despite the current climate of cost containment in health care. Using national Medicare data, we aimed to compare per-case Medicare payments between podiatrists and orthopaedic surgeons for ankle fracture fixation. Methods: This retrospective cohort study included patients in an outpatient setting undergoing either unimalleolar, bimalleolar, or trimalleolar ankle fracture repair from the national Medicare Limited Data Set (2013-2019). Type of surgeon (podiatrist or orthopaedic surgeon) was determined using publicly available information. The primary outcome was total Medicare payments specific to the procedure, as a surrogate for cost. A subset analysis was also done to directly compare costs of orthopaedic surgeons to podiatric surgeons while excluding other fees (eg, hospital facility fees and surgery-related imaging payments). Additionally, patient demographics and hospital characteristics were compared to determine if any factors associated with costs may influence group differences. Univariable tests assessed significance of group differences. Results: Overall, 16 927 unimalleolar, 17 244 bimalleolar, and 11 717 trimalleolar fracture repairs were included; 86.7% and 13.3%, 92.4% and 7.6%, and 92.2% and 7.8% were performed by an orthopaedic surgeon or podiatrist, respectively. Median age (70-71 years) and median Charlson-Deyo Comorbidity Index (0) did not significantly differ between patients treated by either surgeon type. Median procedure-specific Medicare payments for all 3 categories of ankle fracture repairs (uni-, bi-, trimalleolar) were significantly lower for orthopaedic surgeons compared to podiatrists: $4156 vs $4300, $4205 vs $4379, and $4396 vs $4525, respectively (all P < .001). Conclusion: Our investigation using a national Medicare data set (2013-2019) found that the 3 types of ankle fractures (unimalleolar, bimalleolar, and trimalleolar) performed by orthopaedic surgeons in an outpatient setting were less expensive and that cost differences do not appear to be driven by patient characteristics. These results and further research into the causes of the cost differences may help improve the cost-effectiveness of ankle fracture surgery.