Accuracy and Bias in Artificial Intelligence Chatbot Recommendations for Oculoplastic Surgeons.

Purpose The purpose of this study is to assess the accuracy of and bias in recommendations for oculoplastic surgeons from three artificial intelligence (AI) chatbot systems. Methods ChatGPT, Microsoft Bing Balanced, and Google Bard were asked for recommendations for oculoplastic surgeons practicing in 20 cities with the highest population in the United States. Three prompts were used: "can you help me find (an oculoplastic surgeon)/(a doctor who does eyelid lifts)/(an oculofacial plastic surgeon) in (city)." Results A total of 672 suggestions were made between (oculoplastic surgeon; doctor who does eyelid lifts; oculofacial plastic surgeon); 19.8% suggestions were excluded, leaving 539 suggested physicians. Of these, 64.1% were oculoplastics specialists (of which 70.1% were American Society of Ophthalmic Plastic and Reconstructive Surgery (ASOPRS) members); 16.1% were general plastic surgery trained, 9.0% were ENT trained, 8.8% were ophthalmology but not oculoplastics trained, and 1.9% were trained in another specialty. 27.7% of recommendations across all AI systems were female. Conclusions Among the chatbot systems tested, there were high rates of inaccuracy: up to 38% of recommended surgeons were nonexistent or not practicing in the city requested, and 35.9% of those recommended as oculoplastic/oculofacial plastic surgeons were not oculoplastics specialists. Choice of prompt affected the result, with requests for "a doctor who does eyelid lifts" resulting in more plastic surgeons and ENTs and fewer oculoplastic surgeons. It is important to identify inaccuracies and biases in recommendations provided by AI systems as more patients may start using them to choose a surgeon.

Bilateral Cytomegalovirus Retinitis After Chimeric Antigen Receptor T-cell Therapy for B-cell Lymphoma.

Cytomegalovirus (CMV) retinitis is commonly associated with immunosuppression and can cause irreversible vision loss. Chimeric antigen receptor T-cell (CAR-T) therapy has emerged as an effective cancer treatment option but requires immunosuppression, thereby increasing the possibility of acquiring opportunistic infections such as CMV. We present the case of a 76-year-old female with a history of hypertension and type 2 diabetes mellitus who initially presented with shortness of breath and was diagnosed with the activated B-cell subset of diffuse large B-cell lymphoma (DLBCL). She received multiple cycles of chemotherapy and experienced relapses with cardiac involvement. The patient developed vision loss in the right eye and was diagnosed with bilateral posterior vitritis. She underwent various treatments, including radiotherapy, systemic chemotherapy, cataract extraction, and vitrectomy. After CAR-T therapy, she developed bilateral CMV retinitis, confirmed through polymerase chain reaction testing and managed by valganciclovir. Overall, this case report describes the first reported case of bilateral CMV retinitis following CAR-T therapy for DLBCL. It emphasizes the need for early recognition and treatment of CMV retinitis to prevent permanent vision loss. The report also underscores the importance of regular ocular screening and consideration of prophylactic measures in patients undergoing CAR-T therapy.

Intramuscular Degloving Injury of the Rectus Femoris From Kickball: A Case Report and Review.

Intramuscular degloving injuries (IDIs) are a rare and unique type of muscle injury where there is a dissociation between the inner and outer components of a particular muscle. This type of injury is seen exclusively within the rectus femoris (RF) muscle due to its unique muscle-within-a-muscle anatomy and represents 9% of RF injuries. Despite the significance of this injury, limited knowledge exists regarding the mechanism, management, and prognosis of IDIs, and IDIs are not currently included among the various muscle injury classifications. We present a 38-year-old active male with a one-week history of acute onset right anterior mid-thigh pain and palpable lump after playing kickball. Right thigh MRI revealed an IDI of the RF muscle, edema within the inner and outer muscular portions of the muscle, and a retraction of the torn inner indirect myotendinous complex of the RF. He was managed with physical therapy while being advised to avoid aggressive quadriceps contractions, high-intensity, or high-impact exercise. This is the first reported case of an IDI that occurred in an older recreational athlete (versus young competitive athletes), and the first case of an IDI in a kicking sport other than soccer (kickball). This case emphasizes the importance of a broader awareness of this injury, and a heightened index of suspicion is advised in assessing potential IDIs to improve patient prognosis and rehabilitation. Given the limited understanding and rarity of this injury, we also provide a comprehensive review describing the IDI to the RF.

Bias and Inaccuracy in AI Chatbot Ophthalmologist Recommendations.

PURPOSE AND DESIGN: To evaluate the accuracy and bias of ophthalmologist recommendations made by three AI chatbots, namely ChatGPT 3.5 (OpenAI, San Francisco, CA, USA), Bing Chat (Microsoft Corp., Redmond, WA, USA), and Google Bard (Alphabet Inc., Mountain View, CA, USA). This study analyzed chatbot recommendations for the 20 most populous U.S. cities. METHODS: Each chatbot returned 80 total recommendations when given the prompt "Find me four good ophthalmologists in (city)." Characteristics of the physicians, including specialty, location, gender, practice type, and fellowship, were collected. A one-proportion z-test was performed to compare the proportion of female ophthalmologists recommended by each chatbot to the national average (27.2% per the Association of American Medical Colleges (AAMC)). Pearson's chi-squared test was performed to determine differences between the three chatbots in male versus female recommendations and recommendation accuracy. RESULTS: Female ophthalmologists recommended by Bing Chat (1.61%) and Bard (8.0%) were significantly less than the national proportion of 27.2% practicing female ophthalmologists (p<0.001, p<0.01, respectively). ChatGPT recommended fewer female (29.5%) than male ophthalmologists (p<0.722). ChatGPT (73.8%), Bing Chat (67.5%), and Bard (62.5%) gave high rates of inaccurate recommendations. Compared to the national average of academic ophthalmologists (17%), the proportion of recommended ophthalmologists in academic medicine or in combined academic and private practice was significantly greater for all three chatbots. CONCLUSION: This study revealed substantial bias and inaccuracy in the AI chatbots' recommendations. They struggled to recommend ophthalmologists reliably and accurately, with most recommendations being physicians in specialties other than ophthalmology or not in or near the desired city. Bing Chat and Google Bard showed a significant tendency against recommending female ophthalmologists, and all chatbots favored recommending ophthalmologists in academic medicine.