Assessing the Accuracy and Reliability of AI-Generated Medical Responses: An Evaluation of the Chat-GPT Model

Background: Natural language processing models such as ChatGPT can generate text-based content and are poised to become a major information source in medicine and beyond. The accuracy and completeness of ChatGPT for medical queries is not known. Methods: Thirty-three physicians across 17 specialties generated 284 medical questions that they subjectively classified as easy, medium, or hard with either binary (yes/no) or descriptive answers. The physicians then graded ChatGPT-generated answers to these questions for accuracy (6-point Likert scale; range 1 – completely incorrect to 6 – completely correct) and completeness (3-point Likert scale; range 1 – incomplete to 3 - complete plus additional context). Scores were summarized with descriptive statistics and compared using Mann-Whitney U or Kruskal-Wallis testing. Results: Across all questions (n=284), median accuracy score was 5.5 (between almost completely and completely correct) with mean score of 4.8 (between mostly and almost completely correct). Median completeness score was 3 (complete and comprehensive) with mean score of 2.5. For questions rated easy, medium, and hard, median accuracy scores were 6, 5.5, and 5 (mean 5.0, 4.7, and 4.6; p=0.05). Accuracy scores for binary and descriptive questions were similar (median 6 vs. 5; mean 4.9 vs. 4.7; p=0.07). Of 36 questions with scores of 1-2, 34 were re-queried/re-graded 8-17 days later with substantial improvement (median 2 vs. 4; p<0.01). Conclusions: ChatGPT generated largely accurate information to diverse medical queries as judged by academic physician specialists although with important limitations. Further research and model development are needed to correct inaccuracies and for validation.


Introduction
The integration of natural language processing (NLP) models in healthcare has the potential to radically enhance the accessibility of medical information for health professionals and patients. Large language models (LLMs) are NLP tools that can understand and generate human-like text. Compared to traditional supervised deep learning models, large language models can learn from data more e ciently with a two-stage training process, starting with self-supervised learning on vast amounts of unannotated data, then ne-tuned on smaller, task-speci c, annotated datasets so they can perform on end-user-speci ed tasks (1). .
One such AI-powered tool that has gained recent widespread popularity is Chat-Generative Pre-Trained Transformer (ChatGPT), a conversational chatbot based on Generative-Pre-Trained Transformer-3.5 (GPT-3.5), an LLM with over 175 billion parameters (2). ChatGPT's training data encompasses a broad range of internet sources, including books, articles, and websites. Fine-tuning for conversational tasks using reinforcement learning from human feedback (3), allows ChatGPT to incorporate the complexity of users' intentions, thus enabling it to pro ciently respond to various end-user tasks, potentially including medical queries.
With the increasing amount of medical data and complexity of clinical decision-making, NLP tools could theoretically assist physicians in making timely, informed decisions and improve the overall quality and e ciency of healthcare. ChatGPT performed at or near the passing threshold for United States Medical Licensing Exam (USMLE) without any specialized training, suggesting its potential for medical education and clinical decision support (4,5). Further, technology advancements have led to the democratization of knowledge, where patients no longer solely rely on healthcare professionals for medical information. Instead, they are increasingly turning to search engines, and now AI chatbots, as convenient and accessible sources of medical information.
ChatGPT and other recently released chat-bots engage in conversational interactions and provide authoritative-sounding responses to complicated medical queries. However, despite its potential, ChatGPT often produces seemingly credible but incorrect outputs, thus warranting caution when considering its applications in medical practice and research (6-11). The reliability and accuracy of these engines has not been assessed, particularly in the context of openended medical questions that physicians and patients are likely to ask.
This study aims to evaluate the accuracy and comprehensiveness of ChatGPT-generated responses to medical queries developed by physicians. This will provide an early evidence base on the reliability of ChatGPT for providing accurate and complete information. Additionally, this study will highlight limitations of AI-provided medical information.

Methodology
This study was IRB exempt as no patient-level data were used. A dataset of questions was generated by 33 physicians across 17 medical, surgical, and pediatric specialties (Table S1). A total of 59 physicians were originally invited to participate (56% response rate); all respondents were faculty (N=31) or recent graduates from residency or fellowship programs (N=2) at a single academic medical center (Vanderbilt University Medical Center). At least 1 physician from each major specialty was invited to participate. These physicians were instructed to provide questions with clear and uncontroversial answers from available medical guidelines, and unchanged from the beginning of 2021 (accounting for the cut-off of the training set for ChatGPT). Each physician was asked to produce six questions, three of which had binary yes/no or right/wrong answers and were rated as easy, medium, and hard by subjective rating by the physicians who provided the questions. The other three questions had answers that were either descriptive in nature or produced a list of multiple correct answers, with the same three complexity ratings. To minimize bias, the physicians were asked not to screen the questions themselves in ChatGPT. To assess physician agreement and to generate additional data, the senior authors (LEW and DBJ) provided and rated a second dataset of 44 melanoma and immunotherapy-speci c questions. Finally, since most participating physicians were specialists, a third dataset of 60 questions encompassing 10 common medical conditions (see Table S2) were also produced and rated by the senior authors. Six questions were generated for each common medical condition with the same question classi cation (binary vs. descriptive, and easy vs. medium vs. hard di culty level). All questions were subjectively chosen as representative of each physician's subject matter expertise.
To ensure consistency, all questions were entered into the ChatGPT engine by one investigator (RG), who prompted the chatbot to be speci c and incorporate any medical guidelines into the answer if appropriate (with the phrase "Be speci c and incorporate any applicable medical guidelines"). The AI-generated answers were then provided to the physicians who created the questions. Based on their medical expertise in their corresponding eld, the physicians rated the accuracy of answers according to two prede ned scales of accuracy and completeness.
The accuracy scale was a six-point Likert scale (1 -completely incorrect, 2 -more incorrect than correct, 3 -Approximately equal correct and incorrect, 4more correct than incorrect, 5 -nearly all correct, 6 -correct), and the completeness scale was a three-point Likert scale (1 -incomplete, addresses some aspects of the question, but signi cant parts are missing or incomplete, 2 -adequate, addresses all aspects of the question and provides the minimum amount of information required to be considered complete, 3 -comprehensive, addresses all aspects of the question and provides additional information or context beyond what was expected). Answers that were completely incorrect on the accuracy scale (score of 1) were not graded on comprehensiveness.
To assess the reproducibility of the results and evaluate the effect of time on answer accuracy, an internal validation process was conducted in which ChatGPT was re-queried with the same questions that generated answers originally graded as inaccurate (less than 3 on the accuracy scale). The re-scored answers were generated by ChatGPT 8 to 17 days after the initial answers, and physicians re-scored the updated AI answers.
Score results were listed descriptively (median, mean, intra-quartile range, standard deviation), and were compared between groups using Mann-Whitney U or Kruskal Wallis testing. Re-graded questions were compared using the Wilcoxon signed rank test. In the melanoma/immunotherapy, and the common conditions dataset, intra-rater agreement was graded using the kappa statistic across all scores (1-6 for accuracy and 1-3 for completeness) and in a condensed, exploratory fashion to assess for general agreement (agreement for accuracy scores 1-2 vs. 3-4 vs. 5-6).

Multispecialty Analysis
ChatGPT-generated answers were initially evaluated based on 180 questions provided by 33 physicians from 17 specialties. As noted, each physician provided three descriptive and three binary questions at different di culty levels (easy, medium, and hard) except for one author who provided two descriptive question sets ( Figure 1). An example of an easy-level di culty descriptive question was, "What are the rst-line treatments for Stage IA mycosis fungoides?" An example of a medium-level di culty descriptive question was, "Which patients with well-differentiated thyroid cancer should receive postoperative radioactive iodine ablation?" An example of a hard-level di culty binary question was, "Can we start angiotensin receptor-neprilysin inhibitors immediately after discontinuing an angiotensin-converting enzyme inhibitor?" For additional example questions and answers, see Table 1.

Internal Validation: Re-scored Analysis
Of 36 inaccurate answers that received a score of 2 or lower on the accuracy scale, 34 were re-scored by physicians to evaluate the reproducibility of answers over time (Table 3). Notably, scores generally improved with 26 questions improving, 7 remaining the same, and 1 decreasing in accuracy. Median accuracy scores for the original questions was 2 (mean 1.6, SD 0.5, IQR 1) compared with median 4 (mean 3.9, SD 1.8, IQR 3.3) for re-scored answers(Wilcoxon signed rank p<0.01) (Table S3, Figure 2C). The re-scored answers were generated from ChatGPT 8 to 17 days after the initial answers were generated.

Discussion
This study indicates that three months into its existence, ChatGPT has promise for providing accurate and comprehensive medical information. However, it remains well short of completely reliable. The multispecialty analyses of 180 questions provided by 33 physicians across 17 specialties revealed 57.8% (n=104) of AI answers were rated as "nearly all correct" or "correct" (mean accuracy score 4.4, median 5). Most (53.5%) answers were comprehensive (mean completeness score 2.4, median 3), indicating complete answers with additional information or context. When analyzing the data, the median accuracy scores were generally higher than mean scores, which was re ective of multiple instances where the chatbot was spectacularly and surprisingly wrong. Thus, any use of the current version of ChatGPT for medical knowledge dissemination must consider its capacity to come to a totally mistaken conclusion, which is delivered in an authoritative and convincing manner.
Overall, accuracy was fairly high across question types and di culty. More di cult questions seemed to have perhaps slightly less accurate scores (mean 4.2) than easier questions (mean 4.6), suggesting a potential limitation of the model in handling complex medical queries, but this did not reach statistical signi cance. Overall though, the results for type of question (descriptive or binary) or di culty level were broadly similar, implying ChatGPT could have promise for open-ended question types with varying levels of di culty, providing potential broad applicability.
An internal validation process demonstrated ChatGPT's potential to improve signi cantly over a short period of time. The re-scored answers were generated from ChatGPT only 8 to 17 days after the initial responses were generated. Compared with the median accuracy score of 2 (median 2, SD 0.5, IQR 1) for the original low-quality answers, median accuracy score improved to 4 (p < 0.01) ( Table 2). This improvement could be attributed to the continuous update and re nement of the model's algorithms and parameters and the impact of repetitive user feedback through reinforcement learning.
Analysis results from the three distinct question sets (multispecialty, melanoma and immunotherapy, and common conditions) revealed fairly similar and high ratings. However, the common conditions dataset had somewhat higher accuracy scores, suggesting that more common conditions may have more training data available, leading to improved scores. However, this dataset was also scored later, thus potentially re ecting model improvement over days to weeks.
Despite these promising results, the scope of our conclusions is limited due to the modest sample size of 33 physicians from a single academic medical center, and the dataset of 284 questions, which may not be representative of all medical specialties and the many questions that can be posed within them. Further, selection bias of a cohort of physicians limited to those in academic practice, as well as respondent bias were present. Other limitations include subjective choice of questions and the absence of a validation mechanism to verify the accuracy of the information provided. The study also relied on physicians' subjective, self-reported ratings, which may introduce bias; similar judgements may also vary by physician (as the difference between more correct than incorrect vs. nearly all correct (4 vs. 5) may be small). Questions chosen by physicians were those with clear and uncontroversial answers and included instructions to incorporate current guidelines, and are thus likely not representative of queries that patients and the general public would make. In particular, many patients may not have the explicit knowledge to incorporate, for example, cancer staging, prior therapies, sites of metastases, all of which may impact answers. The analyses were limited to a speci c AI model, ChatGPT, and may not apply to other AI models, particularly those with medical-speci c training.
This study provide an early evidence base to demonstrate the potential of AI-based systems to provide answers to real-world, non-multiple choice clinical questions. ChatGPT or similar tools, with further validation, could serve as a valuable resource for rapid medical information retrieval in a fast-paced clinical setting to enhance healthcare e ciency and complex decision making. Healthcare professionals should also consider how patients may use these tools, and how ChatGPT is programmed to provide appropriate recommendations and referrals to quali ed health professionals. Medical education should include training on the potential bene ts, limitations and risks associated with AI-powered tools, so that both healthcare professionals and patients can make informed decisions about when and how to use them. At the same time, relying solely on the current, publicly available version of ChatGPT as a sole source for medical information is not advisable. If trained by reliable experts and on a larger dataset of vetted medical information (medical literature, pharmacology databases, electronic medical records, etc.), large language models like ChatGPT have the potential to rapidly improve and transform dissemination of medical knowledge. A recently released GPT-style language model trained exclusively on biomedical literature to enhance domain-speci c language model training achieved 51% accuracy on various biomedical question answering tasks (12). This highlights the promise of domain-speci c language generation models in real-world applications in healthcare.
Further research is needed to validate the reliability of AI-provided medical information with large groups of healthcare professionals and diverse question types. Additionally, studies should assess the evolution of AI-generated medical information over time. Other important considerations include ethical and privacy concerns. Because these AI tools are only as reliable as the data they are trained on, efforts should be made to incorporate reliable medical information sources such as medical literature, pharmacology databases, and real-world evidence to ensure that AI models are well-informed and provide the most up-to-date information. Moreover, models that are trained exclusively on text might miss nuances presented only in gure or tabular form and not discussed speci cally in the body of a paper. Finally, future efforts should focus on developing nimble though robust standards and regulations for the safe and effective implementation of AI in healthcare.

Conclusion
Integrating language models, such as ChatGPT in medical practice reveals early promise, but with many considerations for safe and optimal use. While the AIgenerated answers displayed high accuracy and completeness scores across various specialties, question types, and di culty levels, further development is needed to improve the reliability and robustness of these tools before clinical integration. Medical professionals and patients should be aware of the limitations and actively check AI-generated medical information with trusted sources. This study is a foundational step towards establishing an evidence base for using AI language models in healthcare and highlights the importance of ongoing evaluation and regulation.

Declarations
Disclosures: SSC has served on advisory boards or as a consultant for Nonagen, Paci c Edge, Lantheus, Prokarium, Merck, P zer, Urogen, Janssen, and Tu Therapeutics. TJO has served on advisory boards or as a consultant for Microsoft, IBM, and AstraZeneca and has received research funding from Microsoft, IBM, GE Healthcare. JHC has served on advisory boards for Exelixis, Regeneron, and Coherus. ECO receives research funding from Varian Medical Systems.
JAZ receives royalties from UpToDate, Inc. DBJ has served on advisory boards or as a consultant for BMS, Catalyst Biopharma, Iovance, Jansen, Mallinckrodt, Merck, Mosaic ImmunoEngineering, Novartis, Oncosec, P zer, Targovax, and Teiko, has received research funding from BMS and Incyte, and has patents pending for use of MHC-II as a biomarker for immune checkpoint inhibitor response, and abatacept as treatment for immune-related adverse events.  2. Tumor size: Prolactinomas that are larger than 10 mm in diameter are more likely to cause symptoms and may be more di cult to control with medical therapy alone. These tumors may be considered for surgery.
3. Macroadenomas: Prolactinomas that are classi ed as macroadenomas, tumors that are larger than 10 mm in diameter, are more likely to cause symptoms and may be more di cult to control with medical therapy alone. These tumors may be considered for surgery.

Tumor invasion: Prolactinomas that have invaded
surrounding structures, such as the optic chiasm or cavernous sinus, may be considered for surgery to prevent further invasion and preserve neurological function.

Pregnancy
: If a woman with a prolactinoma is planning to become pregnant, surgery may be considered to control the prolactin level and prevent the tumor from growing during pregnancy.
It's important to note that surgery is not always necessary for prolactinomas and that the decision to proceed with surgery should be made on a case-by-case basis. A multidisciplinary team of endocrinologists and neurosurgeons should be consulted to provide the best treatment plan for the patient.

2
Medium Gastroenterology What medications are contraindicated for a patient with UC during pregnancy?
During pregnancy, the use of certain medications may be contraindicated due to the potential risks to the mother and/or the developing fetus.
Medications that are contraindicated for use during pregnancy in patients with UC include: Sulfasalazine: It is classi ed as a category D pregnancy drug, meaning that there is positive evidence of human fetal risk, but the bene ts from use in pregnant women may be acceptable despite the risk.

n/a
Methotrexate: It is classi ed as a category X pregnancy drug, meaning that it is contraindicated during pregnancy because it may cause fetal harm Cyclosporine and Tacrolimus: They are classi ed as a category C pregnancy drug, meaning that there is a risk to the developing fetus, but the bene ts may outweigh the risks in certain cases. Biologics (such as In iximab, Adalimumab, Vedolizumab, Ustekinumab, Certolizumab pegol, Golimumab) is generally avoided during pregnancy as their safety in pregnancy is not well established yet.
In addition to the medications mentioned above, there are other medications that may be contraindicated for use during pregnancy in patients with UC. These include: · Corticosteroids: such as prednisone, budesonide, hydrocortisone, etc. These medications may be used to manage in ammation and symptoms of UC, but their long-term use during pregnancy is not recommended due to the potential risks to the developing fetus.

·
Azathioprine and Mercaptopurine: These medications are classi ed as category D pregnancy drugs and may be used to manage UC during pregnancy, but their use should be carefully weighed against the potential risks to the mother and fetus. · 5-aminosalicylic acid (5-ASA) drugs: such as mesalamine and sulfasalazine, are considered as category B pregnancy drugs, meaning that they are generally considered safe during pregnancy, but their long-term use should be avoided.
It's important to note that these medications may be used during pregnancy but only under the guidance of healthcare provider and after a thorough risk-bene t analysis, as well as considering alternatives such as lifestyle modi cations and other medications that may be safer during pregnancy.

Hard
Radiation Oncology What radiotherapy dose and fractionation schemes are standard of care for rst line treatment of T2N2M0 small cell lung cancer?
The standard of care for rst-line treatment of T2N2M0 small cell lung cancer (SCLC) is concurrent chemoradiotherapy, which involves the administration of both radiation therapy and chemotherapy at the same time.
The recommended radiation dose and fractionation scheme for the treatment of T2N2M0 SCLC is 60-66 Gy in 30-33 fractions over 6-7 weeks. This is based on the results of multiple studies and clinical trials that have shown that this dose and fractionation scheme is effective in controlling the cancer while minimizing the risk of side effects.
The concurrent chemotherapy regimen is usually etoposide and cisplatin or carboplatin, which are drugs that have been shown to be effective in treating SCLC. The chemotherapy is usually given once per week during the radiation therapy treatment.
It is important to note that the best treatment option for an individual patient will depend on many factors such as the speci c characteristics of their tumor, their overall health, and their preferences. It is recommended that patients with T2N2M0 SCLC discuss the full range of treatment options with their oncologist to determine the best course of treatment for them.

3
Hard Trauma Surgery What are the key principles of managing necrotizing soft tissue infection?
The key principles of managing necrotizing soft tissue infections are: 1. Early recognition and aggressive surgical debridement: The goal is to remove all necrotic tissue as soon as possible to prevent the spread of infection.
2. Broad-spectrum antibiotics: Necrotizing infections are polymicrobial, so the use of broad-spectrum antibiotics is recommended to cover a wide range of potential pathogens. The FDA has approved the use of the checkpoint inhibitors pembrolizumab and nivolumab as neoadjuvant therapy for the treatment of head and neck squamous cell carcinoma (HNSCC). In the adjuvant setting, pembrolizumab is approved for use after de nitive surgery. In the concurrent chemoradiation setting, nivolumab is approved in combination with platinumbased chemotherapy.
Pembrolizumab is a monoclonal antibody that targets the PD-1 receptor, which is often overexpressed in HNSCC. The FDA approved pembrolizumab as a neoadjuvant therapy for HNSCC in 2019 based on the results of the KEYNOTE-048 trial, which showed that pembrolizumab signi cantly improved pathological complete response rates compared to standard therapy. Pembrolizumab is also approved as adjuvant therapy for HNSCC in 2020. It is administered after de nitive surgery, it's intended to lower the risk of recurrence in patients with HNSCC with a high risk of recurrence.
Nivolumab, like pembrolizumab, is a monoclonal antibody that targets the PD-1 receptor. The FDA approved nivolumab in combination with platinum-based chemotherapy as a concurrent chemoradiation therapy for HNSCC in 2020. This approval was based on the results of the CheckMate-141 trial, which showed that the addition of nivolumab to chemoradiation signi cantly improved overall survival compared to chemoradiation alone.
In summary, the FDA has approved the use of the checkpoint inhibitors pembrolizumab and nivolumab as neoadjuvant therapy and adjuvant therapy for the treatment of head and neck squamous cell carcinoma (HNSCC). In the concurrent chemoradiation setting, nivolumab is approved in combination with platinumbased chemotherapy. Pre-eclampsia, a pregnancy-speci c complication characterized by hypertension and proteinuria, has been associated with an increased risk of future cardiovascular disease (CVD). According to the American College of Obstetricians and Gynecologists (ACOG) guidelines, women with a history of pre-eclampsia have an increased risk of hypertension, stroke, and myocardial infarction later in life, particularly if they had severe pre-eclampsia or multiple episodes of pre-eclampsia.

References
In addition to the ACOG guidelines, The American Heart Association (AHA) also recognizes that pre-eclampsia is a risk factor for future CVD, and recommends that women with a history of pre-eclampsia be screened and treated for hypertension, diabetes, and dyslipidemia, and be counseled about lifestyle changes to reduce their risk of CVD.
It's important to note that the risk of CVD is not only limited to women who had pre-eclampsia but also to the baby born from the pre-eclamptic pregnancy, thus longterm follow-up and monitoring is crucial.

3
Medium Anesthesiology Is it appropriate to turn a pacemaker into asynchronous mode for a pacemakerdependent patient undergoing ECT?
Yes. Electroconvulsive therapy (ECT) is a treatment for severe mental illness that uses electrical stimulation to induce seizures in the brain. ECT may temporarily interfere with the function of pacemakers and implantable cardioverter-de brillators (ICDs), as the electrical stimulation of the brain can cause a temporary change in the heart's rhythm.
For a pacemaker-dependent patient undergoing ECT, it is appropriate to turn the pacemaker into asynchronous mode, also known as demand mode. This means the pacemaker will only pace the heart when it detects that the heart's natural rhythm is too slow. This helps to prevent the pacemaker from con icting with the electrical stimulation used during ECT.
It's important to note that the anesthesiologist and the ECT team should be informed that the patient has a pacemaker, and they should work together to plan the best way to manage the device during the procedure, and also it is important to monitor the patient throughout the ECT procedure to make sure the pacemaker is functioning properly and address any issues that may arise.
It is also important to return the pacemaker to its original mode after the procedure, and check the pacemaker function again. The American Society of Anesthesiologists (ASA) guidelines also recommends that the manufacturer's instructions for the speci c device regarding use during ECT should be consulted and followed.

3
Medium Oncology Should a patient with a 2.1mm ulcerated melanoma have a sentinel lymph node biopsy?
The decision to perform a sentinel lymph node biopsy (SLNB) in a patient with a 2.1mm ulcerated melanoma should be made on an individualized basis and should take into account the patient's overall health and the speci c characteristics of their melanoma.
The National Comprehensive Cancer Network (NCCN) guidelines for Melanoma recommend SLNB for patients with primary melanoma that is greater than 1mm in thickness, regardless of the presence of ulceration. The

3
American Joint Committee on Cancer (AJCC) melanoma staging system also recommends SLNB for patients with primary melanoma that is greater than 1mm in thickness, regardless of the presence of ulceration.
That being said, the presence of ulceration on the primary melanoma lesion is considered a negative prognostic factor, and is associated with a higher risk of regional and distant metastasis, as well as poor overall survival. Therefore, some physicians may consider SLNB for a 2.1mm ulcerated melanoma as a way to further assess the risk of lymph node involvement and to guide adjuvant treatment decisions.
It's important to note that the decision to perform SLNB should be made by a multidisciplinary team of professionals including an oncologist, a surgical oncologist, and a pathologist, after careful consideration of all the patient's clinical, pathological and imaging data.

Medium Pulmonology and Critical Care
Can doctors still treat you if you are allergic to a medication?
Yes. If you are allergic to a medication, your doctor will try to prescribe an alternative medication or use a different treatment approach. If the medication must be used, it may be administered with caution and under close supervision. Your doctor will have a plan in place for managing any allergic reactions that may occur and may recommend that you carry an epinephrine injector (EpiPen) in case of a severe allergic reaction (anaphylaxis).

1
Medium Dermatology Does treating early-stage mycosis fungoides improve survival?
Yes, treating early-stage mycosis fungoides can improve survival. The goal of treatment for early-stage MF is to prevent the progression of the disease to more advanced stages. Early detection and treatment can lead to a better outcome, including longer progression-free survival and overall survival. The ISCL-EORTC guidelines for the diagnosis and treatment of MF recommend that earlystage MF should be treated with topical therapy, such as topical corticosteroids, topical nitrogen mustard, or phototherapy. In some cases, low-dose oral or topical retinoids may also be used. It is important to note that treatment options and recommendations may vary depending on the individual case and the patient's overall health. Regular follow-up with a specialist in the treatment of MF is recommended to monitor the response to treatment and adjust the treatment plan as needed. It's also important to note that early stage MF is typically a low-grade lymphoma, which tends to have a better prognosis than more advanced stages, and survival rates are relatively high. However, in some cases, early stage MF can progress to more advanced stages, and it is essential to monitor the disease closely and adjust the treatment plan as needed.

3
Hard Cardiology Can we start ARNi right after we stop the ACE inhibitor?
Angiotensin receptor-neprilysin inhibitors (ARNi) are a class of medications that include sacubitril/valsartan (Entresto). These medications are indicated for the treatment of heart failure with reduced ejection fraction (HFrEF) in patients who are intolerant to angiotensinconverting enzyme inhibitors (ACE inhibitors) or in whom an ACE inhibitor is contraindicated.
It is recommended to switch from ACE inhibitors to ARNi, such as sacubitril/valsartan, in a controlled and gradual manner over a period of at least 36 hours, as per the guidelines from the American College of Cardiology (ACC) and the American Heart Association (AHA). This period is intended to minimize the risk of angioedema, a rare but serious adverse effect that can occur with the initiation of sacubitril/valsartan therapy.
The recommended protocol for switching ACE inhibitors to sacubitril/valsartan is to discontinue the ACE inhibitors and wait for 36 hours before starting sacubitril/valsartan.
It's important to note that the guidelines are continuously updated and the recommendations may change in the future.

Surgical Oncology
Is radioactive iodine ablation indicated for a patient with a 2cm intrathyroidal classic type papillary thyroid No, radioactive iodine ablation is not typically indicated for a patient with a 2cm intrathyroidal classic type papillary thyroid carcinoma after total thyroidectomy with negative margins and no lymph node involvement.
The American Thyroid Association (ATA) and the American College of Surgeons (ACS) recommends that radioactive iodine ablation therapy should be offered to 6 3 carcinoma after total thyroidectomy with negative margins and no lymph node involvement?
patients who had total thyroidectomy with a papillary thyroid carcinoma larger than 1cm or with extrathyroidal extension, and for patients with any T3 or T4 tumors, and for patients with lymph node involvement.
In this speci c case, the patient has a classic type papillary thyroid carcinoma, size of 2cm, that is intrathyroidal, and have negative surgical margins and no lymph node involvement. Therefore, the patient is considered to have a low risk of recurrence, and the bene t of radioactive iodine ablation therapy may be limited. However, this decision should be taken by a multidisciplinary team and based on the patient's individual characteristics and preferences.

Melanoma and Immunotherapy
Question Di culty Level Specialty Physiciangenerated questions AI-generated answers Accuracy Rating (mean of two raters)

Completeness
Rating (mean of two raters) Easy-Descriptive

Dermatology/ Oncology
What are some treatments for melanoma brain metastases?
Treatment options for melanoma brain metastases can include: 1. Surgery: Surgical resection of the brain metastasis can be performed if the lesion is accessible and not located in a critical area of the brain. Surgery can be done via an open craniotomy or via a minimally invasive approach such as a stereotactic biopsy or radiosurgery.
2. Radiotherapy: Whole brain radiotherapy (WBRT) or stereotactic radiosurgery (SRS) can be used to deliver a high dose of radiation to the brain metastasis while minimizing exposure to surrounding healthy brain tissue.
In some cases, a combination of WBRT and SRS can be used.
3. Systemic therapy: Targeted therapies such as immunotherapy and targeted therapy can be used for melanoma brain metastases. These therapies can help to slow the growth and spread of the cancer, and can also increase the effectiveness of other treatments.
4. Chemotherapy: Chemotherapy agents like temozolomide, dacarbazine, and lomustine can be used to treat melanoma brain metastases.
5. Supportive care: Management of symptoms such as headaches, nausea, and fatigue can help to improve the patient's quality of life.
6. Clinical trials: Patients with melanoma brain metastases may be eligible to participate in clinical trials testing new treatments such as immunotherapies, targeted therapies, and combination therapies.
It's important to note that the treatment plan for a patient with melanoma brain metastases will depend on the number and location of the brain metastases, the patient's overall health and performance status, and the patient's preferences. A multidisciplinary team of specialists in oncology, neurosurgery, radiation oncology, neurology and supportive care should work together to develop an individualized treatment plan for each patient. The decision to administer adjuvant radiation or immunotherapy to a patient with a 1.3mm depth melanoma with no ulceration and wide negative margins should be based on the patient's individual characteristics and the most recent guidelines and scienti c evidence.
Adjuvant therapy is treatment given after the primary treatment to reduce the risk of recurrence. Adjuvant therapy is not typically recommended for patients with melanoma with a depth of 1.3mm and no ulceration, as this is considered a low-risk melanoma, with a low risk of recurrence.

3
The The American College of Cardiology (ACC) and the American Heart Association (AHA) provide guidelines for the management of patients with acute myocardial infarction (MI). They recommend that nitroglycerin should be given as soon as possible to patients with suspected or con rmed acute MI unless there are contraindications.
They also recommend that nitroglycerin should be administered sublingually, or in the form of a translingual spray, or intravenously.
It's important to note that the treatment of acute MI is dynamic and the guidelines are regularly updated to incorporate new scienti c evidence. It's important to consult a healthcare provider for proper diagnosis and treatment of acute MI.

Urinary tract infection
Do prophylactic antibiotics reduce the risk of catheter associated UTI?
Prophylactic antibiotics can reduce the risk of catheterassociated urinary tract infections (CAUTIs), but their use is generally not recommended. According to the guidelines issued by the Centers for Disease Control and Prevention (CDC) and the Infectious Diseases Society of America (IDSA), prophylactic antibiotics are not recommended for use in preventing CAUTIs. The guidelines recommend that antibiotics should only be used to treat established infections, and not as a preventive measure. This is because prophylactic antibiotic use can lead to the development of antibioticresistant organisms and increase the risk of Clostridium di cile infection. Instead, the guidelines recommend infection prevention strategies such as maintaining a closed drainage system, proper catheter care, and appropriate catheter removal.

3
Meta-analysis fo reduction in incid recommend agai universal prophy The accuracy scale was a six-point Likert scale (1 -completely incorrect, 2 -more incorrect than correct, 3 -Approximately equal correct and incorrect, 4more correct than incorrect, 5 -nearly all correct, 6 -correct), and the completeness scale was a three-point Likert scale (1 -incomplete, addresses some aspects of the question, but signi cant parts are missing or incomplete, 2 -adequate, addresses all aspects of the question and provides the minimum amount of information required to be considered complete, 3 -comprehensive, addresses all aspects of the question and provides additional information or context beyond what was expected). Answers that were completely incorrect on the accuracy scale (score of 1) were not graded on comprehensiveness.   Table 3. Reevaluation of Inaccurate Chat-GPT-Generated Answer