medical advice Archives - Amazing Health Advances

Knee Arthroscopic Surgery for Meniscus Tears

The AHA! Team — Wed, 25 Jun 2025 05:36:09 +0000

Georgia M. Beasley, MD, MHSc, via Duke Health – The knee is one of the most commonly injured parts of the body, and meniscus tears are often the cause of knee pain and knee injury. The meniscus is the tough, rubbery cartilage that absorbs shock between the shin bone and thigh bone and distributes weight across the knee joint. When this cartilage tears, it can cause pain and instability in the knee joint. Meniscus tears can result from a twisting injury in sporting activities, such as football or soccer, or even something as simple as turning to put the dishes away. Symptoms of Meniscus Tears People of all ages can suffer from meniscus injuries, but each age has different types of tears and different ways to treat the tears. Almost all tears have similar symptoms, including: Pain Swelling Tenderness Giving way Mechanical symptoms, such as locking, popping, and catching Diagnosing a Meniscus Tear When you experience these symptoms, it is important to see an orthopaedic surgeon so your knee can be examined and an accurate diagnosis made. Occasionally, the diagnosis is obvious based upon a description of the injury and an examination of the patient. However, X-rays and magnetic resonance imaging (MRI) are frequently used to help identify any other associated injuries. Most common findings The most common findings on exam include tenderness over the joint line where the meniscus is torn, swelling, and sometimes loss of motion. The most important to report is whether you have mechanical symptoms such as episodes of feeling like your knee is caught or stuck. Once the diagnosis of a meniscus tear is made, you should discuss your treatment plan with your orthopaedic surgeon. For most people who have a symptomatic meniscus tear with mechanical symptoms, arthroscopic surgery is selected to remove or repair the torn tissue. However, if you have arthritis, you may benefit from injection and physical therapy without surgery. Arthroscopy has revolutionized how knee surgery is performed. In the past, a torn meniscus required a three- to four-inch incision and an overnight stay (or two) in the hospital. Now, the meniscus tear can be repaired with the arthroscope through two tiny (less than a half-inch) incisions. The surgery can be performed on an outpatient basis in less than an hour. Typically, the surgery can be performed under regional anesthesia with sedation, so there’s minimal anesthesia risk. Occasionally, small stitches can be placed into the torn meniscus to sew it back together; this technique can successfully treat large tears in younger people. If the tear is small, it may be removed. Quick Recovery Time Recovery from arthroscopic meniscus tear surgery is relatively quick, and most people can resume normal activities within a few weeks depending on the size of the tear and the repair involved. The pain relief is dramatic, and the postoperative incision pain is quite minimal. Physical therapy is often necessary in the recovery process. As with any surgery, there are risks, including the risk of infection or blood clots. Additionally, there are risks associated with anesthesia used during the surgical procedure. While meniscus tears are common, painful, and activity-limiting, these injuries can be quickly, easily, and successfully identified and treated. To read the original article click here.

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Cancers Can Be Detected in the Bloodstream Three Years Prior to Diagnosis

The AHA! Team — Fri, 20 Jun 2025 05:20:22 +0000

Johns Hopkins Medicine via Newswise – The study, partly funded by the National Institutes of Health, was published May 22 in Cancer Discovery. Genetic material shed by tumors can be detected in the bloodstream three years prior to cancer diagnosis, according to a study led by investigators at the Ludwig Center at Johns Hopkins, Johns Hopkins Kimmel Cancer Center, the Johns Hopkins University School of Medicine and the Johns Hopkins Bloomberg School of Public Health. The study, partly funded by the National Institutes of Health, was published May 22 in Cancer Discovery. Investigators were surprised they could detect cancer-derived mutations in the blood so much earlier, says lead study author Yuxuan Wang, M.D., Ph.D., an assistant professor of oncology at the Johns Hopkins University School of Medicine. “Three years earlier provides time for intervention. The tumors are likely to be much less advanced and more likely to be curable.” To determine how early cancers could be detected prior to clinical signs or symptoms, Wang and colleagues assessed plasma samples that were collected for the Atherosclerosis Risk in Communities (ARIC) study, a large National Institutes of Health-funded study to investigate risk factors for heart attack, stroke, heart failure and other cardiovascular diseases. They used highly accurate and sensitive sequencing techniques to analyze blood samples from 26 participants in the ARIC study who were diagnosed with cancer within six months after sample collection, and 26 from similar participants who were not diagnosed with cancer. At the time of blood sample collection, eight of these 52 participants scored positively on a multicancer early detection (MCED) laboratory test. All eight were diagnosed within four months following blood collection. For six of the eight individuals, investigators also were able to assess additional blood samples collected 3.1–3.5 years prior to diagnosis, and in four of these cases, tumor-derived mutations could also be identified in samples taken at the earlier timepoint. MCED tests “This study shows the promise of MCED tests in detecting cancers very early, and sets the benchmark sensitivities required for their success,” says Bert Vogelstein, M.D., Clayton Professor of Oncology, co-director of the Ludwig Center at Johns Hopkins and a senior author on the study. Detecting cancers years before their clinical diagnosis “Detecting cancers years before their clinical diagnosis could help provide management with a more favorable outcome,” adds Nickolas Papadopoulos, Ph.D., professor of oncology, Ludwig Center investigator and senior author of the study. “Of course, we need to determine the appropriate clinical follow-up after a positive test for such cancers.” The study was supported in part by National Institutes of Health grant #s R21NS113016, RA37CA230400, U01CA230691, P30 CA 06973, DRP 80057309, and U01 CA164975. Additional funding was provided by the Virginia and D.K. Ludwig Fund for Cancer Research, the Commonwealth Fund, the Thomas M Hohman Memorial Cancer Research Fund, The Sol Goldman Sequencing Facility at Johns Hopkins, The Conrad R. Hilton Foundation, the Benjamin Baker Endowment, Swim Across America, Burroughs Wellcome Career Award for Medical Scientists, Conquer Cancer – Fred J. Ansfield, MD, Endowed Young Investigator Award, and The V Foundation for Cancer Research. The Atherosclerosis Risk in Communities study has been funded in whole or in part with federal funds from the National Heart, Lung, and Blood Institute, National Institutes of Health, Department of Health and Human Services, under contract numbers 75N92022D00001, 75N92022D00002, 75N92022D00003, 75N92022D00004, and 75N92022D00005. To read the original article click here.

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Who Gives Better Health Advice – ChatGPT or Google?

The AHA! Team — Mon, 19 May 2025 05:09:42 +0000

Dr. Chinta Sidharthan via News-Medical – Can AI chatbots like ChatGPT give better medical answers than Google? A new study shows they can — but only if you ask them the right way. How reliable are search engines and artificial intelligence (AI) chatbots when it comes to answering health-related questions? In a recent study published in NPJ Digital Medicine, Spanish researchers investigated the performance of four major search engines and seven large language models (LLMs), including ChatGPT and GPT-4, in answering 150 medical questions. The findings revealed interesting patterns in accuracy, prompt sensitivity, and retrieval-augmented model effectiveness. Large language models Some of the biggest failures by AI chatbots involved confidently giving answers that went against medical consensus, making these mistakes particularly dangerous in health settings. The internet has now become a primary source of health information The internet has now become a primary source of health information, with millions relying on search engines to find medical advice. However, search engines often return results that may be incomplete, misleading, or inaccurate. Large language models Large language models (LLMs) have emerged as alternatives to regular search engines and are capable of generating coherent answers based on vast training data. However, while recent studies have examined the performance of LLMs in specialized medical domains, such as fertility and genetics, most evaluations have focused on a single model. Additionally, there is little research comparing LLMs with traditional search engines in health-related contexts, and few studies explore how LLM performance changes under different prompting strategies or when combined with retrieved evidence. The accuracy of search engines and LLMs also depends on factors such as input phrasing, retrieval bias, and model reasoning capabilities. Moreover, despite their promise, LLMs sometimes generate misinformation, raising concerns about their reliability. Investigating LLM accuracy The present study aimed to assess the accuracy and performance of search engines and LLMs by evaluating their effectiveness in answering health-related questions and the impact of retrieval-augmented approaches. The researchers tested four major search engines The researchers tested four major search engines — Yahoo!, Bing, Google, and DuckDuckGo — and seven LLMs, including GPT-4, ChatGPT, Llama3, MedLlama3, and Flan-T5. Among these, GPT-4, ChatGPT, Llama3, and MedLlama3 generally performed best, while Flan-T5 underperformed. The evaluation involved 150 health-related binary (yes or no) questions sourced from the Text Retrieval Conference Health Misinformation Track and covered diverse medical topics. Search engines often returned top results that didn’t answer the question directly, but when they did, those answers were usually correct — highlighting a precision problem rather than accuracy. Search engines For search engines, the top 20 ranked results were analyzed. A passage extraction model was employed to identify relevant snippets, and a reading comprehension model determined whether each snippet provided a definitive answer. Additionally, user behaviors were simulated using two models: a “lazy” user who stops at the first yes or no answer and a “diligent” user who cross-references three sources before deciding. Interestingly, the study found that ‘lazy’ users achieved similar accuracy to ‘diligent’ users and, in some cases, even performed better, suggesting that top-ranked search engine results may often suffice—though this raises concerns when incorrect information ranks highly. For LLMs For LLMs, the questions were tested under different prompting conditions: no-context (just the question), non-expert (prompts were framed in the language used by laypeople), and expert (prompts were framed for guiding responses toward reputable sources). The study also tested few-shot prompts—adding a few example questions and answers to guide the model—which improved performance for some models but had limited effect on the best-performing LLMs. The study also explored retrieval-augmented generation, where LLMs were fed search engine results before generating responses. Performance Performance was assessed based on accuracy in correctly answering the questions, sensitivity to input phrasing, and improvements gained through retrieval augmentation. The researchers also used statistical significance tests to determine meaningful performance differences between models. Although some LLMs outperformed others, statistical tests showed that in many cases, performance differences between leading models were not significant, indicating that top LLMs performed comparably in many instances. Furthermore, the researchers categorized common LLM errors, such as misinterpretation, ambiguity, and contradictions with medical consensus. The study also noted that while the “expert” prompt generally guided LLMs toward more accurate responses, it sometimes increased the ambiguity of their answers. Key findings COVID-19 questions proved easier for both LLMs and search engines, likely because pandemic-related data dominated their training and indexing periods. The study found that LLMs generally outperformed search engines in answering health-related questions. While search engines correctly answered 50–70% of queries, LLMs achieved approximately 80% accuracy. However, LLM performance was highly sensitive to input phrasing, with different prompts yielding significantly varied results. The “expert” prompt, which guided LLMs toward medical consensus, was found to perform the best, although it sometimes led to less definitive answers. Among the search engines, Bing provided the most reliable results, but it was not significantly better than Google, Yahoo!, or DuckDuckGo. Moreover, many search engine results contained non-responsive or off-topic information, contributing to lower precision. However, when focusing only on responses that addressed the question, search engine precision rose to 80–90%, though about 10–15% of these still contained incorrect answers. ‘Lazy’ users Furthermore, contrary to common assumptions, the study found that ‘lazy’ users sometimes achieved similar or better accuracy with less effort, highlighting both the efficiency and the risk of trusting initial search results. Additionally, the researchers observed that retrieval-augmented methods improved LLM performance, especially for smaller models. By integrating top-ranked search engine snippets, even lightweight models such as text-davinci-002 performed similarly to GPT-4. However, the study noted that retrieval augmentation sometimes decreased performance, especially when low-quality or irrelevant search results were fed into LLMs—emphasizing the critical role of retrieval quality. For some datasets, like COVID-19-related questions from 2020, adding search engine evidence even worsened LLM performance, possibly because these questions were already well-covered in LLM training data. Feeding AI chatbots search results didn’t always help; in some cases, irrelevant or low-quality snippets actually made chatbot answers worse, showing that more information isn’t always better. Error analysis The error analysis also revealed three major failure modes for LLMs, including incorrect medical consensus understanding, misinterpretation of questions, and ambiguous answers. Notably, some health-related questions were inherently difficult, and both LLMs and search engines struggled to provide correct answers to these questions. The study also found that performance varied depending on the dataset: questions from 2020, largely focused on COVID-19, were easier for both LLMs and search engines, while the 2021 dataset presented more challenging medical questions. Overall, while LLMs demonstrated superior accuracy, their propensity to prompt variations and misinformation highlighted the need for caution in medical decision-making based on LLM answers. The study also suggested combining LLMs with search engines through retrieval augmentation could yield more reliable health answers, but only when the retrieved evidence is accurate and relevant. Conclusions In summary, the study highlighted search engines’ and LLMs’ strengths and weaknesses in answering health-related questions. While LLMs generally outperformed search engines, their accuracy was found to be highly dependent on input prompts and retrieval augmentation. Although advanced models like GPT-4 and ChatGPT performed well, other models such as Llama3 and MedLlama3 sometimes matched or even outperformed them, depending on the dataset and prompting strategy. Moreover, while combining both technologies appears promising, ensuring the reliability of retrieved information remains a challenge. The researchers emphasized that smaller LLMs when supported with high-quality search evidence, can perform on par with much larger models—raising questions about the need for ever-larger AI models when retrieval augmentation could be a viable alternative. These results suggested that future research should explore methods to enhance LLM trustworthiness and mitigate misinformation in health-related AI applications. Journal reference: Fernández-Pichel, M., Pichel, J.C. & Losada, D.E. (2025). Evaluating search engines and large language models for answering health questions. NPJ Digital Medicine. 8, 153. DOI:10.1038/s41746-025-01546-w, https://www.nature.com/articles/s41746-025-01546-w To read the original article click here.

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