Health Advice Archives - Amazing Health Advances

A “Game-Changer” For Your Heart

The AHA! Team — Wed, 21 May 2025 05:30:38 +0000

Al Sears, MD, CNS – Heart disease continues to be the biggest killer in America for one simple reason: The health advice we’re told to follow is just plain wrong. Giving up meat and fat, jogging, and taking a handful of medications will not cure your heart disease. But there is a cutting-edge, FDA-approved natural therapy that I call a game-changer for your heart… I’m talking about enhanced external counter pulsation, or EECP. Most cardiologists continue to ignore this treatment because it doesn’t fit the traditional image of what they do. They consider heart disease a “plumbing” problem. And the solution is to simply fix the blockages. But if that were true, why do heart attacks happen after these blockages have been cleared or bypassed? The real causes of heart disease are damaged blood vessels that inhibit blood flow and inflammation. That’s where this life-saving therapy comes in. Multiple studies reveal that EECP is the safest and most effective reliever of angina chest pain available. It has been shown to have huge benefits for patients with coronary artery disease and heart failure. It increases blood flow to the heart, strengthens circulation, and provides a proven way to treat heart disease with fewer drugs and without bypass surgeries, angioplasty procedures, or stents. And in many cases, it works better than the Big Pharma meds and risky, expensive surgeries that cardiologists continue to push. Although EECP was invented in the U.S. in the 1950s, it was left undeveloped as cardiology went down the more lucrative path of drugs and invasive surgeries. Instead, doctors overseas took up the challenge. They spent 20 years developing counter pulsation as a non-surgical way to treat coronary heart disease, by getting the timing of these devices just right. Counter pulsation means pumping blood during the heart’s rest phase. When the heart is at rest, the cuffs inflate. When the heart pumps, the cuffs deflate. The cuffs compress the blood vessels in your lower limbs and push blood toward the heart. Each wave of increased blood flow is timed to arrive at your heart the moment the organ relaxes. When your heart pumps again, pressure is released. This essentially acts as a passive form of vigorous exercise, boosting blood flow, and pushing oxygen-rich blood throughout your body more strongly than normal. More than 100 studies along with multiple clinical trials prove the effectiveness of EECP. In one large study, researchers followed more than 5,000 patients worldwide. They discovered that 83% of patients had vastly improved blood flow after EECP and 73% reported a significant reduction in the severity of angina symptoms.1 Yet in America, almost all coronary artery disease patients are prescribed Big Pharma meds instead. Regrow Brand-New Blood Vessels Perhaps the most remarkable benefit of EECP is its ability to strengthen and repair damaged blood vessels and regrow new ones, creating new pathways in and around the heart – without any surgical grafting. That is why EECP is hailed as a “natural bypass.”2 Blocked Coronary Arteries You see, when coronary arteries become blocked with plaque, obstructing blood flow, it causes chest pain and possibly a heart attack. Some people can naturally form new blood vessels that serve to bypass these obstructions. Unfortunately, not everyone can. And despite the conventional opinion that nothing can be done, EECP has worked wonders for these patients. Study involving 1,400 patients In a study involving 1,400 patients with refractory angina, 75% had half as many angina attacks after EECP. And in a three-year follow-up, 16% had no angina at all.3 Other studies show that EECP triggers the production of an important hormone called vascular endothelial growth factor (VEGF). This allows blood to bypass the blocked arteries by creating new ones.4 The effects of EECP last about five years.5 If you are interested in trying EECP to increase blood flow and protect your heart, please call the Sears Institute for Anti-Aging Medicine at 561-784-7852. 2 More Ways To Protect Your Arteries EECP is a miracle cure for unclogging arteries. But if you can’t get to my clinic, there are nutrients you can supplement with at home. Here are a couple to try today: 1. Use the B vitamin that’s proven to protect your heart. You probably know vitamin B9 better than folate or folic acid. Folate is the nutrient found in food, while folic acid is the supplement form.Folic acid lowers levels of toxic substances that irritate the heart’s lining. This relaxes your blood vessels and keeps them flexible. Fewer irritations equate to normalized pulse pressure and a reduction in stroke and heart attack. Simply put… When folate is high your risk of heart attack drops by up to 50%.6 Natural sources of folate are dark green vegetables as well as beef, lamb, chicken liver, and eggs. But your body only absorbs half the folate you get from food. I recommend supplementing with 800 mcg a day. 2. Try heart-saving vitamin K2. Vitamin K2 scrubs your arteries clear of the plaque that clogs blood vessels. In a landmark, Dutch trial researchers followed 4,800 people. Results revealed that high levels of vitamin K2 lowered the risk of coronary artery disease by 57%. It lowered calcium buildup in the arteries by 52%. And it slashed the risk of death from any cause by 26%.7 You can get vitamin K2 directly from foods. Our ancestors got plenty from eating organ meats like liver. Other rich sources are meat, full-fat milk, cottage cheese, butter, and cheese. But these foods MUST come from grass-fed animals.You can also supplement. Look for vitamin K2 in the form of “menaquinone-7.” It’s much more bioactive than other forms. Take 45 to 90 mcg a day with a meal to improve absorption. To Your Good Health, Al Sears, MD, CNS References: Soran O. “Two-year clinical outcomes after Enhanced External Counterpulsation (EECP) therapy in patients with refractory angina pectoris and left ventricular dysfunction (Report from the International EECP Patient Registry). Am J Cardiol. 2006;97(1):17–20. Kiefer D. “Doctors ignore proven alternative to coronary stents and bypass surgery.” Life Extension. Loh PH, et al. “Enhanced external counterpulsation in the treatment of chronic refractory angina: a long-term follow-up outcome from the International Enhanced External Counterpulsation Patient Registry.” Clin Cardiol. 01 Apr 2008, 31(4):159-164 Sharma U, et al. “The role of enhanced external counter pulsation therapy in clinical practice.” Clin Med Res. 2013 Dec;11(4):226-32. Fitzgerald CP, et al. “Enhanced external counterpulsation as initial revascularization treatment for angina refractory to medical therapy.” Cardiology. 2003;100(3):129-35. Pan Y and Jackson R. “Dietary phylloquinone intakes and metabolic syn¬drome in US young adults.” J Am Coll Nutr. 2009;28(4):369-379. Geleijnse JM., et al. “Dietary Intake of Menaquinone Is Associated with a Reduced Risk of Coronary Heart Disease: The Rotterdam Study.” J Nutr. 2004. 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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