A.I. Archives - Amazing Health Advances

MIT Study Warns Regular ChatGPT Use Erodes Critical Thinking, Creates “Cognitive Bankruptcy”

The AHA! Team — Mon, 04 Aug 2025 05:04:47 +0000

Lance D Johnson via Natural News – The MIT study exposes a troubling paradox: while AI promises to democratize learning, it may also stunt intellectual development. In an era where artificial intelligence promises to revolutionize education, a groundbreaking MIT study delivers a sobering reality check: reliance on AI tools like ChatGPT may be crippling the next generation’s ability to think independently. As schools rush to integrate large language models (LLMs) into classrooms, researchers warn that these systems are not just assisting students—they’re replacing the very cognitive processes essential for deep learning, problem-solving, and intellectual growth. The study, conducted by MIT’s Media Lab, reveals that students using ChatGPT for essay writing exhibited alarmingly low brain activity, weak memory retention, and diminished ownership of their work compared to those who relied on traditional research or their own knowledge. Key points: MIT researchers found ChatGPT users showed the lowest neural engagement and produced the weakest essays in quality, coherence, and originality. Brain scans (EEG) confirmed widespread cognitive disengagement—AI users copied and pasted text with minimal critical analysis. Google searchers performed moderately, while the “brain-only” group demonstrated the highest cognitive activation and retention. Lead researcher Nataliya Kosmyna warns policymakers against “GPT kindergarten”, fearing irreversible damage to developing minds. AI’s convenience comes at a cost: passive consumption replaces active learning, eroding problem-solving skills and intellectual autonomy. The cognitive cost of AI dependency The study divided participants into three groups: one using ChatGPT, another using Google, and a third relying solely on their own knowledge to write SAT-style essays. EEG monitoring revealed stark differences in brain activity. ChatGPT users displayed scattered, shallow neural patterns, suggesting their minds were on autopilot—processing information superficially without deep synthesis. In contrast, the brain-only group showed intense, coordinated activation across regions tied to critical thinking, memory, and creativity. “What really motivated me to put it out now before waiting for a full peer review is that I am afraid in 6-8 months, there will be some policymaker who decides, ‘let’s do GPT kindergarten,’” Kosmyna told TIME. “I think that would be absolutely bad and detrimental. Developing brains are at the highest risk.” The findings align with growing concerns about “cognitive offloading”—the tendency to outsource mental labor to machines. Unlike traditional search engines, which require users to evaluate sources and synthesize information, ChatGPT delivers pre-packaged answers, discouraging independent analysis. Researchers noted that AI users struggled to recall their own essays days later, while brain-only participants retained detailed knowledge. Education’s dangerous AI experiment The MIT study exposes a troubling paradox: while AI promises to democratize learning, it may also stunt intellectual development. Younger users, whose brains are still forming critical neural pathways, are most vulnerable. The study’s X post reaction summarized the threat succinctly: AI isn’t boosting productivity—it’s fostering “cognitive bankruptcy.” Historical context amplifies these concerns. Decades ago, educational psychologist Lev Vygotsky emphasized that struggle is essential for growth—forcing the mind to bridge gaps in understanding builds resilience and deeper comprehension. Modern pedagogy, however, increasingly prioritizes speed and convenience over cognitive rigor. The rise of LLMs risks accelerating this decline, creating a generation fluent in regurgitating AI outputs but incapable of original thought. The path forward: Balancing tech with cognitive sovereignty Not all technology undermines learning. The study’s Google group—while outperformed by brain-only peers—still engaged in active information retrieval and evaluation, exercising decision-making skills. The key difference? Search engines demand interaction; AI tools encourage passivity. To mitigate harm, experts urge: Delaying AI integration in early education until brains mature. Structuring assignments to require analysis, not just output generation. Promoting “brain-first” learning—forcing students to grapple with ideas before seeking AI help. Developing learning methods that inspire students to seek information that is useful and to question official narratives. Using AI, not in a passive capacity, but in a way that encourages critical thinking and mastering one’s own learning experience. Utilizing AI to assist in mundane capacities that free up the mind to pursue more creative or stimulating learning endeavors that matter. As AI reshapes education, society must choose: Will we raise thinkers—or just efficient mimics of machine logic? If students are provided AI tools and taught what to think, without question or reason, then kids will grow up looking to be spoon fed narratives and generalized information. If students are provided AI tools but are taught how to think, how to question, and how to master their learning experience, then kids will be better suited to navigate the propaganda and mindlessness that AI engines could impart. Sources include: Yournews.com Scribd.com Enoch, Brighteon.ai 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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AI Breakthrough Slashes Celiac Diagnosis Time from Months to Minutes

The AHA! Team — Fri, 09 May 2025 05:01:21 +0000

Cassie B. via Natural News – Cambridge researchers created an AI tool diagnosing celiac disease as accurately as human pathologists but in under a minute. The AI achieved 97% accuracy in tests using 4,000+ biopsy images, reducing wait times from months to seconds. Experts highlight AI’s potential to ease NHS backlogs but note infrastructure gaps hinder adoption. Untreated celiac disease can cause severe complications, affecting 1 in 100 people globally. British researchers at the University of Cambridge have developed an artificial intelligence tool that diagnoses celiac disease with the same accuracy as human pathologists but at a fraction of the time, potentially reducing diagnosis wait times from months to less than a minute. The breakthrough, published March 27 in the New England Journal of Medicine AI, demonstrates how market-driven technological solutions could alleviate inefficiencies plaguing government-run healthcare systems like Britain’s National Health Service (NHS), where patients routinely face lengthy wait times for diagnosis and treatment. AI matches pathologist accuracy while drastically reducing wait times The machine learning algorithm was trained on more than 4,000 biopsy images from five different hospitals and tested on an independent set of 650 previously unseen images. The results showed remarkable accuracy – correctly identifying celiac disease in more than 97% of cases, with sensitivity exceeding 95% and specificity of almost 98%. “It can take many years to receive an accurate diagnosis, and at a time of intense pressures on healthcare systems, these delays are likely to continue,” said Elizabeth Soilleux, consultant hematopathologist and professor of pathology at Cambridge University, who led the research. “AI has the potential to speed up this process, allowing patients to receive a diagnosis faster, while at the same time taking pressure off NHS waiting lists.” AI model delivers results Dr. Florian Jaeckle, co-author of the research, highlighted the dramatic time savings: while human pathologists require 5-10 minutes to analyze each biopsy, the AI model delivers results “in less than a minute and as soon as a biopsy is scanned.” “Duodenal biopsies are often put at the back of the pathologist’s lists as they are not as serious as for example a possible cancer case, meaning that patients often have to wait weeks or even months to find out if they have celiac disease,” Jaeckle explained. “With AI they could get a result almost instantly… Therefore, there would never be a waiting list with AI.” Government healthcare infrastructure lags behind innovation Despite the promising technology, the president of the Royal College of Pathologists acknowledged significant barriers to implementation within Britain’s government-run healthcare system. Dr. Bernie Croal said that while the AI tool “has the potential to radically transform how we diagnose celiac disease,” the NHS lacks the necessary digital infrastructure to fully utilize such innovations. “More work will be needed to get to the point where AI is fully developed and used safely in the NHS,” Croal admitted. “Investment in digital pathology, joined up functional IT systems… as well as training for pathologists to understand and use AI, will all need to be put in place.” These infrastructure shortcomings highlight a persistent pattern in government-managed healthcare: while private sector innovation rapidly advances diagnostic and treatment capabilities, bureaucratic systems struggle to keep pace with technological progress. Celiac disease affects approximately one in 100 people, causing symptoms including stomach cramps, diarrhea, skin rashes, weight loss, fatigue, and anemia when patients consume gluten. When left untreated, it can lead to serious complications including malnutrition, osteoporosis, infertility, and increased risk of certain cancers. The Cambridge researchers have established a spinout company, Lyzeum Ltd, to commercialize the algorithm, creating a market-based pathway for this life-improving technology to reach patients while government systems catch up. The research received funding from Coeliac UK, Innovate UK, and the Cambridge Centre for Data-Driven Discovery, demonstrating how private sector partnerships can accelerate medical breakthroughs without total reliance on government resources. Sources for this article include: TheGuardian.com Cam.ac.uk MedicalXpress.com To read the original article, click here

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Can AI Recognize the Signs of Depression in People’s Voices?

The AHA! Team — Fri, 28 Mar 2025 05:07:05 +0000

Dr. Chinta Sidharthan via News-Medical – A machine learning tool successfully identified vocal markers of depression in over 70% of cases within 25 seconds, highlighting its potential for improving mental health screening in primary care and virtual healthcare settings. In a recent article in The Annals of Family Medicine, researchers evaluated the effectiveness of a machine learning (ML) tool for detecting vocal signs linked to severe or moderate depression. The tool successfully detected vocal markers of depression in just 25 seconds, correctly identifying cases of depression in more than 70% of samples, highlighting its utility for mental health screening. Background Depression is a major health issue, affecting about 18 million Americans annually, with nearly 30% experiencing it at some point in their lives. Despite guidelines recommending universal screening, depression screening in primary care remains very low (

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The AI Tech That Can Spot Serious Illness Before the Doctor

The AHA! Team — Wed, 05 Mar 2025 06:16:00 +0000

John Jeffay via Israel21c – Lavaa Health’s platform can identify disease and hard-to-diagnose illnesses at the earliest stage, allowing doctors to quickly draw up efficient treatment plans. Meet your GP’s new best friend – artificial intelligence (AI). Lavaa Health, an Israeli startup, watches over all patient data, ready to spot early signs of potential health issues, and uses its vast medical database to identify hard-to-diagnose or rare illnesses. It’s a virtual assistant that works in the background to offer help and alerts, but leaves the physician very much in the driver’s seat, from making the diagnosis to drawing up a treatment plan. The company was founded after a family tragedy. Adam Amitai, Lavaa’s CEO, watched helplessly as his 55-year-old mother-in-law succumbed to ovarian cancer. It had taken a year for the doctors to correctly diagnose her, by which time it was too late. She died eight months later. Amitai doesn’t blame the physicians and says they provided excellent care. But he realized they weren’t exploiting the power of AI to get quicker and more accurate insights. And so he interviewed 200 physicians in the United States, to fully understand how AI could best help them. And he drew on his seven years’ experience as an “offensive cyber officer” in the IDF – where a key challenge was sifting vital details from masses of data. Amitai had also continued to work in intelligence afterwards and had set up an automated trading platform for institutional investors. So, he wasn’t from the world of healthcare, but he recognized that it could benefit from advanced systems that had been developed elsewhere. Handling data more efficiently “I understood there was a big problem with data handling in the healthcare industry,” he tells ISRAEL21c. He saw it when each of his three children were born. Every time, the doctor asked for the family’s medical history. And he saw it with the death of his mother-in-law. He believes AI would have suggested ovarian cancer as a diagnosis much sooner. The main problem is in primary care, the people who are in charge of your health on a daily basis “It’s not the physician’s fault, it’s not the care team fault, they’re doing their best, but they just don’t have the tools,” he says. “The main problem is in primary care, the people who are in charge of your health on a daily basis. They’re reactive instead of proactive. They’re trying to solve a single problem, not your whole health.” And they generally lack the resources to understand what the problem is and to diagnose it correctly. Lavaa’s AI-powered Preventive Care Engine Platform assists the physician by offering evidence-based insights. “We are not allowing the computer to try to automatically detect the conditions. We’re using the accepted worldwide care protocols, but we’re using AI to extract the data,” says Amitai. “Physicians cannot go through all of this data by themselves in the amount of time that they have. It’s just impossible, so this is giving them a huge backup. “The number of parameters for a physician to check and the number of possible diseases is infinite, and time is limited. But computers are really good at matching parameters to diseases. “I realized that technology from the intelligence world already did this, so it was a question of applying it to healthcare.” Prevention, intervention Lavaa is all about prevention and early intervention. Its AI platform can generate questions for a particular patient based on what it sees in their records. It may, for example, ask if a female patient remembers the age at which she had her first period – something that’s relevant for breast cancer, but is never recorded in an EMR (electronic medical record). Or it may send targeted messages, questionnaires, or notifications. It acts as an early warning system, designed to prevent the development of chronic or psychological diseases, and cancer. Lavaa currently looks after over 700,000 patients, all in the US, though the company has plans to expand globally. Amitai estimates the technology has so far saved 1,500 lives. “These are people who had a condition that could have been terminal but caught it on time and we managed to alert the physician, which meant the patients got either the right or better drugs, and better treatment, or a referral to the right place,” he says. Lavaa is not the only such AI solution, but Amitai says the healthcare market is big enough for everybody. Some other companies use AI to both inform and to diagnose – unlike Lavaa – or as a “black box” providing a diagnosis but no explanation of its “thinking.” The company has 12 staff members at its offices in Ra’anana, central Israel, and a team working in the US. Lavaa was founded in 2021, has attracted $5 million in investments. A Series A funding round will be launched later this year. “We want to go global,” Amitai says. “Our solution can work anywhere, and we believe it can improve healthcare around the world.” For more information, click here. To read the original article click here.

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Skin Concern? Skip Waiting for the MD and Use Your Phone

The AHA! Team — Wed, 04 Sep 2019 07:00:00 +0000

Naama Barak via Israel21c – Israeli startup DermaDetect offers users super-fast, AI-based diagnosis for hundreds of skin conditions from the comfort of their own phones. Let’s say your child has a weird skin rash. What do you do? Head to the local clinic and wait for hours until a doctor examines your child for exactly 20 seconds before prescribing medication. A day truly well-spent. Not. (DermaDetect analyzes both photos and patients’ information to provide diagnosis that overcomes the need for physical examination by a doctor. Photo by Evgeniy Kalinovskiy via Shutterstock.com) One parent who had enough of this waiting game is Israeli entrepreneur Eugene Dicker. A few years ago, a single spot appeared on his daughter’s face. They spent the obligatory time waiting for a doctor who prescribed medication, but the condition worsened and even after consulting with another doctor spots appeared over most of her body. Exasperated with the time wasted, Dicker phoned dermatology specialist Prof. Arieh Ingber, who proceeded to ask him a couple of questions before correctly diagnosing his daughter. “When all that happened over the phone, the penny dropped,” Dicker recounts. He realized that visuals aren’t the only channel for diagnosis and that he could translate the over-the-phone questioning to computerized Q&A â€“ a move that could provide patients with correct diagnoses without going to the doctor. Shortly afterward, he joined forces with Ingber and two other doctors, and the startup DermaDetect was born. “Our goal is to enable people to diagnose skin diseases using a smartphone,” he explains. Patients use the DermaDetect app to take a photo of the skin lesion and answer a few questions before receiving either a treatment or management plan. “You might have a mosquito bite that you scratched and is now bleeding, and you’ll get a treatment plan,” Dicker explains. “However, if we reach the conclusion that you have advanced psoriasis that usually can’t be immediately treated, you’ll receive a management plan.” Intelligent System The questions posed to users are intelligent, in the sense that they follow up one another based on the answers already given. “For example, if you choose an area of the lesion that can’t have hair loss, we won’t ask you about hair loss,” Dicker says. “The artificial intelligence knows how to analyze the information both from the photograph and the patient’s answers. That’s our uniqueness.” This artificial intelligence (AI) analysis is based on a deep learning process, which has involved gathering information from health systems and clinics around the world for the past two years. If the thought of leaving your health in the hands of AI scares you, worry not. “At the end of this process, all this data is conveyed in an automatic, anonymous and encrypted manner to specialist skin doctors [who] approve or disapprove the results of the diagnosis â€“ just like Tinder â€” in less than 30 seconds,” Dicker explains. “If they approve, the result is immediately transmitted back to the patient’s app,” he says. If the doctor doesn’t approve, the diagnosis reaches a panel of several other doctors who decide on the case within 72 hours. This process, Dicker stresses, enables quick and professional diagnosis at a time when waiting times are soaring due to a growing shortage of clinical dermatologists, while skin conditions are becoming more prevalent due to stress, air pollution and food pollution. “These two trends are clashing, and that’s why waiting times for dermatologists are getting longer around the world,” he says. DermaDetect, now in the midst of its second round of funding, is in the process of receiving its CE certification and expects to receive FDA approval by the end of the year. For the past six months, it has been operating a pilot in Israel in the field of pediatric skin conditions and is about to begin cooperating with a local HMO. “Our goal for the health system is to eventually bring to the clinic only the cases that need a clinic,” Dicker says. “Most people don’t need to go to the clinic because their problem can be diagnosed from afar.” The solution DermaDetect offers is unique, according to Dicker. Unlike some competitors, it only requires users to take one or two photos of a small area of the body, rather than full-body scans. And it offers diagnoses for around 350 skin conditions. It doesn’t focus on skin cancers, Dicker explains, because cancerous lesions must be examined at a clinic. One top of that, he adds, is the app’s unique capability to combine information from photo analysis and information from the patient. The company doesn’t hold onto or even see the patients’ data; the only people privy to users’ information are the doctors at the end of the process. That’s why, for example, Dicker can’t divulge the most common skin conditions the app deals with, only saying that they belong to the field of common acute dermatology, meaning non-chronic conditions. “We as a company don’t see the information about the cases and the patients. The only person to directly see it is the doctor.” Summer Skin Tips At ISRAEL21c’s request, DermaDetect offered some skin-protection tips ahead of the scorching summer. Their experts’ top tip was to avoid spending too much time in the sun and to make sure to drink plenty of water. They also recommend using high SPF protection on all areas exposed to the sun â€“ ears, noses and even scalps for those of us with a little less hair on top. Wearing wide-brimmed hats is also a good idea, they say, as well as long and dark-colored clothes because dark materials provide a higher ultraviolet protection factor (UPF). The ideal level of protection is UPF 50+, and the average white tee offers a meager UPF 15+.

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