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Business Upturn
6 hours ago
- Business
- Business Upturn
Ant Group Launches AI Healthcare App AQ Amid Push into Healthcare Sector
By Business Wire India Published on June 27, 2025, 11:12 IST Hangzhou, China: Digital leader aims to capture rising cross-generational demand for public and private healthcare services amid demographic shift and urbanization trends. The launch of AQ enhances Ant Group's comprehensive suite of AI solutions for healthcare, enabling medical institutions and doctors to offer more efficient, accessible, and personalized services. Ant Group today launched its AI healthcare app, AQ, to accelerate the company's entry into the healthcare sector. The app helps users manage their daily healthcare needs with over 100 AI-powered services, including doctor recommendations, medical report analysis, and personalized medical advice. It also connects users to digital services from over 5,000 hospitals and nearly 1 million doctors across China. This press release features multimedia. View the full release here: Ant Group launched the AI healthcare app AQ to accelerate its entry into the healthcare sector By around 2035, over 400 million people, or more than 30% of China's population, will be aged 60 or older, according to projections from China's National Health Commission. This demographic shift is expected to significantly increase healthcare demand in the country. 'Ant Group hopes that through AQ, it can provide everyone with a trusted healthcare manager, advancing inclusive healthcare and bringing every Chinese citizen one step closer to a healthier life,' said Cyril Han, CEO of Ant Group. The AQ app is powered by Ant Group's Healthcare Large Model, which draws on over a decade of expertise in the healthcare sector. Since 2014, when Ant Group launched its first online hospital appointment-making service on the flagship Alipay digital platform, the company has been supporting the digital and intelligent transformation of China's healthcare industry. The Healthcare Large Model, equipped with advanced medical reasoning and multimodal interaction capabilities, has consistently ranked in first place in the HealthBench and MedBench evaluations. Furthermore, it leverages Ant Group's leading privacy and security technologies to ensure a safe and reliable experience. Through this model, Ant Group is empowering medical institutions and doctors to offer more efficient, accessible, and personalized services to users with AI-enabled solutions. Empowering Medical Institutions with Secure and Efficient AI Integration Healthcare Large Model Integration In March 2025, Ant Group collaborated with IT industry leaders to integrate its advanced healthcare large model into various types of All-in-One Large Model Machine for Healthcare. This solution allows hospitals to deploy AI models on-premises, ensuring efficient and secure use of AI to optimize daily operations and improve patient services. AI Assistant for Hospital Services Ant Group is also helping hospitals develop user-facing applications. Angel, an AI agent developed in collaboration with public medical institutions in China's Zhejiang Province, has served over 1,000 medical facilities, handling more than 50 million user interactions. AI Medical Insurance Assistant Additionally, Ant Group has supported local basic medical insurance institutions across China in developing Yibaoer, an AI agent designed to assist users with medical insurance-related inquiries. AI-Assisted Diagnosis and Patient Education: Extending Doctor Capabilities Beyond Time and Place Constraints AI Doctor Assistant In January 2025, Ant Group completed the acquisition of Haodf, a leading healthcare platform specializing in online doctor consultations. Together, the two sides launched the AI Doctor Assistant, which supports doctors with AI-assisted diagnosis, medical record management, and patient education. AI Doctor Agents Ant Group is also collaborating with nearly 200 prominent doctors in China to develop AI Doctor Agents that provide patients with credible, authoritative healthcare advice and medical guidance. This initiative not only empowers individuals to maintain better health but also supports those with limited access to medical resources, ensuring timely, expert care when it's needed most. Through these ongoing initiatives, Ant Group is contributing to the more efficient use of limited medical resources, with the aim of supporting greater medical inclusion. About Alipay As the world becomes increasingly digital, Alipay has evolved from a trusted e-wallet into an all-in-one digital platform for daily services, connecting more than one billion consumers to over 80 million merchants across China. Alipay offers users a secure, seamless mobile payment experience and integrates over 10,000 services across sectors like travel, healthcare, tourism, and entertainment. With digital tools like Alipay Tap!, mini-programs, lifestyle accounts, Alipay enables merchants, institutions, and independent software vendors (ISVs) to enhance operational efficiency and effectiveness. In addition, Alipay is developing a new AI-driven open platform by integrating AI agents to deliver smarter, more personalized services to its users as well as facilitating the digital transformation of the service sector. About Ant Group Ant Group is a global digital technology provider and the operator of Alipay, a leading internet services platform in China, connecting over one billion users to more than 10,000 types of consumer services from partners. Through innovative products and solutions powered by AI, blockchain and other technologies, Ant Group supports partners across industries to thrive through digital transformation in an ecosystem for inclusive and sustainable development. For more information, visit View source version on Disclaimer: The above press release comes to you under an arrangement with Business Wire. Business Upturn takes no editorial responsibility for the same. Ahmedabad Plane Crash Business Wire India, established in 2002, India's premier media distribution company ensures guaranteed media coverage through its network of 30+ cities and top news agencies.
Business Wire
a day ago
- Business
- Business Wire
Ant Group Launches AI Healthcare App AQ Amid Push into Healthcare Sector
BUSINESS WIRE)--Ant Group today launched its AI healthcare app, AQ, to accelerate the company's entry into the healthcare sector. The app helps users manage their daily healthcare needs with over 100 AI-powered services, including doctor recommendations, medical report analysis, and personalized medical advice. It also connects users to digital services from over 5,000 hospitals and nearly 1 million doctors across China. By around 2035, over 400 million people, or more than 30% of China's population, will be aged 60 or older, according to projections from China's National Health Commission. This demographic shift is expected to significantly increase healthcare demand in the country. 'Ant Group hopes that through AQ, it can provide everyone with a trusted healthcare manager, advancing inclusive healthcare and bringing every Chinese citizen one step closer to a healthier life,' said Cyril Han, CEO of Ant Group. The AQ app is powered by Ant Group's Healthcare Large Model, which draws on over a decade of expertise in the healthcare sector. Since 2014, when Ant Group launched its first online hospital appointment-making service on the flagship Alipay digital platform, the company has been supporting the digital and intelligent transformation of China's healthcare industry. The Healthcare Large Model, equipped with advanced medical reasoning and multimodal interaction capabilities, has consistently ranked in first place in the HealthBench and MedBench evaluations. Furthermore, it leverages Ant Group's leading privacy and security technologies to ensure a safe and reliable experience. Through this model, Ant Group is empowering medical institutions and doctors to offer more efficient, accessible, and personalized services to users with AI-enabled solutions. Empowering Medical Institutions with Secure and Efficient AI Integration Healthcare Large Model Integration In March 2025, Ant Group collaborated with IT industry leaders to integrate its advanced healthcare large model into various types of All-in-One Large Model Machine for Healthcare. This solution allows hospitals to deploy AI models on-premises, ensuring efficient and secure use of AI to optimize daily operations and improve patient services. AI Assistant for Hospital Services Ant Group is also helping hospitals develop user-facing applications. Angel, an AI agent developed in collaboration with public medical institutions in China's Zhejiang Province, has served over 1,000 medical facilities, handling more than 50 million user interactions. AI Medical Insurance Assistant Additionally, Ant Group has supported local basic medical insurance institutions across China in developing Yibaoer, an AI agent designed to assist users with medical insurance-related inquiries. AI-Assisted Diagnosis and Patient Education: Extending Doctor Capabilities Beyond Time and Place Constraints AI Doctor Assistant In January 2025, Ant Group completed the acquisition of Haodf, a leading healthcare platform specializing in online doctor consultations. Together, the two sides launched the AI Doctor Assistant, which supports doctors with AI-assisted diagnosis, medical record management, and patient education. AI Doctor Agents Ant Group is also collaborating with nearly 200 prominent doctors in China to develop AI Doctor Agents that provide patients with credible, authoritative healthcare advice and medical guidance. This initiative not only empowers individuals to maintain better health but also supports those with limited access to medical resources, ensuring timely, expert care when it's needed most. Through these ongoing initiatives, Ant Group is contributing to the more efficient use of limited medical resources, with the aim of supporting greater medical inclusion. About Alipay As the world becomes increasingly digital, Alipay has evolved from a trusted e-wallet into an all-in-one digital platform for daily services, connecting more than one billion consumers to over 80 million merchants across China. Alipay offers users a secure, seamless mobile payment experience and integrates over 10,000 services across sectors like travel, healthcare, tourism, and entertainment. With digital tools like Alipay Tap!, mini-programs, lifestyle accounts, Alipay enables merchants, institutions, and independent software vendors (ISVs) to enhance operational efficiency and effectiveness. In addition, Alipay is developing a new AI-driven open platform by integrating AI agents to deliver smarter, more personalized services to its users as well as facilitating the digital transformation of the service sector. About Ant Group Ant Group is a global digital technology provider and the operator of Alipay, a leading internet services platform in China, connecting over one billion users to more than 10,000 types of consumer services from partners. Through innovative products and solutions powered by AI, blockchain and other technologies, Ant Group supports partners across industries to thrive through digital transformation in an ecosystem for inclusive and sustainable development. For more information, visit
The Hindu
12-06-2025
- Health
- The Hindu
Benchmarks in medicine: the promise and pitfalls of evaluating AI tools with mismatched yardsticks
In May 2024, OpenAI released HealthBench, a new benchmarking system to test the clinical capabilities of large language models (LLMs) such as ChatGPT. On the surface, this may sound like yet another technical update. But for the medical world, it marked an important moment—a quiet acknowledgement that our current ways of evaluating medical AI are fundamentally wrong. Headlines in the recent past have trumpeted that AI 'outperforms doctors' or 'aces medical exams.' The impression that's coming through is these models are smarter, faster, and perhaps even safer. But this hype masks a deeper truth. To put it plainly, the benchmarks used to arrive at these claims are based on exams built for evaluating human memory retention from classroom teachings. They reward fact recall, not clinical judgment. A calculator problem A calculator can multiply two six-digit numbers within seconds. Impressive, no doubt. But does this mean calculators are better than, and understand maths more than mathematics experts ? Or better even than an ordinary person who takes a few minutes to do the calculation with a pen and paper? Language models are celebrated because they can churn out textbook-style answers to MCQs and fill in the blanks for medical facts and questions faster than medical professors. But the practice of medicine is not a quiz. Real doctors deal with ambiguity, emotion, and decision-making under uncertainty. They listen, observe, and adapt. The irony is that while AI beats doctors in answering questions, it still struggles to generate the very case vignettes that form the basis of those questions. Writing a good clinical scenario from real patients in clinical practice requires understanding human suffering, filtering irrelevant details, and framing the diagnostic dilemma with context. So far, that remains a deeply human ability. Also Read: Why AI in healthcare needs stringent safety protocols What existing benchmarks miss Most widely-used benchmarks—MedQA, PubMedQA, MultiMedQA—pose structured questions with one 'correct' answer or have fill in the blanks questions. They evaluate factual accuracy but overlook human nuance. A patient doesn't say, 'I have been using a faulty chair and sitting in the wrong posture for long hours and have a non-specific backache ever since I bought it. So please choose the best diagnosis and give appropriate treatment.' They just say, 'Doctor, I'm tired. I don't feel like myself.' That is where the real work begins. Clinical environments are messy. Doctors deal with overlapping illnesses, vague symptoms, incomplete notes, and patients who may be unable—or unwilling—to tell the full story. Communication gaps, emotional distress, and even socio-cultural factors influence how care unfolds. And yet, our evaluation metrics continue to look for precision, clarity, and correctness—things that the real world rarely provides. Benchmarking vs reality It can be easy to decide who the best batter in the world is, by only counting runs scored. Similarly, bowlers can be ranked by the number of wickets taken. But answering the question 'Who is the best fielder?' might not be as simple. Measuring fielding is very subjective and evades simple numbers. The number of runs outs assisted or catches taken only tells part of the story. The efforts made at the boundary line to reduce runs or mere intimidation through the presence of the fielders (like Jonty Rhodes or R. Jadeja) preventing runs at covers or points can't be measured easily. Healthcare is like fielding: it is qualitative, often invisible, deeply contextual, and hard to quantify. Any benchmark that pretends otherwise will mislead more than it illuminates. This is not a new problem. In 1946, the civil servant Sir Joseph Bhore, when consulted to reform India's healthcare said, 'If it were possible to evaluate the loss, which this country annually suffers through the avoidable waste of valuable human material and the lowering of human efficiency through malnutrition and preventable morbidity, we feel that the result would be so startling that the whole country would be aroused and would not rest until a radical change had been brought about'. This quote reflects a longstanding dilemma—how to measure what truly matters in health systems. Even after 80 years, we have not found perfect evaluation metrics. What HealthBench does HealthBench at least acknowledges this disconnect. Developed by OpenAI in collaboration with clinicians, it moves away from traditional multiple-choice formats. It is also the first benchmark to explicitly score responses using 48,562 unique rubric criteriaranging from minus 10 to plus 10, reflecting some aspects of real-world stakes of clinical decision-making. A dangerously wrong answer must be punished more harshly than a mildly useful one. This, finally, mirrors medicine's moral landscape. Even so, HealthBench has limitations. It evaluates performance across just 5,000 'simulated' clinical cases, of which only 1,000 are classified as 'difficult.' That is a vanishingly small slice of clinical complexity. Though commendably global, its doctor-rater pool includes just 262 physicians from 60 countries in 52 languages, with varying professional experience and cultural backgrounds (three Physicians from India participated, and simulations from 11 Indian languages were generated). HealthBench Hard, a challenging subset of 1,000 cases, revealed that many existing models scored zero—highlighting their inability to handle complex clinical reasoning. Moreover, these cases are still simulations. Thus, the benchmark is an improvement, not a revolution. Also Read: Artificial Intelligence in healthcare: what lies ahead Predictive AI's collapse in the real world This is not just about LLMs. Predictive models have faced similar failures. The sepsis prediction tool, developed by EPIC to flag early signs of sepsis, showed initial promise a few years ago. However, once deployed, it could not meaningfully improve outcomes. Another company that claimed to have developed a detection algorithm for liver transplantation recipients folded quietly after its model showed bias against young patients in Britain. It failed in the real world despite glowing performances on benchmark datasets. Why? Because predicting rare/critical events requires context-aware decision-making. A seemingly unknown determinant may lead to wrong predictions and unnecessary ICU admissions. The cost of error is high—and humans often bear it. What makes a good benchmark? A robust medical benchmark should meet four criteria: Represent reality: Include incomplete records, contradictory symptoms, and noisy environments. Test communication: Measure how well a model explains its reasoning, not just what answer it gives. Handle edge cases: Evaluate performance on rare, ethically complex, or emotionally charged scenarios. Reward safety over certainty: Penalise overconfident wrong answers more than humble uncertainty. Currently, most benchmarks miss these criteria. And without these elements, we risk trusting technically smart but clinically naïve models. Red teaming the models One way forward is red teaming—a method borrowed from cybersecurity, where systems are tested against ambiguous, edge-case, or morally complex scenarios. For example: a patient in mental distress whose symptoms may be somatic; an undocumented illegal immigrant fearful of disclosing travel history; a child with vague neurological symptoms and an anxious parent pushing for a CT scan; a pregnant woman with religious objections to blood transfusion; a terminal cancer patient is unsure whether to pursue aggressive treatment or palliative care; a patient feigning for personal gain. In these edge cases, models must go beyond knowledge. They must display judgment—or, at the very least, know when they don't know. Red teaming does not replace benchmarks. But it adds a deeper layer, exposing overconfidence, unsafe logic, or lack of cultural sensitivity. These flaws matter more than ticking the right answer box in real-world medicine. Red teaming forces models to reveal what they know and how they think. It uncovers these aspects, which may be hidden in benchmark scores. Why this matters The core tension is this: medicine is not just about getting answers right. It is about getting people right. Doctors are trained to deal with doubts, handle exceptions, and recognise cultural patterns not taught in books (doctors also miss a lot). AI, by contrast, is only as good as the data it has seen and the questions it has been trained on. HealthBench, for all its flaws, is a small but vital course correction. It recognises that evaluation needs to change. It introduces a better scoring rubric. It asks harder questions. That makes it better. But we must remain cautious. Healthcare is not like image recognition or language translation. A single incorrect model output can mean a lost life and a ripple effect—misdiagnoses, lawsuits, data breaches, and even health crises. In the age of data poisoning and model hallucination, the stakes are existential. The road ahead We must stop asking if AI is better than doctors. That is not the right question. Instead, we should ask: Where is AI safe, useful, and ethical to deploy—and where is it not? Benchmarks, if thoughtfully redesigned, can help answer that. AI in healthcare is not a competition to win. It is a responsibility to share. We must stop treating model performance as a leaderboard sport and start thinking of it as a safety checklist. Until then, AI can assist. It can summarise. It can remind. However, it cannot replace clinical judgment's moral and emotional weight. It certainly cannot sit beside a dying patient and know when to speak and when to stay silent. (Dr. C. Aravinda is an academic and public health physician. The views expressed are personal. aravindaaiimsjr10@
Cision Canada
16-05-2025
- Business
- Cision Canada
New Healthcare AI Models Could Reshape Everything From Burnout to Diagnostics
Issued on behalf of Avant Technologies Inc. VANCOUVER, BC, May 16, 2025 /CNW/ -- Equity Insider News Commentary – AI is no longer just assisting healthcare — it's beginning to rewire it from the ground up. From streamlining diagnostics and automating clinical documentation to predicting disease and optimizing hospital operations, generative AI is now touching every layer of the care continuum. As policymakers in places like Connecticut debate how to regulate this growing influence, and initiatives like OpenAI's HealthBench push the frontier of model evaluation, a new class of enterprise-scale innovators is already moving ahead. Among them are several public companies straddling health tech, cloud AI, and data infrastructure — including Avant Technologies, Inc. (OTCQB: AVAI), Palantir Technologies Inc. (NASDAQ: PLTR), GE HealthCare Technologies Inc. (NASDAQ: GEHC), Salesforce, Inc. (NYSE: CRM), and Alphabet Inc. (NASDAQ: GOOG, GOOGL). Industry analysts at MarketsandMarkets project that the AI in healthcare market will expand at a compound annual growth rate of 38.6%, reaching over $110 billion by 2030. Looking further ahead, Accenture estimates that AI could unlock an additional $461 billion in value by 2035—augmenting a global healthcare sector already expected to exceed $2.26 trillion. Avant Technologies, Inc. (OTCQB: AVAI) is quietly but deliberately advancing its position in AI-powered healthcare through a proposed acquisition of its joint venture partner, Ainnova Tech. The two companies, already aligned under the Ai-nova Acquisition Corp. (AAC) banner, are now moving to unify operations—an intentional step that comes just ahead of their scheduled FDA pre-submission meeting this July. If completed, the merger would remove internal friction, streamline clinical trial planning, and strengthen their regulatory posture ahead of potential U.S. market entry. "We believe bringing the two companies together will offer tremendous value for shareholders," said Vinicio Vargas, CEO at Ainnova and a member of the Board of Directors of Ai-nova Acquisition Corp."It will simplify the process of advancing our technology to market, and it will deliver value to our customers and partners as we promote our technology portfolio globally." At the core of this effort is Vision AI, a non-invasive clinical screening platform that combines retinal imaging, vital sign capture, and machine-learning algorithms to detect early signals of chronic illness—including diabetic retinopathy, cardiovascular disease, kidney and liver conditions, and type 2 diabetes. Operating under AAC, the joint venture holds global rights to the platform, which has shown more than 90% sensitivity in early detection, according to NIH -cited research. "This milestone reflects our two-tiered strategy, rapid deployment in low-regulation markets where Vision AI operates as a screening tool, and simultaneous progress toward FDA clearance for the U.S. market," said Vargas. "Entering the U.S. will unlock significant commercial potential, and early engagement with regulators ensures we do so with speed, credibility, and a validated product." Unlike many healthcare AI startups still stuck at concept stage, Avant's technology is already deployed in Latin America —including Chile, Mexico, and Brazil —where it's being tested in real-world clinical workflows. These field programs are not only helping build a safety and efficacy track record, they're also providing critical user feedback that shapes the platform's refinement and usability. To support broader clinical reach, AAC recently integrated four new diagnostic algorithms into Vision AI. Trained on over 2.3 million clinical cases, these additions enhance the system's utility across a wider range of chronic conditions. With proven traction abroad and a pending regulatory milestone in the U.S., Avant is moving from potential to presence—and may soon find itself on the radar of a much larger healthcare conversation. Palantir Technologies Inc. (NASDAQ: PLTR) has entered a long-term partnership with The Joint Commission, the leading healthcare accreditation body in the U.S., to apply its AI and data analytics platform to improve patient safety and operational efficiency. " The Joint Commission is committed to building the accreditation and certification process of the future, today," says Alex Karp, co-founder and CEO of Palantir Technologies. "This work will improve global health outcomes by utilizing AI to drive performance improvements around the world." The collaboration aims to modernize how hospitals manage quality standards, streamline certification processes, and enhance clinical performance. Palantir's platform is already delivering results across major healthcare systems, including Tampa General and Cleveland Clinic. GE HealthCare Technologies Inc. (NASDAQ: GEHC) recently unveiled CleaRecon DL, an FDA -cleared, AI-based solution aimed at elevating image quality in cone-beam CT (CBCT) procedures. Powered by deep learning, the tool addresses long-standing image distortion challenges caused by blood flow and contrast variability, especially in interventional settings like liver and neuro procedures. Clinical validation shows a 94% increase in interpretation confidence and a 98% improvement in image clarity compared to traditional CBCT. "The introduction of CleaRecon DL represents a leap forward in the interventional suite and for the advancement of CBCT," said Arnaud Marie, General Manager, Interventional Solutions at GE HealthCare. "By improving image quality and reducing artifacts, this technology can empower clinicians to perform procedures with greater precision and confidence. This solution builds on our portfolio of tools aimed at improving the user experience and workflow efficiency, enabling clinicians to deliver more accurate and effective interventions for enhanced patient outcomes." Salesforce, Inc. (NYSE: CRM) is expanding its healthcare footprint with the launch of a global Life Sciences Partner Network and broader deployment of its Agentforce digital labor platform. Designed to unify clinical, commercial, and manufacturing data, the initiative enables pharmaceutical and medtech organizations to transition from legacy systems to AI-enabled, compliant workflows. "We are in an unprecedented market moment where, with digital labor grounded in rich data, international life sciences organizations have the opportunity to completely reimagine the way they interact with patients and HCPs," said Frank Defesche, Senior Vice President and General Manager, Salesforce Life Sciences. "Backed by over two decades of industry expertise, Salesforce is uniquely equipped to pioneer this next era with our deeply unified Platform that brings together apps, data, life sciences-specific workflows, and AI – all wrapped in trust and compliance." With integrations from partners like athenahealth, and H1, the platform supports real-time insights, patient engagement, and automated compliance across the healthcare lifecycle. This marks a major step in Salesforce's push to become the digital backbone of modern life sciences operations. Alphabet Inc. (NASDAQ: GOOG, GOOGL), through Google Cloud and Google Public Sector, has partnered with Drive Health and the State of Illinois to launch Healthy Baby, a multi-year AI-powered maternal health pilot targeting underserved communities. The program equips expectant mothers with Google Pixel phones and Fitbit devices, delivering personalized care through Nurse Avery, an AI health assistant. "The Healthy Baby pilot represents a critical step in maternal healthcare, showing how AI can help deliver personalized, proactive health support directly to underserved mothers," said Chris Hein, Field Chief Technology Officer, Google Public Sector. "Using the AI agent, Nurse Avery, and delivering it through Google Pixel phones and Fitbit devices, the program provides real-time support – managing appointments, monitoring vitals, and offering health guidance directly, aiming to make essential resources more readily available." Backed by Google Cloud's secure infrastructure, the initiative aims to reduce maternal mortality, improve birth outcomes, and close care gaps across rural populations. CONTACT: Equity Insider [email protected] (604) 265-2873 DISCLAIMER: Nothing in this publication should be considered as personalized financial advice. We are not licensed under securities laws to address your particular financial situation. No communication by our employees to you should be deemed as personalized financial advice. Please consult a licensed financial advisor before making any investment decision. This is a paid advertisement and is neither an offer nor recommendation to buy or sell any security. We hold no investment licenses and are thus neither licensed nor qualified to provide investment advice. The content in this report or email is not provided to any individual with a view toward their individual circumstances. Equity Insider is a wholly-owned subsidiary of Market IQ Media Group, Inc. ("MIQ"). 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Business Standard
15-05-2025
- Health
- Business Standard
Can AI guide your health questions? OpenAI's HealthBench puts it to test
I am sure you have done it at least once till now… You wake up with a headache or a rash you have never seen before. You Google your symptoms and five clicks later, you are convinced it's something life-threatening, maybe even cancer. What started as a minor worry has turned into full-blown panic. That spiral, fueled by vague search results, medical jargon, and worst-case scenarios, is exactly what makes navigating personal health online so overwhelming. But what if you had an artificial intelligence (AI) tool trained to think like a doctor that can actually explaine what's likely, what's not, and what questions to ask at your next check-up? This is what HealthBench, an open-source benchmark from OpenAI, aims to bring to you. OpenAI is testing how well AI models, like ChatGPT, handle real-world medical scenarios. HealthBench is designed to evaluate if AI can offer reliable, safe, and helpful responses to the kinds of questions people actually ask when they're worried about their health. How does HealthBench work and who built it? Think of HealthBench as a health-focused performance test for AI. It's not an app or a tool that you can download, yet. Instead, it's a benchmarking system. That means it's a way to measure how smart (and safe) AI models really are when it comes to real-world medical questions about things like diagnosis, treatment options, or even understanding symptoms. Announcing the launch on X, OpenAI posted, 'HealthBench is a new evaluation benchmark, developed with input from 250+ physicians from around the world, now available in our GitHub repository.' Evaluations are essential to understanding how models perform in health settings. HealthBench is a new evaluation benchmark, developed with input from 250+ physicians from around the world, now available in our GitHub repository. — OpenAI (@OpenAI) May 12, 2025 'The large dataset, called HealthBench, goes beyond exam-style queries and tests how well artificial intelligence models perform in realistic health scenarios, based on what physician experts say matters most,' the company said in a blog post on Monday. The company stated that the evaluation framework was developed in collaboration with 262 physicians in 26 specialties who have practiced across 60 countries (Full paper available here). 'Improving human health will be one of the defining impacts of Artificial General Intelligence (AGI). If developed and deployed effectively, large language models have the potential to expand access to health information, support clinicians in delivering high-quality care, and help people advocate for their health and that of their communities,' the company wrote in the post. Karan Singhal, who leads OpenAI's health AI team, said in a post on LinkedIn, 'Unlike previous narrow benchmarks, HealthBench enables meaningful open-ended evaluation through 48,562 unique physician-written rubric criteria spanning several health contexts (e.g., emergencies, global health) and behavioral dimensions (e.g., accuracy, instruction following, communication). We built HealthBench over the last year, working with 262 physicians across 26 specialties with practice experience in 60 countries.' What kind of medical problems is HealthBench designed to test? HealthBench gives AI models tough medical cases that real doctors handle in clinics and hospitals every day. These are not simple textbook questions. They're messy, nuanced, and often incomplete, just like real life. The models are scored on how well they understand symptoms, consider different possibilities, suggest correct diagnoses, recommend treatments, and even explain their reasoning. In short, OpenAI is testing whether AI can think like a doctor, not just repeat medical facts. What can HealthBench mean for healthcare users and patients? From confusing lab reports to conflicting opinions on Google, patients often feel lost. HealthBench aims to ensure that AI models, like the ones behind ChatGPT, can safely assist both patients and doctors. If done right, this could lead to tools that: Help patients understand medical info in plain English Support doctors with second opinions or risk assessments Improve diagnosis in remote or resource-poor areas Streamline documentation and decision-making in hospitals How will AI tools like this benefit patients directly? Right now, HealthBench is more of a behind-the-scenes development, but the impact is already visible. For example, newer versions of ChatGPT (like GPT-4-turbo) are getting better at handling medical questions, thanks to testing frameworks like HealthBench. In the near future, we could see: Chatbots that help explain your MRI results AI companions that help you track chronic illnesses Tools to prepare better questions for your doctor's visit Think of it as AI-powered health literacy for everyone. How can HealthBench help doctors in clinical practice? Doctors could eventually use AI tools trained and tested with HealthBench to: Get a second opinion or diagnostic support Save time on clinical documentation Help explain conditions to patients more clearly Stay updated with the latest treatment guidelines HealthBench is also a reminder that AI isn't perfect. It needs to be monitored, cross-checked, and used with caution, just like any other tool in medical science.
