MedLabReport

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Your medical lab results, explained.

How MedLabReport can help you:

Provides a comprehensive report of medical lab results in an easy-to-understand format.
Offers personalized insights based on symptoms, age, and other factors.
Supports a variety of exam result types including blood tests, biopsies, and imaging studies.
Delivers reports quickly, within 5 minutes of file upload.

Why choose MedLabReport: Key features

Utilizes OpenAI's GPT-4, ensuring a high level of analytical capability.
Facilitates better understanding and control over personal health information.
Provides a second opinion that could reveal overlooked aspects of diagnosis.
Guaranteed full refund for unsatisfied customers, no questions asked.

Who should choose MedLabReport:

Individuals seeking to better understand their medical exam results.
Patients looking for a quick second opinion on diagnostic outcomes.
Anyone interested in a comprehensive overview of their health status based on lab tests.

About MedLabReport

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https://medlabreport.com

Release Date

February 2024

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Healthcare Agency Teams Up with Hospitals to Counter False HMPV Claims

HealthPresso uses its flagship AI platform Humeta to help doctors build their practice and hospitals enhance their reputation, CEO Daleep Singh Manhas said. False rumours about Human Metapneumovirus (HMPV) causing a COVID-like pandemic in India are surfacing via articles, blogs, and messages. Misleading claims about potential oxygen shortages and bed availability issues in hospitals have been triggering unnecessary fear and panic among citizens. A similar state of chaos was previously observed during the COVID-19 pandemic when unverified information caused widespread anxiety and uncertainty. To address this issue, HealthPresso, a healthcare marketing agency, has partnered with medical institutes, pharmaceutical organisations, and medical technology (MedTech) companies to create authentic and reliable content to be shared with patients and customers. In an exclusive conversation with AIM, Daleep Singh Manhas, co-founder and CEO of HealthPresso, said, "We help doctors build their practice, hospitals enhance their reputation, health brands create differentiation, and agencies achieve outstanding performance through our unique technology called Humeta." Humeta is HealthPresso's flagship AI platform, designed and built for healthcare communication. Unlike ChatGPT's generalised data model, Humeta employs a retrieval-augmented generation (RAG) approach, sourcing information from over one million credible healthcare databases such as PubMed, WHO, Springer, and the Centers for Disease Control and Prevention (CDC). Notably, Yatharth Hospital and Healthcare at Home have collaborated with HealthPresso to create awareness content on the spread of HPMV. The platform ensures that content follows healthcare communication standards, using templates for press releases, blogs, and white papers. Advanced filters minimise misinformation and prioritise science-backed and factual content while maintaining low hallucination risk. Additionally, built-in regulatory compliance guidelines ensure adherence to medical communication standards, including patient information and disclaimers. Manhas mentioned that they are currently working with AIIMS Delhi, especially with its department of physiology, and taking feedback about the AI platform. While it currently only offers textual content, it aims to offer visual content creation in the future. HealthPresso is a 30-member team that brings together expertise in AI and engineering, healthcare content, business development, marketing, and strategic guidance. The AI and engineering division consists of 10 professionals, five of whom are dedicated to building Humeta, and five others focus on refining the HealthPresso platform. Their efforts are supported by an advisory board of five seasoned healthcare professionals and AI strategists, who provide guidance for the company's strategic decisions. In terms of financial health, the company operates on a subscription and licensing model and caters to both healthcare professionals and businesses. Doctors and healthcare professionals can subscribe to create and distribute personalised patient education content. Meanwhile, healthcare brands, pharma, MedTech, health devices, hospitals, and healthcare associations, can license the AI platform for tailored content solutions. Additionally, API-based integrations enable third-party applications, such as patient engagement platforms and telemedicine apps, to enhance their services with HealthPresso's AI-driven healthcare content. HealthPresso has demonstrated growth potential and secured $100,000 in seed funding from healthcare-focused investors. To further boost its development, the company is seeking $2 million to enhance AI capabilities, expand content partnerships, and scale its market presence. It also plans to enter the US market by June 2025. This funding will validate Humeta's performance with top-tier institutions while driving growth into consumer-centric health education markets in the US. In India, platforms like MyUpchar, Happiest Health, Tata 1mg, Practo, and Lybrate provide general healthcare content. However, none specialise in creating healthcare content using AI-driven RAG models. On the global level, Healthline and WebMD dominate consumer health content but don't offer real-time personalised content. IBM Watson Health previously highlighted the need for specialised AI in healthcare communication but did not incorporate a real-time RAG engine before its discontinuation. Hence, going forward, there is a need to adopt AI in the healthcare industry to help doctors create and share the right information faster.

Analytics India Magazine

Tue, 7 Jan, 12:24 PM UTC

AI models tested for privacy-safe radiology report analysis

Artificial intelligence (AI) and, above all, large language models (LLMs), which also form the basis for ChatGPT, are increasingly in demand in hospitals. However, patient data must always be protected. Researchers at the University Hospital Bonn (UKB) and the University of Bonn have now been able to show that local LLMs can help structure radiological findings in a privacy-safe manner, with all data remaining at the hospital. They compared various LLMs on public reports without data protection and on data-protected reports. Commercial models that require data transfer to external servers showed no advantage over local, data protection-compliant models. The results have now been published in the journal Radiology. Everything has to be in its place. Not only on the operating table or in the office, but also with data. Structured reports, for example, are helpful for doctors as well as for further use in research in databases. Later, such structured data can also be used to train other AI models for image-based diagnosis. In practice, however, reports are usually written in free text form, which complicates further use. This is exactly where the application of AI, more precisely LLMs, comes in. Open and closed models LLMs can be divided into two categories: The closed-weights models are the commercial, well-known AI variants that are also used in chatbots such as Chat-GPT. Open-weights models, such as Meta's Llama models, are an option that can be run on internal clinic servers and can even be trained further. When applying these models, all data remain stored locally, which makes the use of open LLMs advantageous in terms of data security. "The problem with commercial, closed models is that in order to use them, you have to transfer the data to external servers, which are often located outside the EU. This is not recommended for patient data," emphasizes Prof. Julian Luetkens, comm. Director of the Clinic for Diagnostic and Interventional Radiology at the UKB. "But are all LLMs equally suitable for understanding and structuring the medical content of radiological reports? To find out which LLM is suitable for a clinic, we tested various open and closed models," explains Dr. Sebastian Nowak, first and corresponding author of the study and postdoc at the University of Bonn's Clinic for Diagnostic and Interventional Radiology at the UKB. "We were also interested in whether open LLMs can be developed effectively on site in the clinic with just a few already structured reports." Therefore, the research team carried out an analysis of 17 open and four closed LLMs. All of them analyzed thousands of radiology reports in free text form. Public radiology reports in English, without data protection, were used for the analysis as well as data-protected reports from the UKB in German. Training makes the difference The results show that in the case of the reports without data protection, the closed models have no advantage over some of the open LLMs. When applied directly without training, larger, open LLMs were better than smaller, open LLMs. The use of already structured reports as training data for open LLMs led to an effective improvement in the quality of information processing, even with just a few manually prepared reports. The training also reduced the difference in accuracy between large and small LLMs. "In a training session with over 3,500 structured reports, there was no longer any relevant difference between the largest open LLM and a language model that was 1,200 times smaller," says Nowak. "Overall, it can be concluded that open LLMs can keep up with closed ones and have the advantage of being able to be developed locally in a data protection-safe manner." This discovery has the potential to unlock clinical databases for comprehensive epidemiological studies and research into diagnostic AI. "Ultimately, this will benefit the patient, all while strictly observing data protection," explains Nowak. "We want to enable other clinics to use our research directly and have therefore published the code and methods for LLM use and training under an open license."

Medical Xpress

Tue, 14 Jan, 10:19 PM UTC

How 70% of Medical Students and 5% of Research Are Shaped by AI, New Study by Aithor.com

A survey of 3,000 students and an analysis of more than 500 scientific papers from the field of medicine revealed the impact that artificial intelligence currently has on medicine NEW YORK, February 11, 2025 (Newswire.com) - Aithor, the leading provider of artificial intelligence-based solutions, conducted a comprehensive analysis of how medical students have implemented artificial intelligence into their education and work processes. Some use it to find inspiration when writing papers, while others diagnose patients. The research aims to show that artificial intelligence is becoming an integral part of the educational system and it is important to guide students to use technology correctly so that they can be our assistants, but not a substitute. Aithor's research team analyzed more than 1,000 medical students from Tier 1 countries The study involves employees from the USA, Canada, UK, and EU. 79% of respondents are students of medical institutions, while the remaining 21% are employed in the healthcare sector. The gender of the research participants is 37% male, 60% female and 3% selected "Other" as their response. An analysis was also conducted on the use of AI in writing scientific papers. The sources examined included: The Lancet, MDPI, Pubs, and other academic libraries. A total of 500 articles were selected, based on the top five most popular search topics in the following medical fields: Cancer, Mental Health, Epidemiology, Immunology, and Healthy Lifestyle. The research results revealed several significant patterns in the field of medical education Detection of AI-Generated Content: A significant 65% of articles scored between 20% and 40% on AI detection, indicating moderate AI contributions across most medical domains.Oncology leads with 80% of articles demonstrating significant AI involvement, followed by epidemiology (75%) and mental health (68%). AI in Medical Education: 70% of students reported using AI regularly for academic tasks, with 11% of them relying on AI for more than half of their academic work. 33% of students utilize AI for diagnostic assistance, although concerns about AI's reliability in medical diagnosis were raised. Growing Investment in AI: 40% of students pay for AI services, with 9% spending over $100 per month, highlighting the increasing value placed on advanced AI tools in education. Concerns About AI Over-Reliance: Students expressed concerns about over-reliance on AI for academic work, particularly for recalling details from scientific publications and for diagnostic accuracy. Some voiced fears that AI could compromise their professional readiness. The Need for Responsible AI Integration: The study calls for the responsible integration of AI in medical education and practice, with emphasis on AI literacy, reinforcing core diagnostic skills, and developing ethical guidelines for AI use in academic and clinical settings. Complete Study Results: https://aithor.com/research/the-role-of-artificial-intelligence-in-medicine About research authors Aithor is an AI-based platform that provides assistance with articles, paragraphs, and papers. It acts as a writing and paraphrasing tool that offers AI-powered assistance with essays and other works, helping students overcome most common issues faced with paper writing.

Newswire

Tue, 11 Feb, 10:18 PM UTC

ChatGPT Competes With Surgeons in Patient Q&A

With advances in precision medicine at the molecular, genetic, and radiological levels, artificial intelligence (AI) is now shaping patient-doctor interactions. Large language models (LLMs) are neural networks designed to interpret the language context, tone, subtleties, and cultural aspects. These LLMs function as generative AI, capable of producing new text or data similar to the information on which they were trained. Chatbots are among the most widely recognised applications of LLM. These conversational tools provide quick responses to user queries about products, services, and healthcare topics. ChatGPT, a leading example, has become a key player in this domain. German researchers specialising in pancreatic cancer surgery evaluated ChatGPT's ability to answer patient questions about pancreatic cancer surgery, comparing its responses with those of two specialist surgeons. Patients and medical experts assessed the responses for accuracy, clarity for non-specialists, and empathy. Testing Responses A set of 10 frequently asked patient questions was compiled in collaboration with patients from the Heidelberg University Hospital, Heidelberg, Germany. ChatGPT (version 4.0) and two surgeons independently answered these questions. A panel of 24 patients and 25 surgeons evaluated the responses using an anonymised online questionnaire. The evaluation criteria included accuracy, clarity for non-specialists, and empathetic responses. Key Findings Complementary but Limited These findings suggest that ChatGPT can serve as a complementary tool in patient-physician communication, particularly in simplifying complex medical topics for patients. Similar results have been reported in studies on colon cancer and cardiovascular disease prevention. However, AI has some notable limitations. ChatGPT is not specifically designed for medical applications and can produce errors or "hallucinations" -- unexpected inaccuracies that, while surprising at first, are well documented by AI specialists and increasingly recognised by the public. These errors stem from the way that LLMs operate. Unlike humans, AI does not store knowledge but generates responses through mathematical computation. Rather than understanding text, it processes words as fragments called tokens, each of which is assigned a unique mathematical identifier. In some cases, errors arise from the way the data are processed by an LLM. Some models only analyse parts of a dataset, lack the capability to interpret graphs or infographics, or impose restrictions on accessible references. This highlights the need for a careful evaluation of AI-generated medical information. Currently, AI cannot replace the essential human connection in healthcare, particularly in addressing the emotional aspects of illness. Additionally, it is important to clearly indicate when a response is generated by AI to prevent any confusion for patients. Medical liability for AI-related errors remains a significant issue that must be addressed before broader clinical adoption, similar to the ongoing debate on accountability in autonomous vehicle accidents. This story was translated from JIM using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication.

Medscape

Mon, 10 Mar, 2:42 PM UTC

AI-based GPT to offer real-time validated health content to counter misinformation

Healthcare professionals, marketers and institutions spend nearly 40 per cent of their time creating and publishing content, which diverts resources from patient care, research and innovation.Humeta.AI, a specialist health communication GPT would deliver credible, real-time and regulatory-compliant information in a landscape overwhelmed by misinformation, said its developer, an artificial intelligence health content platform. It offers real-time, multi-format, multi-lingual, and validated content thereby transforming healthcare communication, Swadeep Srivastava, founder and managing director, HealthPresso said in a statement. It is powered by a large healthcare knowledge graph, incorporating over 1.5 million research articles and aligning with global health standards set by WHO, FDA, EMA among others, the statement said. Healthcare professionals, marketers and institutions spend nearly 40% of their time creating and publishing content, which diverts resources from patient care, research and innovation, Srivastava said. "With limited tools for real-time, patient-friendly and compliant health information, the need for a robust AI-driven solution has never been greater," he said. Humeta.AI is designed to serve every stakeholder in the healthcare ecosystem, including doctors and healthcare professionals, healthcare brands and pharma marketers, medical institutions and associations, regulatory bodies and policymakers. It is also India's first WhatsApp-based automated health marketing content generator and publisher, enabling health brands, healthcare professionals and marketers to engage physician communities with real-time and credible content daily, Srivastava said. Daleep Singh Manhas, cofounder and CEO, HealthPresso, emphasised the impact of AI on healthcare professionals. "Doctors and healthcare professionals often struggle to balance patient care with content creation and professional outreach. Humeta.AI offers an intuitive, AI-powered solution that significantly reduces the time spent on content generation while maintaining accuracy and compliance. "This is more than a tool. It's a game-changer that allows healthcare professionals to focus on what matters most -- patient care -- while effortlessly staying engaged with their communities," he said.

Economic Times

Fri, 28 Mar, 10:14 AM UTC

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