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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.

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February 2024

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Life X DNA Advances Home Genetic Testing For Personalised Health Insights

At-home genetic testing is revolutionising the way individuals approach their health, offering personalised insights that support long-term well-being. Life X DNA is leading this innovation by providing accessible home DNA health testing in Australia through advanced sequencing and AI-driven analysis. With a focus on accuracy and convenience, Life X DNA empowers individuals to understand their genetic profile and take proactive steps toward better health. Methylation plays a fundamental role in various biological processes, influencing energy production, cognitive function, and detoxification. Certain genetic variations can impact methylation efficiency, potentially increasing the risk of cardiovascular disease, inflammation, and metabolic imbalances. By analysing over 20 million genetic markers and leveraging AI interpretation, Life X DNA delivers detailed health reports that help individuals make informed lifestyle and nutritional choices. For those seeking DNA testing in Perth, Life X DNA offers at-home methylation and MTHFR test kits designed for easy sample collection and in-depth genetic analysis. Unlike traditional lab-based testing, these at-home kits allow users to collect samples using a simple cheek swab and send them for processing without needing a clinic visit. Life X DNA does not operate local testing centres but provides direct-to-consumer kits that ensure accessibility and privacy. Life X DNA employs Next-Gen DNA sequencing combined with AI-powered interpretation to generate highly personalised reports. These reports offer actionable recommendations covering diet, lifestyle modifications, and targeted supplementation based on an individual's unique genetic predispositions. The demand for genetic health insights continues to grow as Australians seek more personalised approaches to wellness. Understanding one's genetic makeup enables individuals to tailor their health strategies, improving energy levels, mental clarity, and overall vitality. Life X DNA's accessible at-home test kits and expert-reviewed reports provide an evidence-based foundation for informed decision-making.

Scoop

Wed, 26 Mar, 2:18 AM UTC

Multimodal generative AI for medical image interpretation - Nature

There is a major opportunity for AI to take a more expansive approach to medical imaging and reporting in the form of GenMI. GenMI models will soon be able to write compelling reports across several specialties, as visualized in Fig. 1, and future models could ingest multiple modalities and clinical data to generate even more accurate, fluent and interpretable reports. Despite the rapid development of GenMI, there is a lack of established guidelines governing their clinical integration. Furthermore, whereas GenMI models can clearly reduce clinician workloads, their role in patient care and education is less intuitive and warrants further discussion. In addition to proposing a new paradigm to leverage these exciting capabilities in a clinical setting, we discuss important challenges that must be overcome to safely realize the benefits of generative AI in this complex domain. Until recently, medical report-generation frameworks have predominantly consisted of a vision encoder and a language decoder. In this framework, the encoder extracts visual information from an image into a vector representation, while the decoder takes a vector and produces a specific output, which is language in this case. For several years, studies have built on the encoder-decoder approach, innovating novel methods to better encode image data, account for external knowledge, screen for abnormalities and more. However, the emergence of foundation models -- large pretrained general-purpose AI systems that include large language models (LLMs) -- has revolutionized medical image interpretation by prompting the development of new GenMI solutions. These GenMI approaches claim to produce more accurate medical reports as well as perform several other downstream tasks and process multimodal data using the same base model. The bulk of these algorithms build upon vision-language models (VLMs), which fuse individual vision and language models together into a unified framework that can jointly encode image and text input. Figure 2 outlines the new applications for medical image interpretation unlocked by multimodal generative AI. The advent of LLMs has significantly affected medical report generation -- algorithms now incorporate GPT variants or other LLMs as their decoder instead of smaller more traditional models. Many studies have made this transition to take advantage of the knowledge and reasoning abilities of LLMs, which make them stronger and more robust decoders for report generation. Nevertheless, this represents a rather confined use of LLMs that does not take advantage of prompting techniques or their unique ability to encode multimodal input when integrated in a VLM framework. Outside of serving as decoders within the encoder-decoder paradigm, LLMs have been leveraged for report quality assurance to rewrite reports and remove hallucinations, which are usually mistaken references to non-existent prior reports. There has recently been a push towards more universal vision-language systems, in which GenMI models have been developed to process multimodal input and perform various medical image-interpretation tasks including report generation and visual question answering. Several such frameworks leverage the ability of LLMs to generate reports and answer questions on different conditions and modalities without additional labelled data. GPT-4V is a popular example from OpenAI that can recognize imaging modalities and anatomical structures in some cases, generate structured reports for pathology images, and modify these reports on the basis of medical prompts. Nevertheless, preliminary findings evaluating the model's out of the box utility for medicine have been poor, and it faces difficulties in accurately diagnosing medical conditions. There are also other GenMI approaches that use smaller language models to achieve similar performance. Although LLMs are promising for future multimodal medical report generation, they currently face several limitations that hinder their clinical utility. LLMs are liable to biases, frequent hallucinations and inaccuracies, which are especially problematic in the context of medical report generation, where remaining unbiased and maintaining clinical accuracy is critical. Clinicians also frequently work with incomplete information, but more complex models experience higher rates of hallucination and misdiagnosis when patient information and history are lacking. Furthermore, the majority of foundation models used for report generation have been developed for English. Except for Chinese, limited research has been conducted for other languages. Many of these issues can be mitigated through prudent data collection and further development of LLMs, and it is important to maintain human partnership when auditing LLMs that are deployed within GenMI frameworks. The ability to incorporate multimodal data is an exciting avenue for more accurate medical report generation; in addition to directly processing an input query alongside an image, multimodal input allows for a stronger encoding of clinical context. Whereas prior approaches for report generation have incorporated multimodal input such as prior medical history, VLMs have opened the door for more effective multimodal processing. For example, one GenMI approach using VLMs can integrate various input modalities by encoding a textual domain identifier token alongside the provided image. Despite the improvements conferred by VLMs, current foundation models struggle in terms of performance when integrating multimodal image input compared to clinicians. Models adapted for multimodal tasks such as Google's Gemini and GPT-4V are especially promising in this regard and perform better on benchmarks designed for multimodal and higher-level reasoning in areas including health and medicine. The basis of improved report generation from multimodal VLMs is their ability to learn tasks using a unified text and image representational space. This type of representation learning typically implements a mechanism called cross-attention, in which the model learns a matrix of 'importance scores' for different aspects of both the image and text sequence when producing these shared representations. Contrastive learning for visual and textual encoders, in which machine learning models are trained to differentiate between similar and dissimilar data by comparing groups or pairs, is key to this learning of the shared representation space and has also led to improvements in medical report generation. Although there have been many studies incorporating multimodal inputs, there are far fewer that have explored multimodal outputs, where the generated report is accompanied by some form of non-textual output to improve interpretability or diversify its clinical utility. For example, one approach is able to output bounding boxes on the input image, which significantly improves its explainability. Another study constructed a unified model that can generate text reports as well as chest X-rays from a given input. Outside of these studies, the usage of multimodal outputs in report generation is relatively unexplored. Despite their capabilities, GenMI approaches still have room for improvement in terms of clinical accuracy before active deployment. We therefore outline two methods that have been successful in enhancing clinical accuracy for prior report-generation solutions: the incorporation of external knowledge and explicit disease classification. Future GenMI models could take inspiration from these methods as part of their training and inference pipeline. One study has already incorporated retrieval augmented generation -- a technique that enables language models to retrieve domain-specific external knowledge during generation -- within such a paradigm. Several approaches have attempted to incorporate external clinical knowledge at different stages of report generation. First, on a smaller scale, memory matrices can help consolidate knowledge by storing cross-modal mapping information between the vision and language components to better align future generated reports with the provided images. Other studies incorporate knowledge on clinical findings and diseases at a broader level. The most common implementation is through knowledge graphs, which are a type of computational graph that consist of nodes and edges. Here nodes represent disease states or pathophysiological features and edges capture the relations between them. Information from these graphs is typically encoded with the input using some form of graph neural network, which is a deep learning architecture that is designed to process computational graphs. The popular knowledge graph RadGraph contains more than six million entities and four million relations extracted from the MIMIC-CXR chest X-ray dataset and has been used frequently in report generation. In addition to clinical findings, certain studies have attempted to encode contextual knowledge relevant to particular patients based on similar reports. Although integrating external knowledge in this manner can improve clinical accuracy, the degree to which it influences the model can affect performance. Furthermore, there appears to be a trade-off between clinical accuracy and natural language similarity, which measures factors including fluency, word overlap, stem similarity and number of related words between the generated text and ground truth report. Nevertheless, when external knowledge is implemented well, it is possible to achieve both high clinical accuracy and language similarity. In addition to external knowledge, explicitly performing disease classification has also been linked with improved clinical accuracy -- some papers use multi-label classification as part of their training process to encourage the model to pay more attention to clinically relevant features. Further methods more directly utilize the outputs of disease classification in generating medical reports. For example, some use the outputs to augment the initial visual features sent to the decoder when generating the radiology report, whereas others perform multi-label classifications in varying anatomical regions to determine contents and focus areas of the report. The disease tag outputs have even been used as starting points for writing reports, where descriptors written based on individual disease tags are combined to form the final report. Given the increasing prevalence of GenMI in radiological image processing, similar approaches have been introduced in other specialties, including pathology and dermatology. One all-in-one toolkit for pathology image analysis can process multimodal inputs and generate multimodal outputs by incorporating a foundation model that interacts with several specialized models. In addition to medical report generation, it can perform tasks including outputting bounding boxes, segmentation and answering questions. Another GenMI approach for dermatology can provide interactive assistance and generate medical reports on the basis of images taken by the user. More broadly, large multimodal models provide a reasonable basis for medical inference across specialties, but are still limited in their depth and current accuracy. Other image-interpretation models have been developed for these fields that do not incorporate formal medical report generation as a downstream task.

Nature

Wed, 26 Mar, 6:24 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

Are AI-chatbots suitable for hospitals? Diagnostic capabilities of large language models tested

Large language models may pass medical exams with flying colors but using them for diagnoses would currently be grossly negligent. Medical chatbots make hasty diagnoses, do not adhere to guidelines, and would put patients' lives at risk. This is the conclusion reached by a team from TUM. For the first time, they investigated systematically whether this form of artificial intelligence (AI) would be suitable for everyday clinical practice. Despite the current shortcomings, the researchers see potential in the technology. They have published a method that can be used to test the reliability of future medical chatbots. Large language models are computer programs trained with massive amounts of text. Specially trained variants of the technology behind ChatGPT now even solve final exams from medical studies almost flawlessly. But would such an AI be able to take over the tasks of doctors in an emergency room? Could it order the appropriate tests, make the right diagnosis, and create a treatment plan based on the patient's symptoms? An interdisciplinary team led by Daniel Rückert, Professor of Artificial Intelligence in Healthcare and Medicine at TUM, addressed this question in an article published in the journal Nature Medicine. For the first time, doctors and AI experts systematically investigated how successful different variants of the open-source large language model Llama 2 are in making diagnoses To test the capabilities of these complex algorithms, the researchers used anonymized patient data from a clinic in the U.S. They selected 2,400 cases from a larger data set. All patients had come to the emergency room with abdominal pain. Each case description ended with one of four diagnoses and a treatment plan. All the data recorded for the diagnosis was available for the cases -- from the medical history and blood values to the imaging data. "We prepared the data in such a way that the algorithms were able to simulate the real procedures and decision-making processes in the hospital," explains Friederike Jungmann, assistant physician in the radiology department at TUM's Klinikum rechts der Isar and lead author of the study together with computer scientist Paul Hager. "The program only had the information that the real doctors had. For example, it had to decide for itself whether to order a blood count and then use this information to make the next decision -- until it finally created a diagnosis and a treatment plan." The team found that none of the large language models consistently requested all the necessary examinations. In fact, the programs' diagnoses became less accurate the more information they had about the case. They often did not follow treatment guidelines, sometimes ordering examinations that would have had serious health consequences for real patients. In the second part of the study, the researchers compared AI diagnoses for a subset of the data with diagnoses from four doctors. While the latter were correct in 89% of the diagnoses, the best large language model achieved just 73%. Each model recognized some diseases better than others. In one extreme case, a model correctly diagnosed gallbladder inflammation in only 13% of cases. Another problem that disqualifies the programs for everyday use is a lack of robustness: The diagnosis made by a large language model depended, among other things, on the order in which it received the information. Linguistic subtleties also influenced the result -- for example, whether the program was asked for a main diagnosis, a primary diagnosis, or a final diagnosis. In everyday clinical practice, these terms are usually interchangeable. The team explicitly did not test the commercial large language models from OpenAI (ChatGPT) and Google for two main reasons. First, the provider of the hospital data has prohibited the data from being processed with these models for data protection reasons. Second, experts strongly advise that only open-source software should be used for applications in the health care sector. "Only with open-source models do hospitals have sufficient control and knowledge to ensure patient safety. When we test models, it is essential to know what data was used to train them. Otherwise, we might test them with the exact same questions and answers they were trained on. Companies of course keep their training data very secret, making fair evaluations hard," says Paul Hager. "Furthermore, basing key medical infrastructure on external services which update and change models as they wish is dangerous. In the worst-case scenario, a service on which hundreds of clinics depend could be shut down because it is not profitable." Developments in this technology are advancing rapidly. "It is quite possible that in the foreseeable future a large language model will be better suited to arriving at a diagnosis from medical history and test results," says Prof. Daniel Rückert. "We have therefore released our test environment for all research groups that want to test large language models in a clinical context." Rückert sees potential in the technology: "In the future, large language models could become important tools for doctors, for example for discussing a case. However, we must always be aware of the limitations and peculiarities of this technology and consider these when creating applications," says the medical AI expert.

Medical Xpress - Medical and Health News

Mon, 22 Jul, 4:04 PM UTC

A chatbot based question and answer system for the auxiliary diagnosis of chronic diseases based on large language model - Scientific Reports

In recent years, artificial intelligence has made remarkable strides, improving various aspects of our daily lives. One notable application is in intelligent chatbots that use deep learning models. These systems have shown tremendous promise in the medical sector, enhancing healthcare quality, treatment efficiency, and cost-effectiveness. However, their role in aiding disease diagnosis, particularly chronic conditions, remains underexplored. Addressing this issue, this study employs large language models from the GPT series, in conjunction with deep learning techniques, to design and develop a diagnostic system targeted at chronic diseases. Specifically, performed transfer learning and fine-tuning on the GPT-2 model, enabling it to assist in accurately diagnosing 24 common chronic diseases. To provide a user-friendly interface and seamless interactive experience, we further developed a dialog-based interface, naming it Chat Ella. This system can make precise predictions for chronic diseases based on the symptoms described by users. Experimental results indicate that our model achieved an accuracy rate of 97.50% on the validation set, and an area under the curve (AUC) value reaching 99.91%. Moreover, conducted user satisfaction tests, which revealed that 68.7% of participants approved of Chat Ella, while 45.3% of participants found the system made daily medical consultations more convenient. It can rapidly and accurately assess a patient's condition based on the symptoms described and provide timely feedback, making it of significant value in the design of medical auxiliary products for household use. Chronic diseases are increasingly receiving attention on a global scale. According to statistics, nearly 25% of adults suffer from one or more chronic conditions, making chronic diseases a major challenge facing global health. Not only do chronic diseases result in poor health, disability, and even death, but they also account for a substantial portion of healthcare expenditures. Therefore, the early diagnosis of chronic diseases becomes critically important. Prompt intervention is required to prevent further progression of the disease. With changes in lifestyle and the impact of the pandemic, the incidence of chronic diseases is on the rise. Consequently, the future demand for health management as well as expenditures on healthcare systems are also gradually increasing. At the same time, the demand for healthcare resources such as hospitals and doctors has also significantly increased. Patients need to spend more time waiting for medical treatment, and doctors are facing immense workloads and patient volumes. However, to reduce both time and financial costs, an increasing number of people are leaning towards contactless consultations and seeking medical advice online. In a home setting, when experiencing physical discomfort, people are more inclined to conduct online searches for medical advice. Indeed, the complexity and uncertain reliability of online information present a potential problem. Therefore, there is an urgent need for remote medical interventions in the management of chronic diseases. Monitoring provided by remote healthcare service organizations can help in the early identification of symptoms and enable rapid responses to disease exacerbation. Developing a system that can accurately assist in the diagnosis of chronic diseases becomes especially important. Telemedicine is a popular aspect of electronic medical applications and has seen increased usage with the development of mobile healthcare technologies. This growth has established the significance and value of remote interventions in the management of health services. These technological solutions have the potential to make patients' lives easier. For example, within clinical decision support systems (CDSS), models such as naive Bayes, neural networks, and K-nearest neighbors (KNN) are utilized to diagnose COVID-19. The VP-expert diagnostic system is specifically designed for populations in underdeveloped regions, aiming to rapidly diagnose diabetes and enhance the diagnostic efficiency of physicians. The recommendation system for iron deficiency anemia, based on clinical oncology, serves as a CDSS, used for diagnosing and managing its treatment. Furthermore, the smart tracking system for acute respiratory infections utilizes mobile health technologies to infer new facts from medical data collected during examinations of pediatric patients. The use of CDSS to assist in enhancing the diagnostic efficiency of healthcare providers has become an established fact. Currently, as the achievements of artificial intelligence in various domains have been effectively validated, deep learning has also exhibited exceptional performance in a variety of CDSS. The concept of deep learning was introduced in 2006. The primary characteristic of deep learning is unsupervised feature learning, which provides a novel solution for dealing with abstract concepts in human thought. Deep learning algorithms have been effectively applied in numerous domains, including image retrieval and web-based knowledge retrieval. For example, expert diagnostic systems for the automatic identification of asthma and chronic obstructive pulmonary disease (COPD) based on deep learning can interpret respiratory sounds recorded by a stethoscope, assisting doctors with remote access. Such systems can provide more timely and accurate diagnoses, offering a quicker and more efficient approach, especially for patients in resource-poor areas or those who cannot receive immediate medical attention. Additionally, multi-modal learning using foundational architectures like long short-term memory can also enable early predictions of exacerbations in COPD. By analyzing a range of modal data, including clinical data and radiographic information, these models capture potential disease features and trends. This helps doctors take timely intervention measures and reduce the risk of disease exacerbation. Deep learning has also shown promise in predicting hospital mortality rates. Through model training and optimization, the risk of patient mortality during hospital stays can be more accurately predicted. This is crucial for the rational allocation of hospital resources, prioritized patient care, and treatment decision-making. As previously described, research has demonstrated the potential achievements of artificial intelligence in the realm of healthcare services. Among them, conversational agents (CAs), also known as AI chatbots, are applications capable of communicating via natural language. And with the rise of large language models, open AI's generative pre-trained transformer (GPT) has gained prominence. Trained on extensive text datasets, these models can generate natural language and be fine-tuned for a variety of language tasks. For example, tasks such as language translation, text summarization, and text completion can be undertaken. After being trained on extensive conversational text datasets, these models can understand the context and intent of a conversation. They can be utilized as virtual assistants and other conversational interfaces that generate human-like responses in dialogue scenarios. While initially mainly used for social chatting, these models are now increasingly being employed in specialized industries. Research in the field of intelligent diagnostics has demonstrated the feasibility of using large language models for radiological decision support in clinical settings, establishing them as effective tools for improving clinical workflows. The isolation measures during the COVID-19 pandemic have heightened people's awareness and understanding of online medical treatment. Doctors and patients have resorted to online consultations when face-to-face interactions are not possible. The number of remote consultations increased by 50%, further highlighting the importance of telemedicine. GPT, as a natural language processing model, serves various roles in healthcare. For instance, it can generate radiology reports to save radiologists time or assist in diagnostic decisions by providing differential and refined information. It can even communicate with patients, offering information regarding test results and follow-up treatment recommendations. Additionally, some research has noted that digital therapy services provided by chatbots for mental health counseling have also gained recognition for their effectiveness. Therefore, the future of healthcare will largely depend on the ability to accurately perform remote diagnoses. The collection of data remotely and its analysis through artificial intelligence will contribute to improved healthcare services and health outcomes. Regrettably, there is currently a relative paucity of research focusing on chronic disease auxiliary diagnosis based on large language models. Seizing this opportunity, our research aims to develop a system for aiding in the diagnosis of chronic diseases by leveraging large language models. This aims to further enhance the quality of remote medical consultation services. This study is significant for the application of language models in healthcare and the development of systems that assist in the diagnosis of chronic diseases. In the current technological context, this study comprehensively leverages the GPT-2 deep learning model. Through meticulous training, optimization, and packaging processes, we have successfully designed and developed an intelligent system for the auxiliary diagnosis of chronic diseases -- Chat Ella. This system is capable of engaging in dialogues with patients, deeply inquiring into related symptoms, and thereby providing preliminary diagnostic results. Such an approach enables patients to gain an initial understanding of their health conditions and consult professional doctors in a timely manner when necessary. After a series of usability tests and evaluations on a validation set, including metrics such as accuracy and AUC, the system's performance has met our anticipated standards. The chatbot-based medical auxiliary diagnosis model holds promise for broader promotion and application in the future. The innovative aspects of this study are primarily reflected in the following areas:

Nature

Thu, 25 Jul, 6:02 AM UTC

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