MDClone

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MDClone is a revolutionary tool in the field of healthcare, harnessing the power of AI to unlock the potential of in-house organizational data. With real-world data instantly accessible, institutions can understand and improve patient outcomes with unprecedented speed and accuracy.

How MDClone can help you :

MDClone enables healthcare organizations to tap into their existing data, generating insights that can enhance operational efficiency, streamline patient care, and ultimately improve health outcomes.
By removing the barriers to data utilization, the tool empowers organizations to become data-driven in their pursuit of improved healthcare.

Why choose MDClone: key features :

MDClone features an innovative Synthetic Data Engine that creates a replica of real-world data without any risk to patient privacy.
Additionally, its rich data ecosystem supports seamless integration of data from numerous sources, promoting evidence-based decision making across the healthcare organization.

Who should choose MDClone :

MDClone is an ideal choice for healthcare organizations wishing to leverage their existing data to improve patient outcomes.
Whether it's a hospital, research institute, or a health-related government entity, MDClone provides valuable insights that drive organization-wide improvements.

About MDClone

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https://www.mdclone.com

Release Date

February 2024

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Freemium

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Revolutionizing Healthcare: Al-Driven Solutions

In the next installment of our Medscape Masters event series, we bring together a unique group of experts to discuss how artificial intelligence (AI) is changing the landscape of medicine, moderated by John Whyte, MD, WebMD's chief medical officer. Joining our panel is David Fajgenbaum, MD, a physician, scientist, author, and associate professor of medicine at the University of Pennsylvania's Perelman School of Medicine; Elaine Mardis, PhD, co-executive director of the Institute for Genomic Medicine at Nationwide Children's Hospital; and Leo Celi, MD, a principal research scientist at MIT and an associate professor at Harvard Medical School. Fajgenbaum is pioneering the use of AI to help repurpose drugs designed to treat one disease and expanding their potential applications to treat a wide range of other diseases. Using AI, Fajgenbaum and his team can analyze thousands of drugs and assess their potential to treat over 18,000 human diseases -- an endeavor that would be impossible for any human team to complete. Remarkably, AI can achieve this in just 24 hours. Fajgenbaum, who was diagnosed with Castleman disease -- a rare disorder causing noncancerous tumors in lymph nodes -- during medical school, applied AI to identify a new treatment option. Through this AI model, he found that a tumor necrosis factor (TNF) inhibitor that had never before been used to treat Castleman's was the key to saving one patient's life when all other treatments failed. It's still crucial for human beings to be involved in these analyses, said Fajgenbaum, especially when it comes to assessing for drug-drug interactions. Mardis feels that AI algorithms devoted to cancer diagnosis and treatment are driving what she calls "precision medicine." This works really well when AI is involved in evaluating the genomics of cancer patients coming out of clinical trials. "What are the other factors in that cancer genome that are helping to predict response, durability of response, and, importantly, the potential for combination therapies?" asked Mardis. According to Mardis, when adult patients are diagnosed with cancer by their doctor, in many cases they should also get a second opinion that is driven by AI models -- even more so for patients diagnosed with a very prevalent and frequently diagnosed cancer type. Celi highlights that the results of a clinical trial conducted in a single population cannot be assumed to apply universally. However, AI models enable the continuous development of trials tailored to specific populations, overcoming this limitation. "It's very expensive to repeat a clinical trial that would engage all types of patients across all types of diseases," said Celi. "It's just economically and logistically impossible." But with the growing number of electronic health records now being amassed, he said, we can use that information for clinical trials going forward. The issue that remains, though, is how to avoid AI-driven biases that might skew the data to reflect only a certain group. The key to overcoming this is transparency -- understanding how the data were collected, who they were collected from, and what equipment was used to collect them. "The clinicians have to be at the front and center," Celi said. "We will only have a successful deployment of AI if we are taking the reins of AI and taking them away from the tech giants." This year's Health Heroes are using AI to find cures, prevent deadly illnesses, and flag cancers before they start -- and for them, it's personal.

Medscape

Wed, 2 Oct, 10:01 PM UTC

GenHealth.ai Unveils Large Medical Model, Surpassing Industry Benchmarks by Over 14% in Healthcare Cost and Risk Prediction

BOSTON, September 24, 2024 (Newswire.com) - In a significant breakthrough in healthcare AI, GenHealth.ai today published a study detailing an advanced generative AI model that decisively outperforms established industry benchmarks by over 14% on healthcare cost and risk prediction. This model challenges traditional approaches by leading firms like Milliman, Cotiviti, and Johns Hopkins, setting a new standard for precision in healthcare prediction while enabling an unprecedented lens into the future of a patient's healthcare journey. The paper, "Introducing the Large Medical Model: State of the art healthcare cost and clinical risk prediction" articulates how GenHealth.ai has leveraged a healthcare-native approach to generative AI to achieve superior results. The Large Medical Model (LMM) combines a unique vocabulary tailored specifically for healthcare, the technology of neural network transformers (which underpins Large Language Models), and data from trillions of healthcare events across 140 million patients. The resulting model achieves a similar leap in performance for the healthcare domain akin to the improvement in LLMs over traditional natural language processing approaches. "We've created the Large Medical Model to address a broad range of healthcare applications where LLMs and traditional analytics fall short. Our healthcare specific AI is built from the ground up to support everything from pop health analytics, to automating prior authorizations, and detecting fraud/waste/abuse." said Ricky Sahu, CEO of GenHealth.ai. "This isn't just about streamlining administrative tasks - though our model excels there. This is the first spark towards truly personalized patient care. It's a shift in how healthcare organizations typically use data to find general patterns, to a model where, in seconds, each patient and encounter can have better than human insight that is actionable. It will be equivalent to the impact large language models had on text interaction." Highlights of the study include: "Our team brings years of hands-on experience with healthcare claims and clinical data. This deep knowledge has given us unique insights that many might miss, but are actually crucial for achieving such high performance and versatility in our AI model. We've been able to fine-tune it for a wide range of applications because we're used to dealing with a lot of the nuance and complexity that's present in so much of healthcare data." added Eric Marriott CTO of GenHealth.ai The GenHealth.ai team is now building applications on top of the Large Medical Model. Similar to how ChatGPT was the first application that democratized access to large language models, GenHealth has introduced applications on top of its medical model including a co-pilot for population health analytics and prior authorization automation software to make the LMM more accessible. "I'm so excited about what we're building and how it can help make life easier for both providers and health plans," commented Ethan Siegel, COO at GenHealth.ai. "We can use our AI systems to help reduce the amount of time people have to spend on painful, manual processes so they can spend more time making sure patients get the right treatment at the right time." For more information, visit GenHealth.ai's website for solutions and generative AI for healthcare. To access the full research paper, please visit GenHealth's website.

Newswire

Tue, 24 Sept, 2:14 PM UTC

Healthtechs face many data challenges with AI - here's how to resolve them

Jon Payne, Director of Sales Engineering and Education at InterSystems discusses the use of AI in wearables, monitoring and imaging. Expand InterSystems Jon Payne, Director of Sales Engineering and Education at InterSystems The use of artificial intelligence (AI) in wearables, monitoring, and imaging is forecast to save hundreds of thousands of lives, and billions of pounds by helping individuals make healthy lifestyle decisions and physicians to detect disease early. A 2023 study by Accenture in the US estimates generative AI could automate 28% of work time in healthcare and augment another 11%. An earlier study by Deloitte estimated the combined effect of eight AI applications could save 400,000 lives annually across Europe, deliver 200 billion EUR in annual savings, and free up 1.8 billion hours of healthcare professionals' time. Yet AI poses specific challenges for the HealthTech industry and on its own it cannot fuel success. Companies seeking to incorporate AI into their product portfolio will only realise its potential if they surmount the hurdles related to data acquisition, interoperability, data cleansing, and privacy. Overcoming the constraints of the training model This is necessary because AI is only as good as the data used for its modelling. AI in medical applications must also go beyond the bounds of its training model to aggregate data from external sources of clinical and non-clinical data. That could be as many as ten different EHR environments, along with next-generation sequencing labs, patient questionnaires, and other sources. This data has to be cleansed, labelled, and rendered interoperable through conformity with a standard such as HL7 FHIR, so senders and receivers understand the information in the same way. The data must also be protected in line with all regulations and privacy laws that apply in any territory. While an AI application can sift through masses of research or clinical data it remains constrained by its model and will not have the full context. This means, for example, that AI can only assess a patient for risk of stroke based on entered patient data. An example from imaging diagnostics Take the increase in diagnostic imaging, for example. A study in the Journal of Digital Imaging suggested nearly 60% of radiology orders had no mention of important chronic conditions despite the increase in their prevalence. There is an obvious need here to include contextual medical record information within diagnostic imaging workflows. Imaging AI and ML applications can ease workloads and cognitive burdens on radiologists by analysing curated clinical data. However, to do this they need to bridge siloed imaging data and disparate picture archiving and communication systems (PACS) that use proprietary technology. Achieving this requires knowledge of healthcare data and systems, and interoperability with the approaches of companies such as Epic, GE Healthcare, 3M Health Care, INFINITT, Guerbet, Ricoh, Canon Medical, and Roche Diagnostics. Compliance with the full range of healthcare protocols and standards is necessary to facilitate information flows across the imaging data silos and sources. Once achieved, this puts the right information in front of radiologists at the right moment and eliminates the need for complex searches in the electronic patient record to retrieve specific patient data. Clinicians have an accurate presentation of current diagnoses, doctor's notes, wearables data, and even genomic information prior to reading a study. The multiple challenges of making data useable in AI Multi-source datasets of the type we are discussing here depend on interoperability and compliance with multiple data standards within the healthcare sector. An AI application may be able to exploit the full potential of HL7 FHIR but may also need to work with legacy standards such as HL7 V2 and non-standard, or even non-clinical data sources. Relying on a single standard is unlikely to gain a new application or device widespread adoption because in the real-world, older, long-standing systems are always in use, generating a flat file or other non-standard format. The requirement for comprehensive, aggregated data means that HealthTechs face real challenges in training on very disparate types of data for whatever purpose. Data pulled in from a wide range of sources, including devices or systems with legacy messaging standards, previously had to be cleaned and checked for errors by data scientists, because it is rarely in structured tables. This preprocessing and labelling transforms data into a format that is suitable for use in AI applications. The challenges of data preparation for AI applications are often underestimated, but the single platform approach is how they are best overcome. Robust machine learning algorithms commonly used in AI, such as neural networks, can automate much of this. They can take care of some of the necessary preprocessing and cleaning through interpreting patterns in the training data. This is especially helpful when the data includes natural language text or other datatypes that are challenging to deal with programmatically. AI applications need to be built on a single data platform The full range of onerous preprocessing requirements, however, can only be fully resolved through a single platform approach that normalises all sources of data and provides the connective tissue with other devices and systems. It is an approach designed for the challenges of the HealthTech industry, providing a patient-centric model that is ready for analysis. A platform can include numerous, trusted pipelines that aggregate data from across sources' formats, complemented with AI-based techniques.It will take care of labelling, which is critical for training supervised machine learning models. A platform should also track data lineage, allowing developers to use subsets to train predictive models, keeping the link back to the full dataset to ensure context is retained. With encryption at rest and in flight, this approach is what HealthTech companies need for all-round success in the health sector when they incorporate AI into their solution. New applications benefit immediately from easier and more streamlined deployments, with the ability to develop the solution rapidly to achieve the genuine scalability on which commercial success depends. The challenges of data preparation for AI applications are often underestimated, but the single platform approach is how they are best overcome.

Med-Technews

Thu, 3 Oct, 4:59 PM UTC

AI in Healthcare: 5 Use Cases and 1 challenge | Ubuntu

The accelerated developments in machine learning and artificial intelligence in healthcare have set the stage for some interesting transformations. By enabling better care for patients, optimizing processes, and generating new opportunities for medical research and treatment, these technologies are indeed going to change the healthcare industry radically. According to a study conducted by the Berkeley Research Group, it's estimated that AI will be able to save the US healthcare system approximately $360 billion annually as well as boost diagnostic accuracy by an average of 15 to 20%. In this post, I will give you insights into how AI and healthcare are working hand in hand, together with real-world examples. We will also examine the main barrier to AI adoption in healthcare - security and compliance. Let's start by exploring predictive analytics. Predictive analytics leverages AI to analyze historical and real-time data to forecast future events. In healthcare, this means predicting patient outcomes, potential complications, and possible disease outbreaks. Another area that AI has had an impact on is medical imaging and diagnostic capabilities. AI-powered medical imaging and diagnostics are enhancing the speed and accuracy of our disease detection abilities. Advanced image recognition algorithms are able to analyze medical images such as X-rays, MRIs, and CT scans to detect abnormalities with high precision. The third point I'd like to touch on is in the realm of proactive medical approaches; personalized medicine. Personalized medicine tailors medical treatment to the individual characteristics of each patient. AI enables the analysis of large amounts of data, which includes genetic information, to create personalized treatment plans. Drug discovery is an important aspect of healthcare. It is essentially the process of identifying and developing new medications in order to treat different diseases and conditions. It often involves a series of different stages, which include the initial discovery of possible drug candidates, preclinical testing, and clinical trials. This process is to ensure that the drug is both safe and efficient for patients. This traditional drug discovery process is lengthy and expensive. Instead, AI can accelerate the process by analyzing large datasets to identify potential drug candidates and predict their efficacy at a quicker pace. AI-powered virtual assistants and chatbots are finding a place in healthcare, especially in the fast and convenient access to information and support for patients. For example, Cleveland Clinic has applied AI in supporting jobs, scheduling, handling formerly laborious tasks, and making operations more efficient to be able to concentrate on direct patient care. To enhance the large language models (LLMs) used in their virtual health assistants, organizations can take advantage of a database like Charmed OpenSearch that supports Retrieval Augmented Generation (RAG). This integration allows the chatbots to deliver real-time information, within context by gathering pertinent data from an extensive knowledge base. Using vector databases along with OpenSearch's k-nearest neighbors (KNNs) functions empowers healthcare organizations to develop chatbots that increase patient interaction quality and operational effectiveness. While AI in healthcare holds a lot of power and benefit, security and compliance remain some of the most critical concerns, and the most significant barrier to AI adoption. ML models require large sets of patient data, which ultimately increases the risk of privacy breaches or data exploitation. In the BRG report, it states that both data privacy and security are top priorities for healthcare organizations. This is unsurprising given the wealth of sensitive data these organizations handle - such as EHRs, genomic data, and much more - as well as the extensive regulation that the industry is subject to. AI solutions in healthcare also often use large amounts of open source packages, which means the possible introduction of vulnerabilities. This is further heightened by the complexities of maintaining security throughout the entire ML lifecycle, starting from data ingestion all the way to model deployment. Lastly, ML holds certain risks such as data poisoning that could potentially undermine the integrity of the AI models. This can result in the manipulation of either the data, or the model, to generate incorrect outcomes - which can be a big issue in healthcare. Organizations need to apply systems that are capable of monitoring and detecting anomalies as well as those capable of retraining pipelines. All of these challenges can make it difficult to safely implement AI and take advantage of the innovations and use cases outlined above. But you can mitigate these challenges with the right technologies. At Canonical, we've designed our end-to-end enterprise AI solution to maximize security at every layer of the stack. Canonical's Ubuntu Pro offers vulnerability fixes for over 30,000 open source packages, including critical ML tools and frameworks like Python, MLflow, TensorFlow and PyTorch. This helps minimize the risk and complexity of utilizing these open source tools. As mentioned before, data privacy is of utmost concern, particularly as models process and infer highly sensitive data. Confidential AI on Ubuntu uses confidential computing to protect data during inference. This can make it possible to use sensitive data while remaining compliant with industry regulations. Last but not least, Charmed Kubeflow can integrate security features like authentication and network isolation into AI workflows. By ensuring that there is end to end protection across the whole ML lifecycle, you can protect the integrity of your models and minimize the risk of data poisoning. The AI revolution in healthcare is making a significant difference in patient care, operational efficiency, and the cycle of medical innovations. As AI/ML advances, it allows for more innovation and better health outcomes by both bending the cost curve and pushing medical research boundaries further. Canonical's AI stack offers a path to securely maximizing the impact of AI on the healthcare industry from the base OS, all the way to the application layer.

Ubuntu

Fri, 27 Sept, 10:00 AM UTC

Insights: How AI is redefining diagnostics and surgery

Image: Supplied The advent of artificial intelligence (AI) ushered in a new era in healthcare - one marked by a degree of precision and efficiency that we had never deemed possible. Equipped with the ability to analyse vast amounts of data in a matter of seconds, AI is reshaping diagnostics, treatment planning, and patient care with unprecedented accuracy and speed. From predicting illnesses before symptoms arise to enabling life-saving surgeries guided by robotic precision, AI is turning science fiction into reality. This transformative force is not just enhancing healthcare - it's redefining it, offering the promise of a future where medicine is more personalised, proactive, and powerful than ever before. When it comes to emerging trends and the future of AI in healthcare, it's essential to view developments through both short-term and long-term perspectives. In the near term, AI is poised to address pressing challenges like data overload and streamlining operational efficiencies. However, the long-term vision for AI in healthcare goes beyond immediate improvements, with the potential to revolutionise personalised medicine and predictive care, and even reshape how we approach disease prevention and treatment. Leveraging the healthcare data surge The immediate challenge in my opinion is centered around the current data surge. Over the past decade, the amount of health data available has surged dramatically. Surgery produces a large volume of data with each procedure, with video recordings contributing the largest data sets. This video data is highly valuable for surgical research, assessing clinical outcomes, ensuring quality control, and enhancing education. What's alarming is that 80 per cent of this data is unstructured and around 97 per cent goes unused - we are sitting on critical insights that could potentially elevate the quality of surgical performance. The operating room (OR) is filled with various medical technologies generating data, but these are often isolated point solutions from different companies. While it's possible to extract this data if the device manufacturer allows it, and the hospital has the right software, most of the time the data remains locked within the individual devices. Hospitals lack a unified platform where they can access all the tools and data healthcare providers use or need. Moreover, there is currently no technology that effectively integrates these disparate data sets to deliver valuable insights that could benefit both healthcare providers and patients. At J&J MedTech, for instance, we have recognised this challenge and have developed a digital ecosystem designed to enhance surgical connectivity, encompassing software applications that are independent of data sources and aimed at providing critical insights precisely when and where they are needed. As we move forward, really is, the question we are constantly asking ourselves is how do we continue to bring forward and expand on the value of surgical data? Scalable AI healthcare models Scaling AI in healthcare is particularly challenging due to complex applications, strict regulations, and critical life-and-death decisions. While many organisations are ready to invest, healthcare often lacks the skilled staff, technical expertise, and resources needed, with readiness far below the 54 per cent average across other industries combined. With AI forecasted to continue its upward swing well beyond 2024, it is essential to tackle several technical obstacles to the adoption of clinical AI. There has never been a greater onus on providing intelligent insights to customers through digital solutions. For instance, while our current infrastructure supports rapid cloud-based deployment of AI models, it lacks the same scalability for monitoring and maintenance. To address this, we're developing a system to track key metrics and detect issues before they affect customers or billing. Additionally, we're scaling AI algorithms in operating rooms to offer surgeons real-time analysis, and we're fostering a community for surgeon collaboration and ongoing education. Despite, and possibly because of these challenges, some of the most innovative and successful methods for deploying AI into production today are emerging from the healthcare sector. AI-powered robotics in surgery Surgical robots represent a significant leap forward, blending advanced technologies like AI with sophisticated mechanics. Robotic arms enable surgeons to perform operations through smaller incisions than traditional open or laparoscopic surgeries. These robots enhance surgical skills by offering greater control, flexibility, and access to complex anatomical areas. Equipped with specialised instruments and controlled via a console, they translate the surgeon's hand movements into precise, smooth actions, reducing tremors and improving accuracy. These systems tackle ongoing challenges in surgery and hold even greater potential for future advancements. The future of modern clinical trials AI is increasingly seen as a key to sustainable and efficient drug development, with its applications in clinical trials (CTs) being actively explored. While having access to data is crucial for personalised medicine, turning this data into actionable insights requires advanced AI models, which must be developed and trained with the right datasets to speed up and improve drug research. Moreover, adaptive clinical trials offer the flexibility to reduce the number of participants or end a trial early if the results are promising. This not only speeds up patient access to treatments but also accelerates market entry. A recent example that comes to mind is the InspIRE study, where strong interim results led to an early termination for success. This was significant as it provided an early glimpse of our new pulse field ablation technology, expected to be a breakthrough in treating atrial fibrillation. To fully harness the power of AI in healthcare, the industry must develop an ecosystem that enables speed, scalability, and widespread adoption. Currently, the most significant challenge lies in the complex and time-consuming process of integrating new software or digitally enabled medical technologies, as each addition requires rigorous approval from IT and security teams. Until these barriers are addressed, the transformative potential of AI will remain out of reach for many healthcare providers. Creating a streamlined, secure, and adaptable framework is essential to unlock AI's full potential and drive meaningful advancements in patient care. The writer is the general manager and lead - Surgery Franchise - MEA at Johnson & Johnson MedTech.

Gulf Business

Fri, 4 Oct, 12:16 PM UTC

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