#digital twin in clinical trials

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Strategic Analysis of the Digital Twins in Healthcare Market

Market Growth and Trends

The Digital Twins in Healthcare market is experiencing significant growth, driven by advancements in artificial intelligence (AI), machine learning, and data analytics. The rising demand for personalized medicine and the increasing focus on predictive healthcare are major factors contributing to the market's expansion.

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Key Applications

Patient Monitoring and Care: Digital twins allow for continuous monitoring of patients, enabling healthcare providers to detect abnormalities early and adjust treatment plans in real-time. This technology is particularly beneficial for managing chronic diseases, where continuous data collection and analysis are crucial.

Surgical Planning and Simulation: Surgeons can use digital twins to simulate and practice complex procedures, reducing risks and improving outcomes. By creating a virtual model of a patient's anatomy, surgeons can plan and execute surgeries with greater precision.

Drug Development and Testing: Pharmaceutical companies are leveraging digital twins to simulate drug interactions and predict outcomes, speeding up the drug development process and reducing costs associated with clinical trials.

Hospital Management: Digital twins are also being used to optimize hospital operations, from managing patient flow to maintaining medical equipment. This results in improved efficiency and reduced operational costs.

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Conclusion

The Digital Twins in Healthcare market is poised to revolutionize the medical field by enabling more personalized, predictive, and efficient care. As technology continues to advance, the adoption of digital twins will likely become a standard practice in healthcare, leading to improved patient outcomes and more efficient healthcare systems.

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AI and Data Science: Optimizing Clinical Trials and Research

The integration of artificial intelligence (AI) and data science into clinical trials represents an important shift in the healthcare industry. These technologies play a crucial role in optimizing various stages of clinical trials, from patient recruitment to data analysis. According to the National Institutes of Health, introducing a new medicine to the market costs over $1 billion. It can take up to 14 years, with a 12-month clinical trial potentially generating up to 3 million data points. By focusing on enhancing clinical trial efficiency and accuracy, AI and data science are changing how medical research is conducted and improving outcomes.

Improving Trials & Research with Artificial Intelligence

Traditional methods of patient recruitment are often slow and inefficient, relying on manual processes that can miss suitable candidates. AI, on the other hand, can analyze large volumes of data from electronic health records (EHRs), social media, and other sources to identify potential participants more quickly and accurately. For instance, AI tools have improved patient recruitment by mining vast datasets to identify suitable candidates, streamlining the process and ensuring that clinical trials are populated with the right participants. This not only saves time but also reduces costs and enhances the success rates of clinical trials, according to the US Food and Drug Administration (FDA).

What Role Does AI Play in Analyzing Real-World Data?

AI has been employed to analyze real-world data from EHRs and medical claims, aiding in the identification of patient cohorts and clinical trial design. This approach enables researchers to create more accurate and representative samples, thereby increasing the validity and reliability of trial outcomes. The FDA noted that predictive modeling in clinical trials, where AI forecasts patient outcomes based on baseline characteristics, enhances participant selection and ensures that the trials are more tailored and effective.

Are AI-Driven Chatbots Transforming Medical Research?

AI-driven chatbots are another innovative application in medical research as they provide accurate information about cancer treatments and clinical trials, improving patient engagement and education. Although they require further refinement to ensure accuracy, their potential to support clinical research and patient care is immense, according to the NCI. The future of AI and data science in clinical research looks promising, with several trends shaping the landscape. The National Institutes of Health’s (NIH) Bridge2AI program, for example, aims to generate AI-ready data and best practices for machine learning analysis, addressing complex biomedical challenges. This program exemplifies the growing trend toward collaborative and interdisciplinary approaches in healthcare research.

Role of AI in Enhancing Real-World Data Analysis

AI’s application in real-world data analysis is another area of significant impact. By analyzing EHRs and medical claims, AI helps identify patient cohorts and design more effective clinical trials. This use of real-world data ensures that clinical trials are based on comprehensive and accurate patient information, leading to more reliable results. The NIH’s Bridge2AI program is a testament to the growing importance of AI in healthcare research, generating AI-ready data and best practices for machine learning analysis.

Additionally, AI’s potential in clinical research is vast, with applications ranging from natural language processing (NLP) to machine learning (ML) to generative AI. These technologies analyze medical literature, extract relevant information, and generate new insights that drive innovation in healthcare. For example, the National Cancer Institute (NCI) funds numerous projects that integrate AI to enhance decision-making and care delivery. AI technologies, such as computer-aided detection and digital twins, are being refined to improve cancer screening and treatment planning. Digital twins, which are computerized ‘twins’ of patients, model medical interventions and provide biofeedback before actual treatment, enhancing the precision and efficacy of interventions, as per the FDA.

Machine Learning (ML) involves algorithms that improve through experience, enabling the identification of patterns in data that can predict drug efficacy. Besides, it helps analyze complex datasets to find correlations that might not be evident through traditional methods. In this regard, Dr. Reddy’s Laboratories’ subsidiary Aurigene introduced an AI and ML-assisted drug discovery platform in April 2024 that uses an iterative ML process for logical and effective chemical design, accelerating projects from hit identification to candidate nomination.

In June 2024, IQVIA launched the OneHome Clinical Trial Technology Platform, utilizing AI and data science to optimize various aspects of clinical trials, particularly in drug discovery. This Gen AI platform is designed to support decentralized trials, enhancing processes such as patient recruitment, real-time data monitoring, and trial management. By integrating with electronic health records (EHRs) and other data sources, OneHome can more efficiently identify eligible participants, potentially reducing recruitment times and improving trial outcomes. The platform’s AI-driven analytics enable continuous monitoring of patient data, allowing for timely interventions and adaptive trial designs, which may lead to more streamlined and accurate clinical trials. This approach demonstrates the increasing role of advanced technologies in enhancing the efficiency and effectiveness of clinical trials.

Case Study: AI in Oncology Clinical Trials

In a recent clinical trial focusing on oncology, AI was utilized to streamline patient recruitment and data analysis. The trial aimed to evaluate the effectiveness of a new immunotherapy treatment for lung cancer. Traditional recruitment methods have been challenging due to the specific patient criteria required. By implementing AI-driven tools to analyze EHRs, the research team identified eligible participants more efficiently.

This approach reduced recruitment time by 30%, enabling the trial to commence sooner than anticipated. Throughout the trial, AI algorithms continuously monitored patient data, providing real-time insights and identifying any anomalies. This proactive monitoring ensured prompt intervention when necessary, thereby maintaining the integrity of the trial and enhancing patient safety. The use of AI also facilitated adaptive trial design, allowing modifications based on interim results, which improved the overall efficacy of the study.

The Importance of Data Science in Clinical Trials

Data science plays a crucial role in managing and analyzing clinical trial data. Its applications in healthcare include clinical trial data management, statistical analysis, and predictive modeling. Handling large and complex datasets allows researchers to draw meaningful insights that drive the development of new treatments and therapies.

For example, data science tools facilitate the visualization of healthcare data, making it easier for researchers to interpret complex results and make informed decisions. Real-time monitoring of clinical data ensures that trials are conducted efficiently, with issues identified and addressed promptly. Predictive modeling is particularly beneficial in clinical trials are it can forecast patient responses to treatments, allowing for more personalized and effective therapeutic approaches. This capability is especially valuable in designing adaptive clinical trials, which can modify protocols based on interim results, thus improving trial efficiency and patient outcomes, according to the FDA.

Final Thoughts: The Future Impact of AI and Data Science on Clinical Trials

In summary, AI and data science are redefining research and clinical trials by enhancing efficiency, accuracy, and patient outcomes. Their applications range from improving patient recruitment and data management to advancing predictive modeling and personalized medicine. As these technologies continue to evolve, they promise to bring about significant breakthroughs in healthcare, paving the way for a more effective and efficient medical research landscape.

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Digital Twins in Healthcare Market Projections: Future Growth and Trends

Digital Twins in Healthcare Market Outlook, Scope & Overview:

Industry reports indicate that the global digital twins in healthcare market was valued at USD 610.1 million in 2023 and is projected to reach USD 4504.73 million by 2031, growing at a CAGR of 25.97% over the forecast period 2024-2031.

Technological Advancements to Drive Growth of Global Digital Twins in Healthcare Market

The adoption of digital twin technology in healthcare will continue to influence global market revenues. Healthcare providers, researchers, and pharmaceutical companies are increasingly utilizing digital twins to enhance patient care, optimize clinical trials, and advance personalized medicine.

As a product segment, digital twin platforms for patient-specific modeling currently hold a significant share of the global digital twins in healthcare market. This segment is anticipated to grow at a year-over-year rate of 25.97% in 2024 over 2023 and reach USD 4504.73 million in revenues by 2031. The increasing need for accurate and real-time patient data for clinical decision-making and treatment planning is expected to drive market growth.

Digital Twins in Healthcare – Market Dynamics

Drivers:

Digital twin technology in healthcare is witnessing significant growth in the global market due to its ability to create virtual models of physical entities, enabling better understanding, analysis, and prediction of patient outcomes. The growing adoption of advanced analytics, AI, and machine learning in healthcare, coupled with the increasing focus on personalized and precision medicine, are key factors driving the adoption of digital twins in healthcare. Additionally, the rising demand for efficient healthcare solutions that can reduce costs and improve patient outcomes is further propelling market growth.

Restraints:

Despite the growth potential, challenges such as high implementation costs, concerns about data privacy and security, and the complexity of integrating digital twin technology with existing healthcare IT infrastructure are hindering the widespread adoption of digital twins in healthcare. Moreover, the need for specialized technical expertise to develop and maintain digital twin models poses additional challenges to market expansion.

Digital Twins in Healthcare – Market Outlook

The proven benefits of digital twins in healthcare, including improved patient outcomes, enhanced clinical trial efficiency, and optimized treatment strategies, have contributed to the market's growth. Digital twins in healthcare are expected to witness increased adoption across major markets, including North America, Europe, and Asia Pacific, driven by advancements in digital health technologies and the growing emphasis on personalized healthcare.

Global Digital Twins in Healthcare Market

The rise in demand for digital twins in healthcare in developed and emerging markets is expected to drive market growth over the forecast period. North America currently holds a significant market share in the global digital twins in healthcare market, with the US being a key contributor to market revenues. Europe and Asia Pacific regions are also experiencing rapid adoption of digital twin technology, supported by favorable regulatory frameworks and increasing investments in healthcare innovation.

Key Players in the Digital Twins in Healthcare Market

Leading companies in the digital twins in healthcare market include Siemens Healthineers, Philips Healthcare, GE Healthcare, and Microsoft Corporation. These companies are at the forefront of developing and commercializing advanced digital twin platforms for various healthcare applications, including patient-specific modeling, disease simulation, and treatment optimization.

In conclusion, the global digital twins in healthcare market is poised for substantial growth over the forecast period, driven by technological advancements, increasing healthcare digitalization, and the expanding adoption of personalized and precision medicine across diverse healthcare settings

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(44) PhD, Postdoc and Academic Positions at Wageningen University & Research in Netherlands

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Early Phase Clinical Trial Outsourcings Market Scope & Growth Projection till 2032

Early Phase Clinical Trial Outsourcings Market provides in-depth analysis of the market state of Early Phase Clinical Trial Outsourcings manufacturers, including best facts and figures, overview, definition, SWOT analysis, expert opinions, and the most current global developments. The research also calculates market size, price, revenue, cost structure, gross margin, sales, and market share, as well as forecasts and growth rates. The report assists in determining the revenue earned by the selling of this report and technology across different application areas.

Geographically, this report is segmented into several key regions, with sales, revenue, market share and growth Rate of Early Phase Clinical Trial Outsourcings in these regions till the forecast period

North America

Middle East and Africa

Asia-Pacific

South America

Europe

Key Attentions of Early Phase Clinical Trial Outsourcings Market Report:

The report offers a comprehensive and broad perspective on the global Early Phase Clinical Trial Outsourcings Market.

The market statistics represented in different Early Phase Clinical Trial Outsourcings segments offers complete industry picture.

Market growth drivers, challenges affecting the development of Early Phase Clinical Trial Outsourcings are analyzed in detail.

The report will help in the analysis of major competitive market scenario, market dynamics of Early Phase Clinical Trial Outsourcings.

Major stakeholders, key companies Early Phase Clinical Trial Outsourcings, investment feasibility and new market entrants study is offered.

Development scope of Early Phase Clinical Trial Outsourcings in each market segment is covered in this report. The macro and micro-economic factors affecting the Early Phase Clinical Trial Outsourcings Market

Advancement is elaborated in this report. The upstream and downstream components of Early Phase Clinical Trial Outsourcings and a comprehensive value chain are explained.

Browse More Details On This Report at @https://www.globalgrowthinsights.com/market-reports/early-phase-clinical-trial-outsourcings-market-101449

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Digital Twin in Healthcare Market: Revolutionizing the Industry

Digital Twin technology is emerging as a revolutionary force in the healthcare industry, offering unprecedented opportunities for enhancing patient care, optimizing operations, and driving innovation. A Digital Twin is a virtual replica of a physical entity, such as a patient, medical device, or even a hospital system, that uses real-time data and advanced simulation techniques to mirror its real-world counterpart. This cutting-edge technology is poised to transform healthcare by enabling personalized medicine, predictive analytics, and efficient management of healthcare resources.

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Latest Developments

Personalized Medicine and Patient Care: The application of Digital Twin technology in personalized medicine has gained significant traction. By creating a virtual model of a patient, healthcare providers can simulate different treatment scenarios and predict their outcomes. This approach allows for tailoring medical interventions to individual patient needs, improving efficacy, and reducing side effects. For instance, in 2023, researchers at the University of California, San Francisco, developed a Digital Twin model to predict the progression of chronic diseases, such as diabetes and cardiovascular conditions, enabling early intervention and better management.

Advanced Surgical Planning: Digital Twins are increasingly used in surgical planning and training. Surgeons can practice complex procedures on virtual models before operating on real patients, enhancing precision and reducing risks. In recent news, Mayo

Clinic collaborated with a leading tech company to create detailed Digital Twins of patients' organs, which are used to plan and rehearse surgeries. This collaboration has led to a significant decrease in surgical errors and improved patient outcomes.

Hospital Management and Operations: The COVID-19 pandemic highlighted the need for efficient hospital management and resource allocation. Digital Twins can simulate hospital operations, predicting patient influx and optimizing the use of medical resources. In a recent development, Cleveland Clinic implemented a Digital Twin of their entire hospital system to manage patient flow and resource distribution during the pandemic. This system proved instrumental in reducing wait times and improving the overall efficiency of hospital operations.

Medical Device Development and Testing: Medical device manufacturers are leveraging Digital Twins to accelerate the development and testing of new products. By creating virtual prototypes, companies can conduct extensive testing in a simulated environment, reducing the need for physical trials and speeding up time-to-market. GE Healthcare, for example, has been at the forefront of using Digital Twins to optimize the design and functionality of their imaging devices, ensuring higher reliability and performance.

Market Analysis

The global Digital Twin in Healthcare market is experiencing robust growth, driven by technological advancements, increasing adoption of IoT and AI in healthcare, and the growing need for personalized medicine. According to a recent report by MarketsandMarkets, the market is expected to grow from USD 1.5 billion in 2021 to USD 5.1 billion by 2026, at a CAGR of 27.2%.

Several factors contribute to this growth:

Technological Advancements: The integration of IoT devices, AI, and machine learning algorithms with Digital Twin technology is enhancing its capabilities. Advanced sensors and wearable devices collect real-time patient data, which is then processed by AI algorithms to create accurate and dynamic Digital Twins. This synergy between technologies is driving the adoption of Digital Twins in healthcare.

Increasing Investment: Significant investments from both public and private sectors are fueling the growth of the Digital Twin market. Governments and healthcare organizations are recognizing the potential of this technology to improve patient care and reduce costs. In 2023, the European Commission launched a major initiative to fund Digital Twin projects in healthcare, aiming to foster innovation and adoption across the continent.

Growing Demand for Personalized Medicine: The shift towards personalized medicine is a major driver for the adoption of Digital Twins. Patients and healthcare providers are increasingly seeking treatments tailored to individual genetic, environmental, and lifestyle factors. Digital Twins enable precise modeling of these factors, facilitating personalized treatment plans and improving patient outcomes.

Regulatory Support: Regulatory bodies are also supporting the use of Digital Twins in healthcare. The FDA, for example, has introduced guidelines for the use of Digital Twins in medical device testing and approval processes. This regulatory support is expected to encourage more healthcare organizations to adopt the technology.

𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐜 𝐒𝐚𝐦𝐩𝐥𝐞 𝐏𝐃𝐅 𝐇𝐞𝐫𝐞-  https://univdatos.com/report/digital-twin-in-healthcare-market/get-a-free-sample-form.php?product_id=58944

Challenges and Future Prospects

Despite its promising potential, the Digital Twin in Healthcare market faces several challenges. Data privacy and security concerns are paramount, as the technology relies heavily on sensitive patient data. Ensuring the accuracy and reliability of Digital Twins is also crucial, as inaccuracies can lead to suboptimal treatment outcomes.

Looking ahead, the future of Digital Twins in healthcare is bright. Continued advancements in AI, machine learning, and data analytics will enhance the capabilities and applications of Digital Twins. Collaboration between technology companies, healthcare providers, and regulatory bodies will be essential to address challenges and drive widespread adoption.

In conclusion, Digital Twin technology is set to revolutionize the healthcare industry, offering immense potential for improving patient care, optimizing operations, and driving innovation. As the technology continues to evolve and mature, its impact on healthcare is expected to grow, ushering in a new era of precision medicine and efficient healthcare management.

Contact Us: UnivDatos Market Insights Email - contact@univdatos.com  Contact Number - +1 9782263411 Website - www.univdatos.com

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How Australian Tech Startups Are Revolutionizing Industries in 2024?

In 2024, Australian tech startups are making a significant impact across various industries, driving innovation and disrupting traditional business models. Here’s how these dynamic startups are revolutionizing industries and setting new standards for success.

1. Healthcare Innovation

Australian tech startups are transforming healthcare with cutting-edge technologies. Companies like HealthMatch and Eucalyptus are leveraging AI and telemedicine to improve patient care and access. HealthMatch’s AI-powered platform matches patients with clinical trials, accelerating medical research, while Eucalyptus provides digital healthcare services, making medical consultations and treatments more accessible.

2. Fintech Advancements

The fintech sector is booming with Australian startups leading the charge. Firms like Afterpay and Airwallex are revolutionizing payment systems and global transactions. Afterpay’s buy-now-pay-later model has redefined consumer credit, while Airwallex offers seamless cross-border payment solutions for businesses, enhancing financial efficiency and reducing costs.

3. Agri-Tech Transformation

Agri-tech startups are reshaping the agricultural landscape. Companies such as AgriDigital and The Yield are utilizing blockchain and IoT technologies to enhance supply chain transparency and optimize farming practices. AgriDigital’s blockchain platform ensures secure and transparent agricultural transactions, while The Yield’s IoT solutions provide real-time data to improve crop management and yield predictions.

4. Education Technology

Edtech startups in Australia are revolutionizing education by making learning more interactive and accessible. Organizations like Cluey Learning and OpenLearning offer personalized online tutoring and collaborative learning platforms. These startups are enhancing educational experiences and ensuring that quality education is accessible to students regardless of their location.

5. Sustainable Energy Solutions

The push for sustainability is driving innovation in the energy sector. Startups like Relectrify and Redback Technologies are developing advanced energy storage and management systems. These innovations support the integration of renewable energy sources, reduce reliance on fossil fuels, and promote sustainable living.

6. Smart Cities Development

Australian startups are also contributing to the development of smart cities. Companies like Urban.io and Lendlease Digital are creating smart infrastructure solutions that improve urban living. Urban.io’s sensor technology enables efficient city management, while Lendlease Digital’s digital twin technology helps in planning and maintaining urban infrastructure.

7. Retail and E-commerce Evolution

Retail and e-commerce are being transformed by tech startups. Brands like Koala and The Iconic are redefining customer experiences with innovative online shopping platforms and sustainable practices. Koala’s direct-to-consumer model and eco-friendly products have set new standards in the furniture industry, while The Iconic offers a seamless online shopping experience with a focus on sustainability.

In conclusion, Australian tech startups are revolutionizing various industries with innovative solutions and technologies. From healthcare to fintech, agri-tech, education, sustainable energy, smart cities, and retail, these startups are driving change and setting new benchmarks for success in 2024. Their contributions are not only advancing their respective industries but also enhancing the quality of life and promoting sustainable practices globally.

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INNOVATIONS TRANSFORMING CANCER TREATMENT: A LOOK AT CUTTING-EDGE TECHNOLOGIES

In the realm of cancer research, what once seemed like science fiction is now a tangible reality, all thanks to a myriad of technological advancements. These innovations are not just changing the way we perceive cancer treatment but are actively accelerating progress against this formidable disease. In this given blog Dr Ashish Gupta will help you understand some groundbreaking technologies that are reshaping the landscape of cancer care and ushering in a new era of hope and possibility.

Artificial Intelligence (AI)

Imagine having a digital twin- a virtual representation of yourself – crafted through sophisticated AI algorithms to help physicians tailor treatment plans with unparalleled precision. This futuristic concept is now within reach, thanks to the remarkable capabilities of artificial intelligence. By sifting through vast troves of data, AI can pinpoint patterns, aiding in cancer diagnosis, drug development, and personalized medicine. As per Dr Ashish Gupta, USA trained, American board-certified medical oncologist, Chief of Medical Oncology, Unique Hospital Cancer Centre, Dwarka India, whether it’s analyzing imaging data or predicting treatment outcomes, AI is poised to revolutionize every facet of cancer care, promising transformative advancements on the horizon.

Telehealth

In an age where connectivity reigns supreme, telehealth emerges as a beacon of hope, seamlessly bridging the gap between patients and cancer care providers. Especially crucial in the midst of a pandemic, telehealth has become instrumental in delivering cancer treatment and running clinical trials remotely. From remote health monitoring to video consultations, telehealth not only enhances accessibility but also ensures safety and convenience for patients and providers alike. However, the journey towards equitable telehealth access poses its own set of challenges, underscoring the ongoing need for research and innovation in this domain.

Cryo-Electron Microscopy (Cryo-EM)

Peer through the lens of cryo-electron microscopy, and you’ll behold a world of molecular intricacies previously unseen. With unprecedented resolution, Cryo-EM enables researchers to delve into the inner workings of molecules, unraveling the mysteries of cancer cell behavior and therapeutic interactions. Recent breakthroughs, such as visualizing drug interactions at the molecular level, exemplify the transformative potential of Cryo-EM in shaping future cancer treatments. As access to this cutting-edge technology expands, so too does our understanding of cancer biology, paving the way for targeted interventions and personalized therapies.

Infinium Assay

The secrets encoded within our genes, the Infinium Assay emerges as a powerful tool in deciphering the genetic underpinnings of cancer. By scrutinizing millions of genetic variations, this innovative assay sheds light on cancer risk, progression, and development. Initially met with skepticism, its journey from conception to widespread adoption showcases the tangible impact of taxpayer-funded innovation. From cancer research to population-wide genomic studies, the Infinium Assay holds immense promise in unraveling the genetic tapestry of cancer and beyond.

Robotic Surgery

Enter the realm of robotic surgery, where precision meets innovation to redefine the surgical landscape. With robotic arms wielding scalpel-like precision, complex cancer surgeries become minimally invasive affairs, offering patients faster recovery times and reduced postoperative discomfort. Beyond the allure of futuristic technology, robotic surgery epitomizes the marriage of precision and compassion, offering renewed hope to cancer patients worldwide.

Conclusion

As we stand at the precipice of a new era in cancer treatment, fueled by cutting-edge technologies, the future holds boundless promise and potential. From AI-driven personalized medicine to the precision of robotic surgery, each innovation represents a stepping stone towards a world where cancer is not just treatable but conquerable. By embracing and harnessing these transformative technologies, we embark on a journey towards a brighter, healthier tomorrow- one where cancer is no longer a formidable adversary but a conquerable challenge. If you are searching best cancer treatment, contact Dr Ashish Gupta, USA trained, American board-certified medical oncologist

[jcmarchi]

Charles Fisher, Ph.D., CEO & Founder of Unlearn – Interview Series

New Post has been published on https://thedigitalinsider.com/charles-fisher-ph-d-ceo-founder-of-unlearn-interview-series/

Charles Fisher, Ph.D., CEO & Founder of Unlearn – Interview Series

Charles Fisher, Ph.D., is the CEO and Founder of Unlearn, a platform harnessing AI to tackle some of the biggest bottlenecks in clinical development: long trial timelines, high costs, and uncertain outcomes. Their novel AI models analyze vast quantities of patient-level data to forecast patients’ health outcomes. By integrating digital twins into clinical trials, Unlearn is able to accelerate clinical research and help bring life-saving new treatments to patients in need.

Charles is a scientist with interests at the intersection of physics, machine learning, and computational biology. Previously, Charles worked as a machine learning engineer at Leap Motion and a computational biologist at Pfizer. He was a Philippe Meyer Fellow in theoretical physics at École Normale Supérieure in Paris, France, and a postdoctoral scientist in biophysics at Boston University. Charles holds a Ph.D. in biophysics from Harvard University and a B.S. in biophysics from the University of Michigan.

You are currently in the minority in your fundamental belief that mathematics and computation should be the foundation of biology. How did you originally reach these conclusions?

That’s probably just because mathematics and computational methods haven’t been emphasized enough in biology education in recent years, but from where I sit, people are starting to change their minds and agree with me. Deep neural networks have given us a new set of tools for complex systems, and automation is helping create the large-scale biological datasets required. I think it’s inevitable that biology transitions to being more of a computational science in the next decade.

How did this belief then transition to launching Unlearn?

In the past, lots of computational methods in biology have been seen as solving toy problems or problems far removed from applications in medicine, which has made it difficult to demonstrate real value. Our goal is to invent new methods in AI to solve problems in medicine, but we’re also focused on finding areas, like in clinical trials, where we can demonstrate real value.

Can you explain Unlearn’s mission to eliminate trial and error in medicine through AI?

It’s common in engineering to design and test a device using a computer model before building the real thing. We’d like to enable something similar in medicine. Can we simulate the effect a treatment will have on a patient before we give it to them? Although I think the field is pretty far from that today, our goal is to invent the technology to make it possible.

How does Unlearn’s use of digital twins in clinical trials accelerate the research process and improve outcomes?

Unlearn invents AI models called digital twin generators (DTGs) that generate digital twins of clinical trial participants. Each participant’s digital twin forecasts what their outcome would be if they received the placebo in a clinical trial. If our DTGs were perfectly accurate, then, in principle, clinical trials could be run without placebo groups. But in practice, all models make mistakes, so we aim to design randomized trials that use smaller placebo groups than traditional trials. This makes it easier to enroll in the study, speeding up trial timelines.

Could you elaborate precisely on what is Unlearn’s regulatory-qualified Prognostic Covariate Adjustment (PROCOVA™) methodology?

PROCOVA™ is the first method we developed that allows participants’ digital twins to be used in clinical trials so that the trial results are robust to mistakes the model may make in its forecasts. Essentially, PROCOVA uses the fact that some of the participants in a study are randomly assigned to the placebo group to correct the digital twins’ forecasts using a statistical method called covariate adjustment. This allows us to design studies that use smaller control groups than normal or that have higher statistical power while ensuring that those studies still provide rigorous assessments of treatment efficacy. We’re also continuing R&D to expand this line of solutions and provide even more powerful studies going forward.

How does Unlearn balance innovation with regulatory compliance in the development of its AI solutions?

Solutions aimed at clinical trials are generally regulated based on their context of use, which means we can develop multiple solutions with different risk profiles that are aimed at different use cases. For example, we developed PROCOVA because it is extremely low risk, which allowed us to pursue a qualification opinion from the European Medicines Agency (EMA) for use as the primary analysis in phase 2 and 3 clinical trials with continuous outcomes. But PROCOVA doesn’t leverage all of the information provided by the digital twins we create for the trial participants—it leaves some performance on the table to align with regulatory guidance. Of course, Unlearn exists to push the boundaries so we can launch more innovative solutions aimed at applications in earlier stage studies or post-hoc analyses where we can use other types of methods (e.g., Bayesian analyses) that provide much more efficiency than we can with PROCOVA.

What have been some of the most significant challenges and breakthroughs for Unlearn in utilizing AI in medicine?

The biggest challenge for us and anyone else involved in applying AI to problems in medicine is cultural. Currently, the vast majority of researchers in medicine specifically are not extremely familiar with AI, and they are usually misinformed about how the underlying technologies actually work. As a result, most people are highly skeptical that AI will be useful in the near term. I think that will inevitably change in the coming years, but biology and medicine generally lag behind most other fields when it comes to the adoption of new computer technologies. We’ve had many technological breakthroughs, but the most important things for gaining adoption are probably proof points from regulators or customers.

What is your overarching vision for using mathematics and computation in biology?

 In my opinion, we can only call something “a science” if its goal is to make accurate, quantitative predictions about the results of future experiments. Right now, roughly 90% of the drugs that enter human clinical trials fail, usually because they don’t actually work. So, we’re really far from making accurate, quantitative predictions right now when it comes to most areas of biology and medicine. I don’t think that changes until the core of those disciplines change–until mathematics and computational methods become the core reasoning tools of biology. My hope is that the work we’re doing at Unlearn highlights the value of taking an “AI-first” approach to solving an important practical problem in medical research, and future researchers can take that culture and apply it to a broader set of problems.

Thank you for the great interview, readers who wish to learn more should visit Unlearn.

[thehealthcareinsights-blog]

The Promise of Digital Twin Technology in Personalized Healthcare

Healthcare is the fastest and most highly evolving sector in today’s world, along with the birth of different technologies that are playing a vital role in the medical field in terms of solving new problems uniquely and helping professionals provide effective treatments. Digital twin technology is used in many fields and healthcare is one of its applications. DT is one among healthcare technologies and is an innovative approach that uses digital replicas of physical entities to predict various health-related issues. This has become a promising tool that can be used in transforming personalized healthcare. In this article, let us discuss in detail this technology and its prominent role in finding solutions to various health-related problems that are improving patient care. 

Defining Digital Twin Technology 

Digital twin technology is the process of creating or making virtual models that are replicas of real entities like human organs, systems, physiological acts, or even the entire organism. These virtual models can be used later for simulation, and analysis, and hence we can learn or understand the behavior of an individual in different conditions. This helps medical professionals or specialists to study the particular person in detail which in turn can be more effective and accurate in the diagnosis process. 

A DT on creating a virtual model can used to conduct surgeries and oversee the effects virtually that might occur on undergoing the real surgery on the patient’s body. This will help in preventing future harm and suffering from pain. All the virtual replicas are updated regularly with data from different sources like wearable health devices, genetic information, and electronic health records (EHRs) for maintaining accuracy. Thus, by integrating all this information doctors and find valuable insights into a patient’s health conditions and hence can provide personalized diagnoses.

Digital Twin Technology working in healthcare 

Digital twin technology mainly works by data integration from different healthcare resources. But it is done in three steps which include the creation of a blueprint, the construction of the first digital twin model, and the third is enhancing its capacity. The data that is integrated will contain the important elements of the patient like medical history, lifestyle, genetic makeup, physical behavior, and emotional factors. All these can be gathered using sensors or wearable medical devices. 

Later these digital twins are analyzed using advanced machine learning techniques that can identify and provide meaning or informative patterns, and prediction of future outcomes, and will also give suggestions that can be adopted in personalized treatment plans.

Benefits of Digital Twin Technology 

A DT technology can be used as the predicting analysis that showcases the results of your diagnosis without actually doing it on the human body. The process of regular and continuous monitoring of the updated real-time data through virtual twins doctors can detect the symptoms at the early stage and is an alert for professionals so that they can take precautionary measures for patients. With his proactive approach, many diseases can be prevented well before.

It is also a very useful approach to monitoring patients remotely as one need not visit physically to provide or get their health updates. This benefits chronic patients as it gives access to continous support and care by healthcare providers. The major use is to provide personalized care and precision medicines based on the individual’s unique character and genomic factor. Along with this analysis of various scenarios, one can get optimization in treatment depending on the outcome effects with this, doctors can adopt effective and better treatment strategies. Apart from all these DT is a powerful tool for learning drug effects, disease symptoms, and research areas for conducting clinical trials.

Conclusion

To conclude, along with its benefits in various areas of healthcare, certain challenges need to be encountered. The major difficulty is data privacy and security which comes with the major concern of misusing personal and detailed information of the patient. Accuracy may be also an issue as in some cases predictions may go wrong and may cause severe effects. With careful planning and implementation, digital twin technology has the potential to improve patient outcomes and transform the future of healthcare.

Visit More : https://thehealthcareinsights.com/the-promise-of-digital-twin-technology-in-personalized-healthcare/

[texploration]

Virtual Trials and Digital Twins in Clinical Trials

In the intricate and evolving landscape of healthcare, clinical trials stand as pivotal voyages of discovery, essential for unearthing new treatments and advancing medical knowledge. Yet, these crucial journeys are not without their challenges. Traditional clinical trials are often beleaguered by a myriad of obstacles – from the arduous task of enrolling enough suitable patients, to the…

[lanettcdmo]

Innovations Transforming Pharmaceutical Manufacturing

Introduction:

In the dynamic landscape of pharmaceutical manufacturing, constant innovation is the key to meeting the growing demand for safe and effective medications. As technology continues to advance, the industry is undergoing a transformative phase that brings about efficiency, quality improvements, and increased agility. This blog will explore some of the notable innovations shaping the future of pharmaceutical manufacturing.

Advanced Process Automation: Automation is revolutionizing pharmaceutical manufacturing by streamlining processes, reducing human error, and enhancing overall efficiency. From robotic process automation (RPA) in packaging to automated quality control systems, these technologies not only improve production speed but also ensure precision in drug formulation.

Digital Twins in Manufacturing: The concept of digital twins involves creating a virtual replica of the manufacturing process. This allows for real-time monitoring, analysis, and optimization of production. By integrating sensors and data analytics, pharmaceutical manufacturers can identify potential issues before they occur, minimizing downtime and improving overall quality.

Continuous Manufacturing: Traditional batch manufacturing is giving way to continuous manufacturing, a process that operates 24/7 without interruptions. This approach offers several advantages, including reduced production time, increased flexibility in adjusting formulations, and enhanced scalability. Continuous manufacturing aligns with the industry’s goals of improving efficiency and reducing waste.

3D Printing of Pharmaceuticals: 3D printing technology is making waves in pharmaceutical manufacturing by allowing the creation of personalized medications. This innovation enables the production of intricate drug structures that may have improved bioavailability and targeted drug delivery. The ability to customize drug formulations based on individual patient needs holds great promise for the future of medicine.

Blockchain for Supply Chain Transparency: Blockchain technology is being implemented to enhance transparency and traceability in the pharmaceutical supply chain. By providing an immutable and decentralized ledger, blockchain ensures the integrity of the entire manufacturing and distribution process. This not only helps in preventing counterfeit drugs but also facilitates quicker recalls and compliance with regulatory standards.

Artificial Intelligence (AI) in Drug Discovery: While not directly related to manufacturing, AI plays a crucial role in drug discovery, leading to the development of more effective medications. Machine learning algorithms analyze vast datasets to identify potential drug candidates and predict their success in clinical trials. This accelerates the drug development process and ultimately impacts manufacturing by bringing new products to market faster.

Conclusion:

The pharmaceutical contract manufacturing landscape is undergoing a significant transformation fueled by technological advancements. From automated processes to innovative approaches like continuous manufacturing and 3D printing, these changes are not only improving efficiency but also paving the way for personalized medicine. As the industry continues to embrace these innovations, the future holds exciting possibilities for safer, more effective, and accessible pharmaceuticals.

[moremedtech]

Wearable tech, AI and clinical teams join to change the face of trial monitoring

Wearable tech, AI and clinical teams join to change the face of trial monitoring. A multi-disciplinary team of researchers has developed a way to monitor the progression of movement disorders using motion capture technology and AI. In two ground-breaking studies, published in Nature Medicine, a cross-disciplinary team of AI and clinical researchers have shown that by combining human movement data gathered from wearable tech with a powerful new medical AI technology they are able to identify clear movement patterns, predict future disease progression and significantly increase the efficiency of clinical trials in two very different rare disorders, Duchenne muscular dystrophy (DMD) and Friedreich's ataxia (FA). DMD and FA are rare, degenerative, genetic diseases that affect movement and eventually lead to paralysis. There are currently no cures for either disease, but researchers hope that these results will significantly speed up the search for new treatments. Tracking the progression of FA and DMD is normally done through intensive testing in a clinical setting. These papers offer a significantly more precise assessment that also increases the accuracy and objectivity of the data collected. The researchers estimate that using these disease markers mean that significantly fewer patients are required to develop a new drug when compared to current methods. This is particularly important for rare diseases where it can be hard to identify suitable patients. Scientists hope that as well as using the technology to monitor patients in clinical trials, it could also one day be used to monitor or diagnose a range of common diseases that affect movement behavior such as dementia, stroke and orthopedic conditions. “Our approach gathers huge amounts of data from a person’s full-body movement – more than any neurologist will have the precision or time to observe in a patient. Our AI technology builds a digital twin of the patient and allows us to make unprecedented, precise predictions of how an individual patient’s disease will progress. We believe that the same AI technology working in two very different diseases, shows how promising it is to be applied to many diseases and help us to develop treatments for many more diseases even faster, cheaper and more precisely. Senior and corresponding author of both papers, Professor Aldo Faisal, from Imperial College London's Departments of Bioengineering and Computing, who is also Director of the UKRI Center for Doctoral Training in AI for Healthcare, and the Chair for Digital Health at the University of Bayreuth (Germany), and a UKRI Turing AI Fellowship holder, said Dr Balasundaram Kadirvelu speaks with Luchen Li who models the suit. Credit: Thomas Angus/Imperial College London The two papers highlight the work of a large collaboration of researchers and expertise, across AI technology, engineering, genetics and clinical specialties. These include researchers at Imperial, the UKRI Centre in AI for Healthcare, the MRC London Institute of Medical Sciences (MRC LMS), UCL Great Ormond Street Institute for Child Health (UCL GOS ICH), the NIHR Great Ormond Street Hospital Biomedical Research Centre (NIHR GOSH BRC), Ataxia Centre at UCL Queen Square Institute of Neurology, Great Ormond Street Hospital, the National Hospital for Neurology and Neurosurgery (UCLH and UCL/UCL BRC), the University of Bayreuth, the Gemelli Hospital in Rome, Italy, and NIHR Imperial College Research Facility.

Movement fingerprints – the trials in detail

“Patients and families often want to know how their disease is progressing, and motion capture technology combined with AI could help to provide this information. We’re hoping that this research has the potential to transform clinical trials in rare movement disorders, as well as improve diagnosis and monitoring for patients above human performance levels.” Co-author of both studies Professor Richard Festenstein, from the MRC London Institute of Medical Sciences and Department of Brain Sciences at Imperial said: In the DMD-focused study, researchers and clinicians at Imperial, Great Ormond Street Hospital and University College London trialed the body worn sensor suit in 21 children with DMD and 17 healthy age-matched controls. The children wore the sensors while carrying out standard clinical assessments (like the 6-minute walk test) as well as going about their everyday activities like having lunch or playing. In the FA study, teams at Imperial, the Ataxia Centre, UCL Queen Square Institute of Neurology and the MRC London Institute of Medical Sciences worked with patients to identify key movement patterns and predict genetic markers of disease. FA is the most common inherited ataxia and is caused by an unusually large triplet repeat of DNA, which switches off the FA gene. Using this new AI technology, the team were able to use movement data to accurately predict the ‘switching off’ of the FA gene, measuring how active it was without the need to take any biological samples from patients. The team were able to administer a rating scale to determine level of disability of ataxia SARA and functional assessments like walking, hand/arms movements (SCAFI) in 9 FA patients and matching controls. The results of these validated clinical assessments were then compared with the one obtained from using the novel technology on the same patients and controls. The latter showing more sensitivity in predicting disease progression. In both studies, all the data from the sensors was collected and fed into the AI technology to create individual avatars and analyze movements. This vast data set and powerful computing tool allowed researchers to define key movement fingerprints seen in children with DMD as well as adults with FA, that were different in the control group. Many of these AI-based movement patterns had not been described clinically before in either DMD or FA. Scientists also discovered that the new AI technique could also significantly improve predictions of how individual patients’ disease would progress over six months compared to current gold-standard assessments. Such a precise prediction allows to run clinical trials more efficiently so that patients can access novel therapies quicker, and also help dose drugs more precisely.

Smaller numbers for future clinical trials

This new way of analyzing full-body movement measurements provide clinical teams with clear disease markers and progression predictions. These are invaluable tools during clinical trials to measure the benefits of new treatments. The new technology could help researchers carry out clinical trials of conditions that affect movement more quickly and accurately. In the DMD study, researchers showed that this new technology could reduce the numbers of children required to detect if a novel treatment would be working to a quarter of those required with current methods. Similarly, in the FA study, the researchers showed that they could achieve the same precision with 10 of patients instead of over 160. This AI technology is especially powerful when studying rare diseases, when patient populations are smaller. In addition, the technology allows to study patients across life-changing disease events such as loss of ambulation whereas current clinical trials target either ambulant or non-ambulant patient cohorts. “These studies show how innovative technology can significantly improve the way we study diseases day-to-day. The impact of this, alongside specialized clinical knowledge, will not only improve the efficiency of clinical trials but has the potential to translate across a huge variety of conditions that impact movement. It is thanks to collaborations across research institutes, hospitals, clinical specialties and with dedicated patients and families that we can start solving the challenging problems facing rare disease research.” Co-author on both studies Professor Thomas Voit, Director of the NIHR Great Ormond Street Biomedical Research Centre (NIHR GOSH BRC) and Professor of Developmental Neurosciences at UCL GOS ICH, said: “We were surprised to see how our AI algorithm was able to spot some novel ways of analyzing human movements. We call them ‘behavior fingerprints’ because just like your hand’s fingerprints allow us to identify a person, these digital fingerprints characterize the disease precisely, no matter whether the patient is in a wheelchair or walking, in the clinic doing an assessment or having lunch in a café.” Joint first author on both studies, Dr Balasundaram Kadirvelu, post-doctoral researcher at Imperial’s Departments of Computing and Bioengineering, said “Researching rare conditions can be substantially more costly and logistically challenging, which means that patients are missing out on potential new treatments. Increasing the efficiency of clinical trials gives us hope that we can test many more treatments successfully.” Joint first author on the DMD study and co-author on the FA study, Dr Valeria Ricotti, honorary clinical lecturer at the UCL GOS ICH said “We are thrilled with the results of this project that showed how AI approaches are certainly superior in capturing progression of the disease in a rare disease like Friedreich’s ataxia. With this novel approach we can revolutionize clinical trial design for new drugs and monitor the effects of already existing drugs with an accuracy that was unknown with previous methods.” Co-author Professor Paola Giunti, Head of UCL Ataxia Centre, Queen Square Institute of Neurology, and Honorary Consultant at the National Hospital for Neurology and Neurosurgery, UCLH, said “The large number of FA patients who were very well characterized both clinically and genetically at the Ataxia Centre UCL Queen Square Institute of Neurology in addition to our crucial input on the clinical protocol has made the project possible. We are also grateful to all our patients who participated in this project.” The research was funded by a UKRI Turing AI Fellowship to Professor Faisal, NIHR Imperial College Biomedical Research Centre (BRC), the MRC London Institute of Medical Sciences, the Duchenne Research Fund, the NIHR Great Ormond Street Hospital (GOSH) BRC, the UCL/UCLH BRC, and the UKRI Medical Research Council. More information: Valeria Ricotti et al, Wearable full-body motion tracking of activities of daily living predicts disease trajectory in Duchenne muscular dystrophy, Nature Medicine (2023). DOI: 10.1038/s41591-022-02045-1 Balasundaram Kadirvelu et al, A wearable motion capture suit and machine learning predict disease progression in Friedreich's ataxia, Nature Medicine (2023). DOI: 10.1038/s41591-022-02159-6 Journal information: Nature Medicine Source: Imperial College London Read the full article

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How ‘digital twins’ could change the way we develop new drugs

How ‘digital twins’ could change the way we develop new drugs

Unlearn.ai’s technology aims to shorten drug approval time—and improve the ethics of clinical trials. Clinical trials are the quicksand of drug development. Clinical trials to support a new drug can drag out for six to seven years, on average, with a median cost of $19 million. Now, using machine learning and generative AI, a startup called Unlearn.ai is aiming