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We are excited to invite you to an enlightening webinar on "Accelerating Clinical Research and Artificial Intelligence & Machine Learning in Clinical Trials", presented by industry expert Manuj Vangipurapu, Founder and Director of Clinion Pvt. Ltd. ClinoSol Research 📅 Date: 19th May 2024 ⏰ Time: 10:00 AM 📍 Location: Zoom Meeting & YouTube Live This webinar will provide valuable insights into the integration of AI and ML in clinical research, offering a unique opportunity to learn from a leader in the field. Key Highlights: The role of AI and ML in enhancing clinical trials Case studies and real-world applications Future trends and career opportunities in clinical research Why Attend? This webinar offers a unique chance to learn from industry expert Manuj Vangipurapu Vangipurapu on how AI and ML are transforming clinical trials. Attendees will gain valuable insights, stay ahead of industry trends, and explore the future of clinical research. We encourage all students interested in clinical research, data science, and emerging technologies to attend this session. Please find the Registration Link below: https://lnkd.in/gFhQPU8e #ClinosolResearch #ClinicalResearch #FutureOfMedicine #MedicalResearchIndia #StudentWebinar #ZoomSessions #LearnFromExperts #CareerInClinicalResearch

“#Artificial #intelligence (#AI) has unparalleled potential to unlock useful information from #realworlddata to innovate trial design…   Current #clinical #trials are complex, labor-intensive, expensive, and may be prone to unexpected errors and biases (i.e. gender, racial, and socioeconomic #bias). ➡Two of the main causes of high trial failure rates are the poor patient #cohort selection and #recruiting mechanisms, together with the inability to monitor #patients effectively during trials.   Currently, companies or qualified medical institutions are adopting #patientcentric approaches to recruit and engage with trial participants. The practical pattern of patient-centric trials can be established via #digital tools (e.g. mobile #apps and #socialmedia) and collaboration to improve access to clinical trials, alleviate the patient burden, increase the diversity of #participants, and accelerate approval of breakthrough therapies.   In recent years, the use of AI-enabled technologies and real-world #data (#RWD), i.e. scientific data from a variety of sources, in healthcare has started transforming the way we approach clinical trials, which allows us to reshape key steps of clinical trial design. Here, we discuss the potential of #AI to transform the next generation of clinical trials.…[1]”   5 days ago, Nature communications medicine published a useful article / comment.   ✅#My2_cents   The merit of this article is the updated overview of existing approaches, making it easier for trial designers to engage with the puzzle of current ideas.   “AI can be used to inform clinical trial ⚪#eligibility criteria, ⚪enhance the #diversity of participants, and ⚪reduce #samplesize requirements.   Liu et al. developed an open-source AI tool called Trial Pathfinder, which used electronic health record (EHR) data to simulate clinical trials, integrating EHR data according to different inclusion criteria, and analyzing the overall survival risk ratio (defined as the difference in survival rates between two or more groups of patients)[2].” Their publication was cited in many other useful publications (check my references[3-7] for some recently published examples). Among these I would like to highlight 🆔Understanding common key indicators of #successful and #unsuccessful cancer drug trials using a contrast mining framework on ClinicalTrials.gov[8] 🆔Machine Learning in Clinical Trials: A Primer with Applications to Neurology[9] 🆔The need for pragmatic, affordable, and practice-changing real-life clinical trials in oncology[10] 🆔Machine learning enabled subgroup analysis with real-world data to inform clinical trial eligibility criteria design[11] ➡If you are planning a clinical trial for 2024, then you should have tried at least some of the AI-based approaches. Because at some point in the next five years, someone may ask you how your design came about and why you didn't use AI for quality assurance. References check my 1st comment 20231226-10800

We the editors Dr Pushan Kumar D., Dr. Pronaya Bhattacharya, Haipeng Liu, Joel Rodrigues and Gautam Sethi have come up with the new edited volume entitled "Revolutionizing Healthcare 5.0: The Power of Generative AI Transforming Data, Diagnostics, and Decision-Making in Healthcare": Springer is publishing an exciting new book examining how generative deep learning is positioned to transform every facet of healthcare by 2025. We are seeking contributors to author specialized chapters on the applications of variational autoencoders, generative adversarial networks, reinforcement learning, and related unsupervised models across all areas of medicine. Potential topics include but are not limited to: Clinical Decision Support Early Disease Detection Precision Oncology Personalized Medicine Medical Imaging Diagnostics Public Health Informatics Healthcare Data Security This Scopus-indexed compilation will offer a mix of theoretical foundations, techniques, case studies, and practical considerations around deploying healthcare AI responsibly.#HealthcareAI, #DeepLearning, #GenerativeAI, #GANs, #VAEs,#precisionmedicine  #DigitalHealth, #MLinHealth,  #AI4Healthcare, #HealthData, #DataScience, #UnsupervisedML, #NeuralArchitectures,#ClinicalDecisionSupport, #CDS,#Diagnostics, #PopulationHealth, #SyntheticData #PHG, #FederatedLearning, #Privacy, #HealthDataSecurity, #PrecisionMedicine, #MedicalImaging, #springer #springernature #callforproposals

🌟 Diving into the Future of Clinical Research with Generative AI 🌟 I’m thrilled to share that I recently participated in an enlightening webinar organized by the Indian Society for Clinical Research (ISCR), focused on the transformative "Impact of Generative Artificial Intelligence (AI) on Real World Evidence (RWE) studies and clinical research". This session provided deep insights into how emerging technologies are shaping the future of our field. Key Insights from the Webinar: 1. Generative AI’s Role in Clinical Research: The webinar highlighted how Generative AI is transforming data analysis and evidence quality by synthesizing complex data sets, improving our understanding of patient outcomes and streamlining research processes. 2. Ensuring Data Integrity and Patient Safety: We discussed how to integrate Generative AI while maintaining data integrity, patient safety, and PHI confidentiality. It’s crucial to adopt these advancements responsibly to uphold data security and ethical standards. 3. Applied Intelligence and Future Directions: The session explored practical applications of AI in research, including strategies for integrating AI into existing frameworks to enhance decision-making, predict trends, and accelerate research timelines. Highlights from Esteemed Speakers: - Anitha Bhosle, Senior Manager of Patient Safety Solutions at Fortrea, kicked off the webinar with a comprehensive introduction, setting the stage for an in-depth exploration of AI in clinical research. - Uma Janapareddy, Managing Director at SyMetric, provided a detailed analysis of how Generative AI is impacting RWE and clinical research. Her insights highlighted the practical benefits and challenges associated with these technologies. -Dr Arun Bhatt , Consultant in Clinical Research & Drug Development, offered a forward-looking perspective on the future of applied intelligence in clinical research, discussing innovative approaches and potential developments. Why This Matters: This webinar was not just an opportunity to learn about new technologies but a chance to reflect on how we, as professionals in the healthcare and clinical research fields, can leverage these advancements to drive meaningful progress. The integration of Generative AI promises to enhance our ability to conduct more precise research, optimize patient care, and uphold the highest ethical standards. I am excited to apply the knowledge gained from this session to my ongoing projects and continue exploring how AI can contribute to advancing our field. A heartfelt thank you to Indian Society for Clinical Research and the esteemed speakers for an enlightening and thought-provoking experience. #GenerativeAI #ClinicalResearch #HealthcareInnovation #RealWorldEvidence #AI #Pharmaceuticals #DataIntegrity #PatientSafety #FutureOfMedicine #ISCR #HealthcareAI #DataScience #MedicalResearch #PharmaInnovation #AIinHealthcare #ClinicalTrials

AI Revolutionizing Healthcare: The Concept of Deep Medicine In the UK's National Health Service (NHS), challenges such as time constraints, limited resources, and overstretched staff often result in incomplete patient data and less accurate diagnoses. In his 2019 book, American cardiologist Eric Topol introduced the concept of "deep medicine," advocating for the use of artificial intelligence (AI) to transform healthcare. The deep medicine framework, as proposed by Topol, comprises three core pillars: deep phenotyping (comprehensive patient data), deep learning (AI analysis of complex data), and deep empathy (AI streamlining of administrative tasks and fostering better patient-staff relationships). By embracing deep medicine, the NHS could potentially enhance patient care, support healthcare staff, and strengthen the entire healthcare system. Thank you Hiroko Suzuki for your submission!

The The National Institutes of Health has unique needs that drive the development and application of AI/ML tools for electronic health records, imaging, and disease-specific data. The collaboration between The National Institutes of Health, NSF, and DOE Media to launch the National Artificial Intelligence Research Resource (NAIRR) is poised to enhance AI research and application, particularly in #healthcare. By offering secure access to health care records and other data, NIH can provide invaluable resources for AI model development, fostering advancements in medical research, diagnostics, and personalized medicine. Learn more about the NIH's strategic plan to advance healthcare using artificial intelligence: #NIH #TechnologyInnovation #HealthcareAI #NAIRR #Future #StrategicPlan #AI #ML

🧬The Dream Team: Genomics and AI Join Forces to Revolutionize Healthcare The worlds of genomics and artificial intelligence (AI) are no longer strangers; they're becoming fast friends, forming a powerful alliance that's poised to transform healthcare as we know it. Imagine being able to: 🤖Analyze vast genomic datasets with unprecedented speed and accuracy, uncovering hidden patterns and insights into disease. ⚕️Develop AI-powered diagnostics that can detect diseases at their earliest stages, leading to better treatment outcomes. 🩺Design personalized therapies based on an individual's unique genetic makeup, maximizing treatment effectiveness while minimizing side effects. This is the exciting future that awaits us at the intersection of genomics and AI. Let's explore how this dynamic duo is changing the game: 📈Genomics provides the data: Our genetic code holds a treasure trove of information about our health and predisposition to disease. By sequencing and analyzing this data, we can gain valuable insights into individual biology. 🔓AI unlocks the secrets: AI algorithms excel at pattern recognition and complex data analysis. They can sift through massive genomic datasets, identifying subtle patterns that might escape the human eye, leading to groundbreaking discoveries and personalized insights. 🪄Together, they create magic: When combined, genomics and AI create a powerful synergy. AI can analyze genomic data at scale, uncovering hidden connections and potential drug targets. This information can then be used to develop personalized treatments and therapies, ushering in a new era of precision medicine. But there are challenges to overcome: Data privacy and security: Ensuring the responsible use and protection of sensitive genetic data is paramount. Explainability and bias: AI models need to be transparent and unbiased to ensure fair and equitable healthcare outcomes. Accessibility and affordability: Making these advancements accessible to everyone, regardless of their background or socioeconomic status, is crucial. Despite these challenges, the potential of genomics and AI in healthcare is undeniable. From early disease detection to personalized treatment plans, this powerful duo holds the key to unlocking a healthier future for all. #genomics #ai #healthcare #future #linkedin #precisionmedicine #data #ethics #personalizedtreatment #machinelearning

We're thrilled to announce our tools have been featured in a new Nature Scientific Reports publication! This publication underscores Auxillium Health's commitment to patient-centered innovation, improving care for chronic non-healing wounds. Clinical relevance Our study, which includes contributions from Jeffrey Niezgoda and Sandeep Gopalakrishnan of Auxillium Health, aligns with the innovative approaches of Auxillium Health in leveraging Artificial Intelligence for wound care, demonstrating the practical utility of our multi-modal network in clinical settings. Similar to Auxillium Health’s solutions, which utilize deep learning models for real-time wound monitoring and analytics, our network offers a significant advancement in wound image classification, supporting healthcare providers with reliable, data-driven insights for treatment planning. The incorporation of such AI-based tools in clinical practice, as evidenced by previous authors and applications like those developed by Auxillium Health, underscores the transformative potential of AI in enhancing patient care and outcomes. Read more: https://lnkd.in/g-kR_HXD Auxillium Health University of Wisconsin-Milwaukee Adiuvo Diagnostics Pvt. Ltd. Jeffrey Niezgoda Zeyun Yu Bala Pesala Geethanjali Radhakrishnan Aravind Sridhar Aravindhan Palanivel Deepak Devendran Vikasini S Yash Patel Mrinal Kanti Dhar TIRTH SHAH Sandeep Gopalakrishnan

No, AI will not replace radiologists. But #XAI can build a platform for mutually beneficial AI-radiologist-doctor-patient collaboration. The question is how to do it? That's what I plan to work on this summer as part of my Fulbright Poland STEM Impact Award fellowship at Harvard Medical School. (I have been working in data science for healthcare ⚕️ for 20 years, but I did not even dream that it would allow me to work in such a fantastic place - dreams come true) I am very happy to be working with two great researchers from HMS (prof Arkadiusz Sitek and prof Mai Hoang). But if you happen to be in the Boston area and would like to talk about #TrustworthyXAI then let me know, I'd be happy to have a coffee ☕☕☕ around. You will ask, why is explainability important? There are many reasons but they can be based on two pillars  #BLUE-XAI 🟦 good explanations can increase end-user trust in a model's prediction, it also helps to make sure models don't violate patient rights, e.g. don't discriminate #RED XAI 🟥 good explanations can help improve models, increase model performance and make sure model performance is consistent with user knowledge If you are interested in the RED-XAI/BLUE-XAI perspective, you can hear more about it at ICML 2024 or read about it https://lnkd.in/dpi3DjmY

In an exclusive interaction with Express Computer, Srinivasan Raman, Asst. GM-IT, Kokilaben Dhirubhai Ambani Hospital, highlighted the hospital's utilisation of AI and #data analytics to enhance patient care and operational efficiency. He emphasised over predictive analytics, #AI-driven clinical decision support, and #GenAI's potential in the #healthcare ecosystem. Raman also stressed the importance of addressing ethical concerns in AI deployment. Looking forward, he discussed trends like precision diagnostics, Emotion AI, immunomics, and AI's role in drug discovery and chronic disease management. Read here- https://lnkd.in/d8ZmRUni