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In a recent Clinical Leader article by Abby Proch, leaders in clinical research discuss the transformative impact of AI in the field of clinical research. The article highlights AI's potential to optimise clinical trial processes, from designing protocols and selecting trial sites to reducing costs and streamlining safety surveillance. Importantly, AI also offers the potential to enhance patient engagement in clinical trials and information accessibility. Innovation in AI and its applications in clinical trials will continue to evolve at an exponential pace in 2024 and beyond, and so as the article emphasises, there is a need for all stakeholders within the clinical research ecosystem to up skill in AI concepts and actively contribute to its appropriate development and deployment. A sentiment endorsed by BioCollab and its Founder and Director Julie Ince-Demetriou, PhD. #clinicaltrials #clinicalresearch #aiinhealthcare #innovationinhealthcare #innovationinclinicaltrials #patientexperience #patientrecruitment #clinicaltrialmanagement

🚀 Revolutionizing Clinical Trials with AI: Insights from the Clinical Feasibility Consortium 🌐 🔍 "Harnessing AI for streamlined clinical trial planning and operations has the potential to revolutionize the way clinical research is conducted, making it more efficient, cost-effective, and patient-centric." 🔭 To bring this vision to life, experts from the ZS-sponsored Clinical Feasibility Consortium have deeply explored the integration of AI into clinical trial planning. 📊 Dive into our full article to discover the insights of the Clinical Feasibility Consortium on how AI is set to transform the landscape of clinical research. https://lnkd.in/dxsaCFQj #ClinicalTrials #feasibility #ArtificialIntelligence #ZS

We are excited to announce the results of a study that will be presented at the ASCO Annual Meeting, highlighting the importance of multi-AI model scalability in streamlining the clinical trial site selection process. This study showcases how ConcertAI's Digital Trial Solutions platform can efficiently and effectively identify the most promising sites for clinical research networks, academic centers, and industry-sponsored trials. By leveraging advanced AI and machine learning, ConcertAI is revolutionizing the way clinical trials are conducted, ultimately leading to quicker access to life-saving treatments for patients. Learn more about this study at by visiting poster #429 on June 1st between 9:00 am and 12:00 pm. Read announcement: https://hubs.ly/Q02yYfYn0 #ASCO24 #ClinicalTrialInnovation #Publication #Research #DigitalTrialSolutions

With AI's rise, clinical trials might get the redesign that everyone in that industry has been longing for for years. Here are 5 ways it could get a boost through AI even this year: - Identify underrepresented populations - Optimize trial design & site selection - Turbocharge patient engagement & recruitment strategies - Enable real-time monitoring and adaptive trials - Tackle biases in data collection "While 2024 might not eradicate the lack of representation in clinical trials, thanks to the integration of AI, it will be a pivotal year where significant strides are made. Healthcare leaders have an unprecedented opportunity to harness the potential of AI to address healthcare disparities, particularly within the realm of clinical trials."

Thanks to the integration of AI, 2024 will be a pivotal year where significant strides are made to eradicate the lack of representation in clinical trials.   Healthcare leaders have an unprecedented opportunity to harness the potential of AI to address healthcare disparities, particularly within the realm of clinical trials.   Ryan B. explores five ways AI is poised to transform clinical trials via MedCity News: https://bit.ly/3TCmEAI | #AI #AIinHealthcare #ClinicalTrials #HealthDisparities

FDB is committed to the elimination of preventable medication errors and adverse drug events. The key to this complex challenge is to combine precise and useful medication decision support with more intuitive information systems. #AI #AIinHealthcare #ClinicalTrials #HealthDisparities

With AI's rise, clinical trials might get the redesign that everyone in that industry has been longing for for years. Here are 5 ways it could get a boost through AI even this year: - Identify underrepresented populations - Optimize trial design & site selection - Turbocharge patient engagement & recruitment strategies - Enable real-time monitoring and adaptive trials - Tackle biases in data collection "While 2024 might not eradicate the lack of representation in clinical trials, thanks to the integration of AI, it will be a pivotal year where significant strides are made. Healthcare leaders have an unprecedented opportunity to harness the potential of AI to address healthcare disparities, particularly within the realm of clinical trials."

With AI's rise, clinical trials might get the redesign that everyone in that industry has been longing for for years. Here are 5 ways it could get a boost through AI even this year: - Identify underrepresented populations - Optimize trial design & site selection - Turbocharge patient engagement & recruitment strategies - Enable real-time monitoring and adaptive trials - Tackle biases in data collection "While 2024 might not eradicate the lack of representation in clinical trials, thanks to the integration of AI, it will be a pivotal year where significant strides are made. Healthcare leaders have an unprecedented opportunity to harness the potential of AI to address healthcare disparities, particularly within the realm of clinical trials."

𝗨𝗻𝗹𝗲𝗮𝗿𝗻'𝘀 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗶𝘀 𝗿𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝗶𝘇𝗶𝗻𝗴 𝘁𝗵𝗲 𝗹𝗮𝗻𝗱𝘀𝗰𝗮𝗽𝗲 𝗼𝗳 𝗰𝗹𝗶𝗻𝗶𝗰𝗮𝗹 𝘁𝗿𝗶𝗮𝗹𝘀 𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝘁𝗵𝗲 𝗰𝗿𝗲𝗮𝘁𝗶𝗼𝗻 𝗼𝗳 𝗱𝗶𝗴𝗶𝘁𝗮𝗹 𝘁𝘄𝗶𝗻𝘀. Digital twins are essentially individualized computer simulations of clinical trial participants. This innovative approach leverages artificial intelligence (AI) and machine learning to predict how patients will respond to treatments, thereby enabling smaller, faster, and more efficient studies. 𝘊𝘳𝘦𝘢𝘵𝘪𝘰𝘯 𝘰𝘧 𝘋𝘪𝘨𝘪𝘵𝘢𝘭 𝘛𝘸𝘪𝘯𝘴 Unlearn's AI models are trained on vast amounts of patient data from past clinical trials and real-world sources. The company has aggregated data from thousands of clinical trials and observational studies, covering about a million patients across approximately 30 different indications. This extensive dataset allows Unlearn to create highly accurate digital twins that can predict a patient's response to a treatment. The process begins by taking historical and real-world data about a particular disease and its progression on current treatments. This data is encapsulated within a computer model. When a patient enrolls in a clinical trial, their data at the beginning of the trial is input into the model, which then creates simulations of what might happen to the patient in the future. If the real patient receives the experimental treatment, the digital twin is run through an algorithm to see what would have happened if they didn't receive the treatment. 𝘐𝘮𝘱𝘢𝘤𝘵 𝘰𝘯 𝘊𝘭𝘪𝘯𝘪𝘤𝘢𝘭 𝘛𝘳𝘪𝘢𝘭𝘴 - Reduced Enrollment Needs: By accurately predicting patient outcomes, Unlearn's technology can reduce the size of the control group needed in a clinical trial while maintaining the same statistical power. This can lead to a reduction in patient enrollment requirements by 25% to 50%, significantly shortening the time to complete enrollment and the overall timeline of the clinical trial. - Regulatory Recognition: Unlearn's approach has gained regulatory recognition, with the company going through qualification with the EMA. The FDA has also concurred with EMA’s qualification. - Expansion into New Indications: Initially focused on neurology, Unlearn is expanding its technology to new indications in immunology, metabolic disease, and other areas. https://www.unlearn.ai #ai #artificialintelligence #aiinclinicaltrials

Incorporating machine learning (ML) is opening up fresh pathways to enhance patient outcomes and streamline clinical processes within the rapidly changing healthcare environment. Across disease diagnosis, drug discovery, and epidemic management, ML applications are transforming all aspects of healthcare provision. https://lnkd.in/gi5eWX6z #machinelearning #healthcare #precisionmedicine #artificialneuralnetworks #visualdiagnostics #medicalimaging #medicalprognosis #smartrecords #roboticsurgery #augmentedsurgery #vaccinedevelopment #drugdiscovery #trialoptimization