Department for Science, Innovation and Technology的动态

Imagine a world where clinical trial timelines are predicted with unparalleled accuracy and based in historical patterns. Now stop imagining, because Kognitic Predict Next-Gen is here to make it a reality. ?? At Kognitic, we are not just reporting, we are predicting- and our latest release is a testament to our commitment to pioneering a new era of AI-powered intelligence. Here are the highlights of our updated Prediction module: ?? Enhanced Accuracy: Kognitic Predict Next-Gen demonstrates superior accuracy compared to Sponsor estimations, particularly at trial launch. Our new model uses neural networks, which when combined with Kognitic’s AI NLPs, can analyze vast clinical trial datasets to identify the patterns within. While sponsor predictions can be off by over 1 year, our forecasts exhibit a deviation of less than 1 quarter, showcasing our enhanced predictive capabilities. ?? Revamped Visualization: Based on user feedback, we've revamped our prediction visuals within the Predict 2.0 module. The platform has a timeline dashboard to track multiple trial launches in one place. Our new launch features intuitive, customizable visuals including confidence scores and prediction summaries for maximal clarity. ?? Compare "Look-alike" Trials: Predict 2.0 allows you to view and compare complete and active "look-alike" trials, empowering you to make informed decisions based on historical data and patterns. Ready to explore the future of predictive analytics in clinical trials? Reach out to Chad Hinrichs ([email protected]) to learn more and unlock the power of Kognitic Predict Next-Gen! ?? #Kognitic #PredictiveAnalytics #ClinicalTrials #AI #Innovation #HealthcareTech #DataScience

"AI isn’t some miracle worker…But if a treatment or study that could help move things forward is not visible between these organizations, it slows down the whole field." Professor Suchi Saria, the founder of Bayesian Health and a member of Duality’s advisory board, emphasizes that “There are very messy, real-world data sets that can provide immense value” if we can harness and utilize this data. Imagine if we could securely share and collaborate on patient data, healthcare research, and more. Organizations have recognized this need and are driving research to integrate new technologies into the industry. Take the Cancer AI Alliance, which recently committed $40M to accelerate AI research in healthcare. Their mission? To bridge the gap between medical and tech expertise, collaboration is central to healthcare advancement. And this mission would sound far fetched if it weren't for Duality's case study with Dana Farber from earlier this year already shows exactly how that can work. A key challenge here is adoption. The healthcare sector is known for its caution around new technologies; until just six years ago, healthcare organizations were largely cloud-averse. But the advantages of AI and ML are too significant to ignore, as we’re already seeing in: 1. Sepsis alerts in emergency rooms: AI systems monitor patients, helping identify sepsis risks early enough to act. 2. Parkinson’s progress monitoring: Real-time data capture provides insights into disease progression beyond what in-person questionnaires reveal. 3. COVID-19 recovery monitoring: AI helps track and predict patient outcomes, identifying those at higher risk of complications. Check out Duality’s Chief Scientist and Co-founder, Professor Shafi Goldwasser—a Turing Award recipient—alongside Professor Suchi Saria, founder of Bayesian Health, and member of Duality’s advisory board, as they discuss the role of ML, AI, and data collaboration in driving #DigitalTransformation in healthcare. Full interview: https://lnkd.in/d9vT-cji #healthcaredata #dataprivacy #datasilos #datacollaboration #healthcareresearch #artificialintelligence

Exciting AI developments in the news today. The UK is well placed as a fulcrum of AI application. Suspect the path of development in health AI application is about to accelerate. Huge, transformational benefits are in the pipeline. Some challenges in mental health to ensure AI is used to its full potential (among others): 1. Ensuring that mental health specific agents are developed using consensus measures of alignment that fit the particular cognitive space that is mental health clinical reasoning, which involves a lot more than signal detection and diagnosis. 2. This year, with advances such as the new Nvidia local compute device, it'll be easier to train and deploy large parameter models locally, offline. Governance of the deployment of a potential multiplicity of agents that such powerful local compute will enable, within an adequate regulatory framework, will be important for patient safety. 3. Utility and safety measurement will likely be different across different specialities. Mental health services have huge scope, involving more than therapy and medication algorithms and heuristics. Specialty specific alignment measures are needed. Frameworks for specialty and subspecialty agent development are needed to support this. 4. As we should co-produce all the services we offer, meaningful involvement of service users should be right at the top of the agenda. If there's a proposal that *reasoning* about care will ever even be partly delivered by any artificial agent, users of services have a right to know and be involved both in the development of agents and in decisions about how they might be used. https://lnkd.in/ezX9UGpD

It is incumbent on those who develop AI to understand the backstory of the data. The social patterning of the data generation process refers to what data we collect, from whom we collect them, how we collect them and with what do we collect them. Health data is collected in a non-neutral space. Non-neutral space means power structures drive the impact of care on patient outcomes. The power structures represent missing confounders when we model the effect of tests and treatments. The best way to failsafe AI is to develop or at least validate the algorithms by local diverse teams which include patients. Such a collaborative approach allows for critical reflection and can build trust among patients in such technologies. https://lnkd.in/ekkrZrB6

?? AI in Healthcare: A Double-Edged Sword? ?? It could make medicine faster and more accessible. But without careful oversight, AI might amplify existing biases in healthcare. The challenge? Harnessing AI's potential while ensuring it helps everyone equally. It's time we ask: Who is building these tools? Are diverse voices represented? How can we ensure AI helps everyone, not just the privileged? Let’s push for stronger regulation and inclusive development! ?? #AI #HealthcareInnovation #HealthEquity #EthicalAI

#PrimaryCare and ultimately the health and well-being of a Whole Person, not just a disease or a data point, requires complex and multifaceted decision-making. I am eager to advise in the development of approaches with Coalition for Health AI (CHAI) with this priority concept in mind - primary care must be a priority stakeholder, not just involved as an afterthought for patient navigation purposes. “Complexity science recognizes that primary care decision making emerges from not only the doctor-patient relationships and knowledge of confounding factors for treating an individual patient such as comorbidities, social determinant challenges, and unique patient attitudes and beliefs, but also the interrelated hierarchical layers and feedback loops of health care systems. AI/ML approaches may harm primary care by acting to minimize an understanding of these complexities, often by limiting the number of features that it uses to develop its algorithms.” #healthcare #technology #AI #wholehealth #healthcareonlinkedin

?? The exponential growth of AI capabilities is ushering in a new era of human potential. Sam Altman's vision of the Intelligence Age presents a compelling framework for understanding the transformative impact of AI on society. As Altman states, "We are more capable not because of genetic change, but because we benefit from the infrastructure of society being way smarter and more capable than any one of us." Key implications for healthcare and beyond: ? AI accelerating medical research and drug discovery ? Personalized AI teams revolutionizing precision medicine ? AI-driven education democratizing access to medical knowledge ? Potential for shared prosperity addressing global health inequities Altman emphasizes: "We need to act wisely but with conviction. The dawn of the Intelligence Age is a momentous development with very complex and extremely high-stakes challenges." And there are challenges to be addressed: ? Ethical considerations in AI-assisted medical decisions ? Potential exacerbation of healthcare disparities ? Cybersecurity risks in AI-integrated healthcare systems ? Regulatory frameworks for AI in healthcare As we navigate this paradigm shift, it's imperative that we proactively address these challenges while harnessing AI's potential to revolutionize healthcare delivery and outcomes. ———— Read: https://ia.samaltman.com/ #AIinHealthcare #MedicalInnovation #FutureofMedicine

Excellent use of AI!

?At UHCW we’re always looking for new ways to innovate and improve the services we provide for our patients. We are leading the way in the NHS in using artificial intelligence (AI) and you can find out how it’s making a positive difference for patients in an article on The Times website. We’ve been working with IBM and other technology partners on a series of projects using techniques such as process mining and generative AI that have helped make us more efficient. One of these projects led to a reduction in the number of last-minute cancellations for Outpatient appointments which has enabled us to see hundreds of additional patients each week. Read more about our pioneering work here ?? https://lnkd.in/eNmJebJx