DataRobot’s Post

In recent years, AI has revolutionized the field of clinical research, leading to significant advancements in medical science. From drug discovery to patient diagnosis and treatment, AI has played a crucial role in transforming healthcare. In this post, we'll explore 10 compelling use cases of AI in clinical research, shedding light on the innovative ways in which this technology is shaping the future of medicine. Stay tuned for insights into how AI is revolutionizing the healthcare industry! #clinicalresearch ,#aihealthcare ,#innovation

On average, it takes 10 to 15 years and over $1.5 billion USD to bring a new drug to market, with over 50% of time and investment going toward the clinical trial phase. Of the many ways we can incorporate #AI into #healthcare, the introduction of AI-based methods to improve the reach, efficacy and cost of #clinicaltrials is perhaps one of the most exciting. Just published in JACC Journals, our paper discusses the framework for which we can utilize begin to leverage AI in #cardiovascular clinical trials. Nivee Amin, MD, MHS Ami Bhatt, MD Krista Zachariah, MD, it was a pleasure partnering with you on such an important piece. Check it out below. #innovation #digitalhealth #clinicaltrials #CVD Heartbeat Health American College of Cardiology https://lnkd.in/ebhm2_ph

An article worth reading. Thanks for this enlightenment Tinglong Dai.

This past September, I had the opportunity to participate in an eye-opening event. The event was a discussion of AI in #medicine held on the campus of Harvard University. During the discussion, a senior faculty leader at Harvard Medical School commented to the effect that research performed at the medical school for a particular specialty had shown that AI could make a patient diagnosis with 90% greater accuracy than a physician. Can #AI be applied to revolutionize #healthcare and medical outcomes? Discover here: https://lnkd.in/gYQGH2vi

The rise of AI applications in healthcare has sparked a wave of concern about the potential for these technologies to replace their roles. This fear is particularly prevalent in specialties heavily driven by logic and data, such as radiology and biochemistry. However, it's essential to understand that AI is not here to replace healthcare professionals but to enhance their capabilities. By becoming AI literate and leveraging these tools, healthcare practitioners can focus on what truly makes their profession unique: the human connection.

JAMA Network Health Systems & Recruitment now has an #AI in Clinical Practice portal. Have you checked it out yet? From the website: "AI in Clinical Practice is the JAMA Network home for the latest scientific advances in artificial intelligence (AI) that impact the practice of medicine, as well as the latest discussion, opinion, and reviews on AI in clinical practice. In a short period of time, AI has touched nearly every aspect of medicine from diagnostic imaging, predictive analytics, healthcare delivery, patient interactions, medical education, among many others. We launched AI in Clinical Practice to provide a home for the rapidly growing body of research and knowledge and to address the critical questions asked by authors and readers around the world..." #AI #HealthCare #HealthTech

To successfully implement AI in healthcare, it is essential not only to support achieving the intended benefits—such as reducing uncertainty through enhanced precision and predictive capabilities—but also to address the new uncertainties that may arise as a result of introducing the technology. In this study, we explore these challenges in the context of AI used to predict the risk of mortality following discharge from the emergency department. Excellent work by Daniel Tyskbo.

Researchers at the National Institutes of Health (NIH) have recently published a study in npj Digital Medicine that explores the use of artificial intelligence in medical diagnostics. The study tested an AI model against human physicians on the New England Journal of Medicine's Image Challenge, evaluating its ability to diagnose diseases from clinical images. Although the AI demonstrated high accuracy, it struggled with effectively describing the images and elucidating its diagnostic reasoning, even when its conclusions were correct. This finding highlights the indispensable role of human expertise in medical diagnostics and underscores the limitations of AI, indicating that AI cannot yet replace the nuanced judgment of healthcare professionals. #AIinHealthcare #MedicalDiagnostics #DigitalMedicine Read more: https://lnkd.in/exjR3MUS

➡️ Experts agree that #AI is essential for managing the growing complexity and cost of clinical trials. Discover more insights from our virtual panel, "Optimizing Clinical Trials at Health Systems: The Opportunity for AI." Watch on-demand now! 🎥https://lnkd.in/exP2Vyir