Nicoleta Economou’s Post

Check out this recently published article, proposing a scalable governance framework to support the trustworthy implementation of algorithmic technologies in patient care and healthcare operations.

Exciting new advancements in AI ethics and governance: This paper delves into the critical need for adaptable AI governance frameworks and outlines some tools to drive technology towards a more responsible and ethical future. We're at a pivotal moment where interdisciplinary collaboration can pave the way for technology that both innovates and safeguards equity and inclusivity. #AIEthics #ResponsibleTech #aioperations

Governance is so important with technology especially in healthcare. I am looking forward to diving deeper in this work published in the Journal of AMIA

Looking forward to the continued national dialogue on Responsible AI in healthcare!

A groundbreaking paper by Duke Health’s Nicoleta Economou, PhD, the Duke Algorithm-Based Clinical Decision Support (ABCDS) Oversight Committee and other Duke University co-authors introduces an Implementation Guide for the Evaluation and Governance of Algorithmic Technologies in Healthcare, aiming to address the challenges of regulating rapidly advancing #AI technologies. https://lnkd.in/eQQZReRT By streamlining oversight processes and promoting education within the Duke community, Duke researchers have developed a scalable framework supporting the trustworthy implementation of algorithmic technologies in patient care and healthcare operations. Grounded in key ethical and quality principles, the guide provides standardized evaluation criteria, fostering objective oversight at Duke. The results showcase improved #safety, #effectiveness, and #equity in algorithms through evidence-backed principles. #HealthTech #Innovation #AIinHealthcare Duke Health, Duke University, Duke Clinical Research Institute, Michael Pencina, Eric Poon, David Gallagher M.D., Armando D. Bedoya MD MMCi, David Leverenz, Michael Gao, Afreen Shariff, MD, Michael Lipkin, Suresh Balu, Kay Lytle, DNP, RN-BC, Scott Elengold, Michael Cary, Sophia Bessias, Nancy Lillard Walden,

In a groundbreaking proposal, researchers led by Dr. Nigam Shah from Stanford University advocate for a public-private partnership to establish a nationwide network of health AI assurance labs. Featured in JAMA, their vision aims to ensure the accuracy and security of AI models in healthcare. In an exclusive interview with Jeremy Faust, MD, editor-in-chief of MedPage Today, Dr. Shah delves into the details of this transformative proposal. The assurance labs concept addresses the current lack of standardized evaluation for AI models in medicine. Dr. Shah draws parallels with car safety standards, emphasizing the need for a systematic approach to test and validate AI models. The labs would play a crucial role in transparently reporting the performance of these models in a national registry. In discussing the future of AI in healthcare, Dr. Shah envisions a scenario where AI aids clinicians in real-time decision-making. He emphasizes the importance of local context in evaluating AI models, challenging the traditional approach of universal external validation. Dr. Shah encourages clinicians to actively engage in the process, whether through meetings, influencing purchasing decisions, or leveraging patient advisory councils. Addressing concerns about data privacy, Dr. Shah advocates for secure data sharing, emphasizing the duty to contribute individual data for the greater good. He highlights the need to update privacy laws to align with the goals of a learning health system. In closing, Dr. Shah discusses the innovative approach of using de-identified datasets for research, balancing the need for sharing knowledge while ensuring data security. https://lnkd.in/gTKxPExF #AI #Healthcare #Medicine #DataSecurity #IP #VC #Patents #DeepTech

This is interesting but should be viewed with the appropriate caveats as the "cognitive biases" that AI models eliminate also intersect with "gut instinct", something experienced physicians use with great effect! #physican #AI #clinicalreasoning

"Personalized Medicine meets Artificial Intelligence", published by @Springer Publishing and edited by Alfredo Cesario, Marika D'Oria, PhD, Charles Auffray and Giovanni Scambia is a brand-new book which aims at addressing the most relevant topics related to the usage of #ArtificialIntelligence in #PersonalizedMedicine. From #PublicHealth and #Biomedical perspectives on personalized medicine and artificial intelligence, up to GDPR regulations, moving on to a focus on the role of big data and #AI in clinical research up to the challenges of the #metaverse and the opportunities of #QuantumComputing, this book will give researchers, data scientists and health professionals a comprehensive overview of the current state-of-the-art of artificial intelligence in personalized medicine. The book will be available for purchase starting from September 23rd!  . #togethermovefirst #digitalmedicine #artificialintelligence #personalizedmedicine

AI is making waves in medical research, but there's more to it than meets the eye. From complex algorithms to potential biases, compromised research is quickly becoming a growing concern, making scientific integrity a top priority. That's why clear guidelines are essential. Here's a quick overview of the guidelines tailored for medical journal publications. By keeping things transparent and accountable, we're not just boosting the quality of AI-driven research, but also maintaining the integrity of medical literature. Check out the full article for more insights: https://ow.ly/3nRa50R3RNp #MedicalResearch #AIInResearch #ScholComm #ResearchIntegrity

This figure visualizes the interdisciplinary impact network of LLMs in healthcare. It outlines the critical issues such as model interpretability, data privacy, and the necessity for continuous improvement. The figure also maps out key groups involved in the development and application of LLMs, including medical professionals, AI scientists, government regulators, and patients and their families. Each group contributes differently: medical professionals advise on clinical needs, AI scientists address technical challenges, regulators ensure compliance with standards, and patients provide feedback to make the models user-centric. The network underscores the importance of collaboration among these diverse stakeholders to effectively integrate LLMs in medical scenarios, ensuring the technology is both clinically effective and ethically sound. ✍🏻 Tang Y.-D., Dong E.-D., and Gao W. LLMs in medicine: The need for advanced evaluation systems for disruptive technologies. The Innovation. April 2, 2024. DOI: 10.1016/j.xinn.2024.100622