NEJM AI’s Post

In code, AI thrives, Healing whispers in machines, Health's future arrives.   Artificial Intelligence (AI) can do many things like writing this haiku about AI in healthcare. But with all the promises that AI technologies hold for healthcare to boost efficiency and reduce cost, what more do we need to consider when we integrate AI into the workflows of our healthcare systems?   Our experts Prof Julian Savulescu, Director of the NUS Centre for Biomedical Ethics under the NUS Yong Loo Lin School of Medicine; Prof Marcus Ong, Director of the Health Services and Systems Research Programme at Duke-NUS Medical School; and Assoc Prof Nan Liu from the Centre for Quantitative Medicine at Duke-NUS Medical School, share their insights on incorporating AI innovations in healthcare.

A Transformative Publication by the Swedish National Council of Medical Ethics I'm pleased to share a significant publication titled "AI in Healthcare," released by the Swedish National Council of Medical Ethics as part of their "In Brief" series. This insightful document explores the transformative potential of AI in healthcare, detailing its capabilities to enhance diagnostics, improve patient outcomes, and streamline administrative processes. 🔍 Key Highlights: AI Definitions and Types: Comprehensive analysis of the various forms of AI, including narrow AI, general AI, and the roles of machine learning and deep learning. AI in Diagnostics: Insights into how AI is revolutionising image diagnostics, identifying diseases such as cancer, and predicting conditions like sepsis and cardiovascular diseases. Decision-Making Support: Examination of AI’s potential in providing precise treatment recommendations and supporting complex decision-making processes in healthcare. Patient-Focused Applications: Exploration of AI-driven health apps and wearables that empower patients with personalised health management and self-care tools. Ethical and Societal Challenges: Engagement with critical discussions on patient safety, data quality, and the balance of risks and benefits in AI deployment. This publication is essential reading for professionals interested in the future of healthcare and the innovative applications of AI. It offers valuable insights into how AI can revolutionise healthcare, ensuring better patient care and operational efficiency. #AIinHealthcare #HealthcareInnovation #MedicalAI

This article recommends the ethical use of #AI in healthcare, especially when data sets are trained in a particular geography and used in a different location can lead to inaccurate predictions. Quote from the paper "It suggests that software developers and programmers who work on LMMs that could be used in health care or scientific research should receive the same kinds of ethics training as medics. And it says governments could require developers to register early algorithms, to encourage the publication of negative results and prevent publication bias and hype." https://lnkd.in/dzqAgQaf

Over the past year, my primary focus has been on my academic research, delving into the practical applications of generative AI in the healthcare industry. Many of you have responded to my surveys - thank you! I have published some of my data on researchgate.net. There is more to come. In the meantime, here's some info that might interest the medical writers in my network: From March 2022 to May 2023, the number of medical writers with experience using an AI writing assistant, such as ChatGPT and GPT-4, increased from 19.5% to 69.7%. The difference was statistically significant (p=.0001). You can download the article for free: https://lnkd.in/eGQsqxxM You are welcome to use the data as long as you cite me as the author and researcher. Reference: Bourre, N. (2023). AI in Healthcare Communication: A Study on the Current State of Training and Development. https://lnkd.in/eHPYeSdm #medical #marketing #communications #medcomms #health #healthcare #ai #artificialintelligence #chatgpt #gpt4 #writers #medicalwriter #medicalwriters

As Large Language models become mainstream, healthcare applications are on the rise. With aging populations and a shortage of medical staff, AI can offer solutions. A study led by Shan Chen at Harvard explored GPT-4's potential in cancer-related queries, showing a balance of efficiency and risks. While 77% of oncologists saw time-saving benefits, 7.1% of AI responses were deemed potentially harmful. In our opinion, inherent biases in training data, especially regarding non-white males in medicine, pose challenges for equitable outcomes in health systems. This aspect deserves attention in the pursuit of unbiased AI-generated medical advice. The study also addresses verbosity in AI responses, raising questions about the balance between detailed education and comprehensibility. The call for a 'human in the loop' to scrutinize and edit AI outputs is crucial, emphasizing the potential harm in unedited responses. In reshaping healthcare, AI requires a conscientious approach. Mitigating historical data bias, refining clarity, and maintaining human oversight are essential for harnessing AI's potential while prioritizing patient safety. #AIinHealthcare #MedicalTechnology #HealthEquity #ArtificialIntelligence #HealthcareInnovation Links to originals: https://lnkd.in/gu9WwUHQ https://lnkd.in/g-5CvEfk Harvard University New Scientist

Look at how JAMA (Journal of American Medical Association) is approaching AI. I'm not disputing the concern. I'm suggesting we face the Monitor vs Mentor conversation again. Clearly, AI can do things humans cannot. What would healthcare look like IF the medical community was enthusiastic about designing and implementing cutting edge technology that could transform patient care? "The Authors Guild and 17 authors recently filed a suit against OpenAI for copyright infringement of their works of fiction on behalf of writers whose works were used to train GPT. The complaint states that “Defendants then fed Plaintiffs’ copyrighted works into their…algorithms designed to output human-seeming text responses” and that “at the heart of these algorithms is systematic theft on a mass scale.” How different is this situation from the developments in medicine where physicians are giving away their knowledge to artificial intelligence (AI) on a voluntary basis and spend hours of valuable research time sharing expert knowledge with AI systems. AI has entered the medical field so rapidly and unobtrusively that it seems as if its interactions with the profession have been accepted without due diligence or in-depth consideration. It is clear that AI applications are being developed with the speed of lightning, and from recent publications it becomes frightfully apparent what we are heading for and not all of this is good. AI may be capable of amazing performance in terms of speed, consistency, and accuracy, but all of its operations are built on knowledge derived from experts in the field. We here follow the example of the kidney pathology field to illustrate the developments, emphasizing that this field is only exemplary of other fields in medicine." #ai #genai Paul W. Kevin Rank, MBA

Parexel and colleagues from pharma, a tech solution provider, and an HTA body presented a multi-stakeholder perspective on the use of generative AI, including large language models, in evidence generation for HTA submissions during an Educational Symposium at ISPOR’s Europe conference in Nov ‘23. Read this short summary for their assessment of using ethical AI to streamline health economics and outcomes research.

Parexel and colleagues from pharma, a tech solution provider, and an HTA body presented a multi-stakeholder perspective on the use of generative AI, including large language models, in evidence generation for HTA submissions during an Educational Symposium at ISPOR’s Europe conference in Nov ‘23. Read this short summary for their assessment of using ethical AI to streamline health economics and outcomes research.