Micheleen Harris

FOCUSING ML HEALTHCARE RESEARCH ON VERIFIABLE, EQUITABLE PATIENT BENEFITS ⬇️ This paper focuses on shifting the application of ML in healthcare from mere technical novelty to achieving equitable impact. It critically examines how the current incentive structures in healthcare ML research prioritize technological innovation over real-world clinical outcomes, and proposes a reorientation towards impactful applications. 1️⃣ Emphasizes the need to distinguish between ML applied to healthcare data for technical challenges and ML designed with the primary goal of solving clinical problems. 2️⃣ Highlights the common disconnect between healthcare ML research goals and tangible clinical impacts, suggesting that it should aim for direct improvements in patient care. 3️⃣ Critiques the healthcare ML community for often neglecting meaningful deployment and integration of technologies within healthcare settings, leading to limited practical use despite technical advancements. 4️⃣ Proposes a series of "Impact Challenges" designed to refocus efforts on real-world health equity and effective clinical applications of ML technologies. 5️⃣ Discusses structural barriers within the field, such as funding biases towards novelty, and offers recommendations for promoting research that addresses these barriers to enhance the real-world applicability and impact of healthcare ML. ✍🏻 Aparna Balagopalan, Ioana Baldini, Leo Anthony Celi, Judy Gichoya Liam McCoy, Tristan Naumann, Uri Shalit, Mihaela van der Schaar, Kiri L. Wagstaff. PLOS Digital Health. April 15, 2024. DOI: 10.1371/journal.pdig.0000474 💡 Focusing on equitable impact is not just ethically imperative but also essential for the sustainable integration of AI technologies in healthcare, where they can genuinely contribute to health outcomes and not exacerbate existing disparities. 💥 Join our community of 30.000 curious minds! ✅ Subscribe to our newsletter for your front-row seat to the latest groundbreaking studies. Get started here: https://lnkd.in/eR7qichj.

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This paper examines the economics and equity challenges of implementing LLMs in healthcare, focusing on three pathways: training from scratch, fine-tuning pretrained models, and using out-of-the-box models, and evaluates their trade-offs in cost, customization, and accessibility. 1️⃣ Training from scratch offers maximum customization and transparency by building LLMs on specific healthcare data but requires significant resources in infrastructure, workforce, and upfront costs. 2️⃣ Fine-tuning pretrained models balances cost and customization by adapting existing LLMs with healthcare-specific data but risks biases from the original training data and has limited transparency. 3️⃣ Using out-of-the-box models enables rapid deployment with minimal infrastructure and workforce needs but offers limited customization and transparency, often relying on general-purpose LLMs. 4️⃣ Cloud service providers play a critical role in scaling LLMs through managed services, federated learning, and hardware accelerators, but affordability and equitable access remain challenges. 5️⃣ Privacy, ethics, and regulatory considerations, including data deidentification and compliance with standards such as HIPAA and GDPR, are essential for LLM adoption in healthcare. 6️⃣ Potential applications of LLMs include chatbots, clinical decision support, and data summarization, but ongoing efforts are needed to mitigate biases, toxicity, and ensure model transparency. ✍🏻 Radha Nagarajan, FHIMSS, FAMIA, Midori Kondo, Franz Salas, Emre Sezgin, Yuan Yao, Vanessa K., Sandip Godambe, Naqi Khan, MD, MS, Alfonso Limon, Ph.D., Graham Stephenson, Sharief Taraman, MD, DABPN, DABPM, FAAP, Nephi Walton, Louis Ehwerhemuepha, Jay Pandit, Deepti Pandita, Michael Weiss, Charles Golden, Adam Gold, John Henderson, Angela Shippy MD, Leo Anthony Celi, Bill Hogan, Eric Oermann, Terence Sanger, Steven Martel, MD. Economics and Equity of Large Language Models: Health Care Perspective. Journal of Medical Internet Research. 2024. DOI: 10.2196/64226 📄 Planning to submit your research to a peer-reviewed, open-access journal? Use the discount code AMJB200 at checkout to save $200 USD on the article processing charge at any JMIR Publications journal. Start your submission here: https://lnkd.in/deuAEN2r. It's important to me that you know this isn't a sponsored post. I'm simply committed to keeping you informed about opportunities to disseminate your research effectively.

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Snowflake’s BUILD 2024 AI/ML Development Conference Highlight – AI, Agents, and Data by Andrew Ng (Founder of LandingAI) The session centered on the evolution of AI with a specific focus on AI agents and the emerging paradigm of agentic reasoning. This new approach emphasizes the ability of AI to engage in iterative, goal-directed workflows, fundamentally transforming how applications are developed and deployed. With the exponential growth in the volume and complexity of data, the role of unstructured data—text, images, audio, and video—is becoming a cornerstone in driving automation and innovation across industries. Ng's discussion explored how this convergence of AI agents, agentic reasoning, and unstructured data is poised to revolutionize automation and application development in a cost-effective and scalable way. Recent advancements in large language models (LLMs) and large multimodal models (LMMs) have drawn significant attention due to their broad applicability and power. Building on these foundational technologies, AI agents are emerging as the next significant breakthrough. These agents, designed to reason and interact iteratively, are proving transformative in industries that require complex decision-making and operational efficiencies. As the emphasis shifts from models to applications, the importance of effective data engineering and the integration of unstructured data in workflows will shape the next generation of AI-driven solutions. Video Link: https://lnkd.in/e2njUdFC #ArtificialIntelligence #AIAgents #AgenticReasoning #LLMs #LargeLanguageModels #Innovation #LargeMultimodalModels #LMMs

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At a time when the hype cycle for “AI” is in various stages of peaking, the awarding of the Nobel in Physics to Hopfield and Hinton for their work on #MachineLearning is a good time to reflect on the impacts we already experience from this field: 🔬Diagnostic imaging accuracy, speed, and ability to find life threatening conditions 💊 Drug development and new uses for existing treatments that treat what was previously incurable 💸 Fraud detection, credit scoring, and automated investing make financial tools and resources more accessible to more people in need 🚗 Public transportation and self-driving vehicles make driving safer and less impactful to the environment 🛍️ Retail and marketing in general are more personalized and real-time 📱Technology like speech recognition and translation allow us to communicate, learn, and share more seamlessly and globally ➡️➡️What are some of the ways you experience the impact of ML models in life and work? ⬅️⬅️ #AI #ml #cybersecurity #nobelprize #nobel2024 #artificialintelligence #healthcare #research #drugdiscovery #imaging #predictivemedicine #medicine #surgery #radiology #pharmacy #predictiveanalytics #neuralnetworks #gpu #medtech #fintech #edtech Siemens Healthineers GE HealthCare Roche Novartis Pfizer Moderna Medtronic Intuitive Merck AstraZeneca Tesla Apple Amazon Amazon Web Services (AWS) Google OpenAI Microsoft Toshiba Philips NVIDIA AMD TSMC https://lnkd.in/eePQtdaY

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What is the best approach to removing bias from clinical AI models? Debiasing algorithms have become a more popular approach to addressing fairness issues in AI models, but does addressing bias after the model has already been built make for the most effective approach? Haoran Zhang, Walter Gerych, and MIT Jameel Clinic PI Marzyeh Ghassemi recently published a Comment in Nature Reviews Methods Primers on how a more data-centric approach is needed to resolve the historic biases that reflect long-standing disparities in medical treatment and diagnosis. The researchers argue that addressing the source of bias will reduce the effort needed to build machine learning models that are not only fair, but also capable of generalizing across different healthcare environments and populations. Paper: https://lnkd.in/epg7i9wF #aiforgood #aiforhealth #aibias #aisafety #machinelearningsolutions

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Synthetic Data for LLM applications Synthetic healthcare datasets have been around for some time. Whenever we discuss synthetic data, we often find ourselves trapped in one question: How realistic is the synthetic data? We create synthetic data because we cannot share the original data for various obvious reasons, e.g., privacy, security, etc. The challenging part is that we want the synthetic data to have all the nice characteristics of the original data. With all the challenges aside, many researchers adopted and tried to prove the value of synthetic data. This morning, I came across this well-summarized survey article, "Synthetic data in healthcare: A narrative review," by Aldren Gonzales, Guruprabha Guruswamy, and Scott Smith. According to the paper, people have used synthetic data for seven categories: 1. Simulation and Prediction Research 2. Hypothesis, Methods, and Algorithm Testing 3. Epidemiological Study/Public Health Research 4. Health IT Development and Testing 5. Education and Training 6. Public Release of Datasets 7. Linking data I have been thinking about synthetic data quite a bit these days. It's because of the Large Language Models (LLMs) like ChatGPT by OpenAI and Claude by Anthropic. I have been playing around with those LLMs. I realize these days that, even if you are an expert, it's almost impossible to predict the outputs. As we cannot logically bind the output behaviors, testing an LLM model with all possible(or available) cases is the only way. Here, I think, is the new application of synthetic data: LLM application testing. If we were to build many LLM applications, we would need a lot of synthetic healthcare data. In this case, the synthetic data do not need to be hyper-realistic. They just need to represent the "quirkiness" of the real-life use cases, so weird synthetic data should be fine. Healthcare-focused LLM applications would need to test with all sorts of available synthetic data to see if such applications produce weird outputs. We may need a mechanism to do so. I think this new use case of synthetic data will be critical in healthcare. Let's see. [1] https://lnkd.in/e8DiEH9j

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👉🏼 Expansive data, extensive model: Investigating discussion topics around LLM through unsupervised machine learning in academic papers and news 🤓 Hae Sun Jung 👇🏻 https://lnkd.in/ef_MF54v 🔍 Focus on data insights: - Utilized data from Web of Science and LexisNexis to explore topic modeling methods for Large Language Models (LLMs). - Evaluated various topic modeling approaches such as LDA, NMF, CTM, and BERTopic based on diversity and coherence metrics. - BERTopic demonstrated superior performance in diversity and coherence across both platforms. 💡 Main outcomes and implications: - News articles covered a wide range of topics related to LLM applications, focusing on specialized domains. - Research papers concentrated on technical aspects of LLM technology. - Insights gained can help understand future challenges and opportunities for LLMs in different sectors. 📚 Field significance: - Enhances understanding of how LLMs are utilized in academic and news contexts. - Provides insights for enterprises leveraging LLMs for service delivery. 🗄️: #topicmodeling #unsupervisedlearning #machinelearning #datainsights

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👉🏼 Potential roles of large language models in production of systematic reviews and meta-analyses 🤓 Xufei Luo 👇🏻 https://lnkd.in/eVTxQ6Ce 🔍 Focus on data insights: - Large language models (LLMs) like ChatGPT are increasingly utilized in medical research. - LLMs can expedite steps in systematic reviews such as defining clinical questions, literature search, and information extraction. - Attention to transparent reporting and avoiding academic misconduct is crucial when using LLMs. 💡 Main outcomes and implications: - Enhanced efficiency and resource conservation in systematic reviews with the use of LLMs. - Need for clear differentiation between genuine and false content generated by LLMs. - Future research directions to maximize the benefits of LLMs in systematic reviews. 📚 Field significance: - Advancements in utilizing LLMs for systematic reviews and meta-analyses. - Importance of ethical considerations and quality control when integrating LLMs in research processes. 🗄️: [#medicalresearch #systematicreviews #LLMs #efficiency #ethics]

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