Stefan Niculescu

As enterprises grapple with AI strategy and emerging regulatory pressure, high-performing companies are establishing cross-functional AI councils to align AI use with enterprise strategy, values, and regulatory requirements. Recently one of our healthcare clients was approached to participate in a research partnership to build out a disease specific large language model - thanks to the establish risk management practices, they were able to ask the right questions and implement appropriate guardrails. In my most recent Forbes Technology Council article, I share: • Key roles to include on an AI council (hint: it's more than just tech experts) • Core responsibilities and deliverables • Real-world examples of successful AI councils • Common pitfalls to avoid and how to overcome them Whether you're just starting your AI journey or looking to level up your governance, AI councils are critical for staying competitive while mitigating risks. With incoming regulations like the EU AI Act, having a strong AI governance structure is no longer optional. Check out the full article below to learn how to build a successful AI council for your organization 👇 #AI #AIGovernance #ResponsibleAI #Innovation

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In the latest installment in our series on data innovation in healthcare, Jiby Varghese and Ryan Sousa explore the first half of solving the cloud equation. The authors cover: > The history of cloud computing and its applications in healthcare settings > Choosing a cloud provider and navigating security certifications, interoperability, and being HIPAA compliant > Building a "Cloud Castle" and optimizing for cultural fit 🔗 Read here: https://lnkd.in/gu2ynjKZ

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When applying AI in healthcare, its role must always be one of augmentation, not replacement. Our latest blog dives into how AI-enabled solutions, like our CognitiveML™ model, are revolutionizing healthcare revenue cycle management with a hybrid approach combining LLMs, NLP, and machine learning. At Iodine Software, our heritage in clinical AI technology runs deep. We have a proven track record of pioneering solutions that enhance financial performance in the mid-revenue cycle. Generative AI’s ability to consolidate clinical and financial information in real-time can generate unprecedented improvements across the health industry. 🔗 Read the full blog post here: https://shorturl.at/3wIYR #AI #healthtech #RevenueCycleManagement #HealthcareInnovation #HealthcareTech #GenAI

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Imran Qureshi, CTO at b.well, sheds light on the transformative power of AI in healthcare in an exclusive interview with Healthcare IT News. Explore how large language model AI, like ChatGPT, is revolutionizing patient care and clinician workflows. Imran shares valuable insights on how hospitals and health systems can leverage AI technologies to: - Improve patient care efficiency - Ease clinician workload - Empower patients to take charge of their health journey Discover how ChatGPT and other LLM-based technologies are reshaping healthcare delivery, from streamlining administrative tasks to enhancing patient engagement. Read the full interview here: https://lnkd.in/gG4UK4Vk #HealthTech #digitalhealth #healthIT

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AI and data go hand in hand, wouldn't you agree? On the podcast, Dimitry Tran, co-founder and CEO of Harrison.ai, highlights the crucial importance of health data – coining it "the new uranium." The delicate nature of health data means that once its primary use is complete, many are hesitant to utilise it further. However, AI cannot function without data. The key is to handle this data with caution, ensuring it can be refined into something that supports AI and drives healthcare innovation. 🎧 Listen to the full episode to hear Dimitry delve into the detail: https://lnkd.in/eq-f2DjD #heathtech #digitalhealth #thehealthtechpodcast #podcast

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The rise of AI in medicine is exemplified by #Google's #MedGemini. But the blend of optimism and skepticism is here. Can #AI truly match the nuanced understanding that doctors offer? Historically, people frustrated with conventional medical advice turned to "Doctor Google" often with mixed results. Today, AI tools like #ChatGPT are stepping into this arena. With again mixed results. Now they promise that "Med-Gemini destroys GPT's benchmark and outperforms doctors". It's critical to remember that AI is a tool — not a replacement for human expertise. As we marvel at AI's potential to analyze vast data sets and even engage in complex reasoning, the technology's current limitations must be acknowledged. AI can suggest, but not substantiate, it can propose, but not empathize. At TenderHut, we recognize the transformative potential of AI across industries, including healthcare. Our AI-powered healthcare solutions analyze medical results, generate alerts, and assist with bespoke dictionaries for illnesses, medications, and procedures. I agree that Med-Gemini, represents a significant leap forward in clinical diagnostics and has vast potential for real-world application. ➡ It is designed to handle various types of medical data, including images (such as X-rays and CT scans), genomics, and other relevant information. It excels in understanding these different modalities simultaneously. ➡ Unlike general-purpose AI models, Med-Gemini is specifically fine-tuned for medical use. It leverages its core capabilities from the Gemini family to reason about complex medical scenarios. This means it can provide more accurate and context-aware predictions. ➡ Med-Gemini-2D sets a new standard for AI-based CXR report generation. Expert evaluation shows that it outperforms previous results by a significant margin. For both normal and abnormal cases, a substantial percentage of AI-generated reports are considered equivalent to or better than radiologists’ reports. ➡ Med-Gemini-3D achieves the first-ever large multimodal model-based report generation for 3D CT volumes. While 53% of AI reports are considered clinically acceptable, further research is needed to match expert radiologist reporting quality. ➡ Med-Gemini-2D performs exceptionally well in CXR VQA tasks, surpassing state-of-the-art or baseline models in 17 out of 20 tasks. ➡ It also excels in image classification tasks related to histopathology, ophthalmology, and dermatology. It outperforms baselines across 18 out of 20 tasks. ➡ Med-Gemini-Polygenic improves disease risk prediction beyond the standard linear polygenic risk score-based approach. It even generalizes to genetically correlated diseases not explicitly trained for. This marks an exhilarating phase in #ClinicalDiagnostics. I believe that AI is indeed a superb advancement, but its role should be to enhance human capabilities, not to replace them. #HealthTech #MedicalAI #PatientCare #Technology #Healthcare

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#EmidsHCSummit Healthcare companies are navigating a delicate balance between innovation and regulation. As they harness the power of Generative AI, these organizations are committed to both exploring its vast potential and safeguarding patient data. During the Emids Healthcare Summit '23, we asked industry leaders about their strategies in this challenging landscape. Their responses revealed a thoughtful approach to leveraging technology while prioritizing privacy. Discover more insights from these discussions in the article below - https://lnkd.in/e__j-8dV

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Check out our latest blog post on how EHR integration can retrieve contextual patient information and improve recommendations from Gen AI: https://lnkd.in/g5RjmKiA GPT-4 for Information Retrieval and Comparison of Medical Oncology Guidelines By Ferber et al. had some great insights on how to improve recommendations from Gen AI. With contextual patient data the accuracy of recommendations went from 57% to 84%! https://lnkd.in/gD8nhTxi

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💡Scaling Generative AI: Technical Insights from Experience (Part 1) 🚀 As I've been working with customers on their Generative AI (GenAI) initiatives, I've noticed some familiar challenges surfacing - reminiscent of the early cloud adoption days. As enterprises are gearing up to launch their first few GenAI workloads in production, they want to get ahead of potential roadblocks for smooth scaling and adoption across lines of business (LoBs). Here are a few crucial considerations based on recent engagements: Robust Identity and Access Management (IAM) has surfaced a few times already. IAM is key in ensuring users can securely access only the necessary resources (LLMs, APIs, Vector Databases, Data, etc.) - most LoBs don't openly share data/assets across BU boundaries. Additionally, tracking and allocating resource consumption for each line of business (LOB) promotes fairness and manages costs across departments. Learning from the Cloud adoption days, organizations want the usual FinOps best practices on 'Metering Usage', 'Show Backs' and 'Charge Backs' sorted before mass adoption. Organizations are starting to realize the need early on for a flexible enterprise GenAI platform that supports both commercial and open-source Large Language Models (LLMs) - allows users to choose the best-suited models for their specific use cases, and selecting the most cost-performant LLM based on business needs is important. Who wants to pay $$$ for vacation policy lookup? Also, ability for the organization to evaluate LLMs based on performance, accuracy, and relevance is emerging as an essential capability for enterprise customers. This is because enterprises are already getting humbled by the various creative ways in which employees/LoBs are considering leveraging GenAI. So much variety warrants choice and flexibility! Enterprises want to be able to put necessary guardrails in place for what prompts can be sent to the LLM and what outcome can be shared back with the employees. As such, platform's capability to assessing the quality, ensuring safety of prompts, and adherence to corporate guidelines is critical for GenAI production launch. Likewise, implementing thorough testing and validation automation is surfacing as a core capability for path to production. To adhere with audit and compliance, organizations want to implement Large Language Model Operations (LLMOps) practices for model management, tuning, deployment, and monitoring streamlines operations. Stay tuned for Part 2 of this post, where I'll share additional technical insights that my customers are considering to help build a robust, scalable, and secure enterprise GenAI platform. 💬 Do share your GenAI experiences and technical learnings on the path to production in the comments below! 📢 Subscribe to my newsletter to get access to strategies and practical guidance on accelerating adoption of generative AI within your organization. Get started here: https://lnkd.in/g3bdneR7  #genai #ai #aws

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"Explore along with Nilesh Chandra, Stephen Carnall, Erik Moen, and Nori H. the best use of AI in healthcare registries for improved data management and patient outcomes. Discover more as this article drops next week! #HealthDataManagement #ClinicalData #AIinHealthcare #HealthcareTechnology #DataAnalytics

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While LLMs show immense potential in healthcare, there is a gap between its promise and actual application. As with any innovation journey, we must develop the necessary regulations and practices to close it. The piece below does an amazing job of highlighting the missing pieces to this AI puzzle. I think the biggest takeaway here is to not let our excitement get the best of us The longevity of these technologies will require a lot of thoughtful trial and error. #Healthcare #LLMs #AI

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This article raises an interesting question: Is hybrid cloud the answer to hosting hybrid large language model (aka your Generative AI estate, enriched by your own data and prompts)? Given the nature of large language models, any prompt used in a public cloud large language model (LLM) setting can conceivable become part of the next cycle of learning. This is a different concern than traditional privacy and security considerations - and can naturally lead to the question of how to best host your own LLM (expensive as that may be), whether specialized or not. Personally I am hopeful that the emergence of small language models will ultimately resolve the cost aspect, until then we may have to revisit our cloud hosting strategies. https://lnkd.in/gjPM6dae

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Ever wondered how distinct #LLMs can communicate effectively with one another while maintaining meaning? Explore the importance of semantic interoperability – and the complexities of achieving it – in #healthcareAI from IMO Health’s Amol B., a clinical informatics expert, in this on-demand webinar. Learn actionable strategies for optimizing LLMs and how clinically trained LLMs used for #NLP applications are crucial in securing accurate healthcare insights. Find out more here: https://bit.ly/46uf8ew #DataQuality #SemanticInteroperability #Providers

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Wearable tech in patient health management? Justin A., Fellow of the American College of Health Data Management explores the transformative potential in a forthcoming article! #HealthDataManagement #WearableTech #PatientCare #DigitalHealth #HealthTech. "

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🔥 Just walked out of the HMPS 2024 keynote on AI in healthcare and wow, the insights from Kaveh Safavi were 🤯. Obviously too much to share in one post, so let me start with the CRITiCAL need for AI literacy in your organization: 📚 **AI Literacy is a Must-Have**: We've reached a point where understanding AI is no longer optional. Every leader, every team member interacting with technology needs a solid grasp on AI basics to lead effectively and make informed decisions. 🏥 **Real-Life Impact**: We learned about the potential of a "technology quotient" program, ensuring every employee gets schooled in tech basics. It's not just about using tools but understanding them deeply to drive fear away and foster informed discussions. 🤖 **Drive Adoption, Enhance Performance**: When people understand AI, they adopt it more willingly, use it more effectively, and can even help others navigate it. Plus, a well-informed team is a performing team! 🌐 **Everyday AI Interaction**: AI is popping up in everyday tasks, so a baseline literacy isn’t just helpful; it’s critical to keep up with the pace of innovation. ✨ **Responsibility and Guidance**: With knowledge comes the power to guide responsible AI use within our organizations, ensuring tools are used effectively and ethically. Let’s make AI literacy a priority to harness the full potential of technology in healthcare , every sector, and beyond! 🌟 #AIAdoption #TechnologyLiteracy #DigitalTransformation #HMPS2024

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In this post, we explore how we can use generative AI to produce clinical notes from patient visits and enhance the overall quality of healthcare documentation. #DataScience #AI #ArtificialIntelligence https://hubs.li/Q02zk98R0

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