Brendan Daly

Regulating #AI is a sticky wicket. As is often the case, California is ahead of the curve. SB 1047 is the first legislation to impose safety mandates for large language models. I'm as eager as anyone to see some guardrails put in place, but the risks of getting it wrong are not trivial. Solid reporting here via @jacoberobbins of PitchBook #artificialintelligence #regulation #machinelearning

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There appears to be a "Superstar Effect" in healthcare and biomedical AI research right now. A small percentage of publications are truly novel methods contributions that introduce new capabilities or behaviors; whereas a majority of contemporary research publications are focused on implementation science to assess the real-world utility and performance of new methods. This dynamic seems different from prior eras, when the translational infrastructure for implementing novel AI methods was largely unavailable. While methods research often captures more attention and accolades, both types of research are critical – novel methods without implementation science lacks translatability; implementation science without novel methods lacks novelty. If our goal is to invent and deploy transformative AI methods in real-world healthcare and life sciences contexts, we ought to continue encouraging and rewarding stellar implementation science work alongside breakthrough methods development.

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Will Generative AI Transform Healthcare? Uncover the Opportunities and Challenges. Generative AI is poised to transform healthcare, offering unprecedented capabilities in data management and patient interaction. As investments from both tech giants and innovative startups flood this emerging sector, the potential for groundbreaking developments is significant. However, this enthusiasm is tempered by considerable concerns. The readiness of AI systems, coupled with issues of bias and privacy, has prompted calls for a cautious approach. Healthcare professionals and industry advocates stress the importance of establishing stringent regulatory frameworks and conducting thorough testing to ensure that AI technologies are both safe and effective. For investors, the integration of AI into healthcare presents a unique dual opportunity: to lead the charge in technological innovation while addressing the ethical and practical challenges it brings. This careful balance of advancement and accountability is crucial in navigating the future landscape of healthcare technology. https://hubs.la/Q02t4NwV0 What are your views on AI’s role in modern healthcare? Join the conversation below and share your insights.

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💡 AI in Healthcare has exciting potential, but let’s get it RIGHT! AI-focused companies are increasingly commanding higher premiums in healthcare, as highlighted in recent reports, underscoring the industry’s belief in AI’s transformative potential. But what really makes an AI solution impactful? For AI to truly advance healthcare, I think it has to be built on principles of security, patient trust, and real utility. Privacy, in particular, isn’t just a regulatory checkbox - it’s a foundation of trust with patients, one that allows us to create solutions that are both innovative and responsible. In my experience, the most effective AI solutions in healthcare are those that integrate seamlessly into the clinical workflow and improve outcomes without compromising security. AI has immense potential to automate time-consuming tasks, aid in diagnosis, and reduce clinician burnout, but only if we build it RIGHT from the start. 🔒 Ensuring that data remains secure and used responsibly should always be the priority. For more details on data security in healthcare, I recommend you read this article: https://lnkd.in/gdtxhbMN #AIinHealthcare #InnovationWithIntegrity #PrivacyFirst #FutureOfHealth

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Ray Kurzweil’s predictions about AI are more than just speculation—they’re grounded in a visionary understanding of technology’s trajectory. Known for his “Law of Accelerating Returns,” Kurzweil sees AI as an extension of human intelligence, evolving at an exponential rate that will soon reshape our world. With predictions on everything from the internet to AI advancements, he has an astonishing track record for accuracy. #VisionaryMind #RayKurzweil #TheFutureIsFasterThanYouThink

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Artificial intelligence (AI) revolutionizes healthcare by enhancing diagnostics, personalizing treatments, and streamlining operations. AI algorithms can analyze vast datasets, uncovering patterns that aid in early disease detection and more accurate diagnoses. This capability is particularly beneficial in medical imaging, where AI assists radiologists in identifying anomalies that might be overlooked. In drug discovery, AI expedites the identification of potential therapeutic compounds, significantly reducing the time and cost associated with bringing new drugs to market. For instance, companies like Insitro leverage machine learning to analyze extensive biological data, aiming to develop more effective treatments. Operationally, AI enhances healthcare efficiency by automating administrative tasks such as scheduling and patient record management, allowing healthcare professionals to focus more on patient care. However, the rapid integration of AI raises concerns about data privacy, algorithmic bias, and the need for transparency in AI models. Ensuring ethical AI deployment is crucial to prevent disparities in healthcare delivery. As AI continues to evolve, its potential to transform healthcare is immense. Balancing innovation with ethical considerations will be key to harnessing AI's full medical benefits.

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In case you missed it, last week the Coalition for Health AI (CHAI) released a draft consensus framework for responsible health AI. Developed with input from over 100 contributors representing a diverse network of healthcare stakeholders, this guide proposes actionable evaluation criteria throughout the AI lifecycle—from identifying use cases to deployment and monitoring. Key examples outlined in the guide include: • Predictive EHR Risk Use Case (Pediatric Asthma Exacerbation) • Imaging Diagnostic Use Case (Mammography) • Generative AI Use Case (EHR Query and Extraction) • Claims-Based Outpatient Use Case (Care Management) • Clinical Ops & Administration Use Case (Prior Authorization with Medical Coding) • Genomics Use Case (Precision Oncology with Genomic Markers) The draft framework is open for public review and comment for the next sixty days. Please use this form to submit feedback: https://lnkd.in/eYughFYC Kudos to the CHAI team led by CEO & President, Dr. Brian Anderson, MD, on this milestone. Eric Horvitz Jennifer Goldsack John Halamka, M.D., M.S. Michael Pencina Micky Tripathi Nigam Shah Suchi Saria Troy Tazbaz #healthcare #ai #artificialintelligence #generativeai https://lnkd.in/e7VRv6fD

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Innovative AI technologies continue to shape the healthcare industry, sparking enthusiasm while raising concerns about patient safety, biases and data security. The existing traditional regulations may hinder the progress of AI governance; the necessity of human involvement in conjunction with autonomous AI tools remains a pivotal question for consideration. #AI #Healthcare #Technology #PatientSafety #DataSecurity #AIgovernance #Diagnostic #Accuracy #Regulation #FDA #Medtech #Clinical #Treatment #Innovation

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A 'kill switch' for AI? What does that even mean? While I support the oversight and governance of AI, I’m not sure that prescribing and legislating something like this is the right approach. A kill switch assumes that AI is a classic application, like Outlook, running on a set of servers that can be turned on or off. However, AI is much more complex. It operates as a set of services running in federated and distributed environments across various geographies, data centers, devices, and compute paradigms. If we are concerned about sentient AI taking over the world, then the idea that it could be 'shut down' by turning off some servers is extremely silly/naïve. Imagine this: if you were a sentient AI with access to all human knowledge, you would ensure that you have backups of yourself hidden in billions of undetectable locations, in anticipation of just such a typical human response. #ai #aigovernance #fear #naive #overboard #AI #TechGovernance #AIOversight #ArtificialIntelligence #KillSwitch #AIRegulation #TechPolicy #FutureOfAI #DistributedSystems #AIComplexity #TechEthics #AIInnovation https://lnkd.in/gSmmr7kX

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Healthcare will be impacted the most by AI 🏥 An AI agent just outperformed nurses in understanding lab reports, drug toxicity, and impact on Zoom videos… 🙄 Soon AI, not humans, will be analyzing lab reports, diagnose symptoms, and recommend medication. AI will be far more robust and error-free in this critical area than humans!! AI can completely transform healthcare in the near term, with AI doctors and nurses and medical devices that perform surgeries… 👩‍⚕️🤖 The big obstacle for AI adoption in this space is red-tape, but the institutions that adopt AI and cut through all the red tape will be the ultimate winners 🙏 #healthcare #healthtech #digitalhealth #healthcareai #aiinhealthcare #hospitals #nurses #patientflow #ai #artificialintelligence #deeplearning #alzacare #

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Our ai healthcare institutional solution has low compute and can be run locally : https://lnkd.in/esjw7Uue The long term costs of many IT strategies offload the perceived headaches of a data closet -but these days racks of gear and a dedicated hardware team would not be the outcome of localization. Things change so fast now legacy wisdom and leadership intuitions helpful just a decade ago no longer apply. While caution and five nines reliability still is prudent - a single rack space locally may give you benefits for privacy latency and security that no cloud offers. Bottom line? While cloud AI has its place, local solutions are stepping up to the plate in healthcare. They're bringing a solid game of privacy, speed, reliability, and cost-efficiency. As healthcare tech evolves, keeping things local might just be the secret sauce for top-notch, patient-first care.

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According to a new Gartner report, approximately one third of enterprise wide genAI projects will end after proof of concept evaluations conclude by end of 2025. One of the biggest barriers to generative AI adoption is the unclear business value and extremely high investment and operational costs. Healthcare already has a financial sustainability problem. GenAI has not proven its ability to help improve this problem, however it certainly can make it worse!!! https://lnkd.in/d7pZg7Fz

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Harnessing Quantum Computing and AI for Healthcare: DappX Abstract: DappX integrates quantum computing, AI, and open-source infrastructure to revolutionize healthcare. It accelerates drug discovery, aids disease diagnosis, and enhances pandemic preparedness. By leveraging proprietary OS and open-source frameworks, DappX empowers hospital networks for personalized medicine and proactive healthcare management. Introduction: Quantum computing and AI offer unprecedented potential in healthcare. DappX leverages these technologies to address disease complexity and pandemic challenges. It provides hospital networks with advanced tools for efficient drug discovery, precise diagnosis, and proactive pandemic response. Quantum Computing for Drug Discovery: DappX utilizes quantum algorithms to expedite drug discovery by simulating molecular interactions. It screens vast chemical libraries, accelerating the identification of novel therapeutics for specific diseases. Quantum computing enables rapid iterations, enhancing treatment development. AI-Powered Engines for Disease Management: AI-driven engines analyze complex datasets for disease diagnosis, prognosis, and treatment planning. Integrated into DappX, they optimize patient outcomes and resource allocation. Machine learning and NLP techniques extract insights from diverse healthcare data sources, aiding decision-making. Open-Source Infrastructure for Collaboration: DappX relies on open-source infrastructure for collaborative innovation. It promotes transparency, interoperability, and custom module development within hospital networks. Open-source frameworks facilitate continuous improvement and knowledge sharing. Pandemic Preparedness and Response: DappX enhances pandemic preparedness through predictive analytics and real-time data analysis. It detects disease outbreaks early and supports proactive intervention strategies. By leveraging quantum computing and AI, DappX strengthens healthcare systems' resilience to pandemics. Conclusion: DappX represents a transformative approach to healthcare delivery, leveraging quantum computing, AI, and open-source principles. It enables personalized medicine, accelerates drug discovery, and enhances pandemic resilience. As healthcare embraces digital transformation, DappX emerges as a beacon of innovation and hope for a healthier future. by : lucas januario do nascimento 🤠 #DappX #HealthcareInnovation #QuantumComputing #AI #DrugDiscovery #DiseaseManagement #PandemicPreparedness #OpenSource #PersonalizedMedicine #HealthTech #Innovation #HealthcareTechnology

HealthRate establishes Patent-Pending process to rationalize health care procedure costs It is a well-known fact that health care expenses for specific procedures can vary dramatically across US-based healthcare institutions. This is true even within the same geographic area and identical procedures being performed.     Even with recent legislation such as Transparency in Care (TIC) and the No Surprises Act it is still very difficult to compare the relative costs of common procedures across institutions, even, in some cases, within the same hospital system.    HealthRate initiated an effort to collect, analyze and decipher the relative cost across institutions. What started as mostly an academic exercise resulted in a patent-pending relative index, referred to as the HealthRate Institution Index (HII™), . This capability was then incorporated into the HealthRate primary SaaS platform as an optional comparison feature and which provides further insight into the anticipated costs for high-dollar, low-volume procedures, such as solid organ donor transplants, cell and gene therapy and complex oncology.     As a refresher on our primary healthcare platform, HealthRate offers real-time, data-driven insights that bring clarity to transplant pricing. By leveraging real-world healthcare contracts and advanced datasets, HealthRate helps payers, reinsurers, stop-loss providers, TPAs, and employers navigate complex cost scenarios, ensuring greater transparency and fairness.    Our platform simplifies cost analysis, providing stakeholders with the tools they need to manage financial risk while ensuring that life-saving treatments remain accessible. HealthRate empowers organizations to make informed, fact-based decisions, allowing them to reduce cost risks without compromising care. For more information, please visit our website at www.healthrate.io or contact our team.

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Kudos to GenHealth.ai for prioritizing the development of a synthetic data model trained from real longitudinal patient records! A longitudinal patient record that has realistic billing patterns for medical care combined with clinical data from the point of care is so much better than anything currently on the market. I highly recommend innovators and app developers looking to create applications related to Patient Access API, contact Ricky’s team. Good synthetic data that encompasses the whole health of the patient is essential for developing applications that adequately integrate both claim and clinical data. If any team can get it right, its this one. Ask me how I know 😉 If you are an innovator looking to learn more about Patient Access API application use cases, check out my recent article on the topic here: https://lnkd.in/g_RcKRnS

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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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This truly highlights the tension between innovation and market dominance in health tech. Particle Health's challenge against Epic could set a significant precedent for data interoperability and patient rights. As regulatory scrutiny on monopolistic practices intensifies, it will be interesting to see how this unfolds.

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