Standing Committee of European Doctors (CPME)’s Post

This four-phase framework for AI system evaluation offers a comprehensive approach to assessing AI technologies in healthcare. It's designed to ensure that AI systems are safe, effective, and ethically sound. 1️⃣ Model design and purpose: This phase ensures the AI system is meticulously designed to meet specific healthcare challenges, seamlessly integrating into clinical workflows to enhance efficiency and patient care without disrupting existing processes. 2️⃣ Algorithmic validation: During this phase, the AI model undergoes rigorous testing against a wide array of datasets to assess its accuracy, reliability, and ability to generalize across diverse patient populations and settings, ensuring it performs consistently outside of its training environment. 3️⃣ Clinical validation: This critical phase involves conducting detailed clinical trials to rigorously evaluate the AI system's actual effectiveness and safety within a healthcare setting, ensuring it delivers measurable health benefits and improves patient outcomes. 4️⃣ Ongoing monitoring: Post-deployment, the AI system is continuously monitored to assess its performance in real-world clinical settings, ensuring it remains effective and safe over time, and making necessary adjustments based on evolving data and clinical practices. 🌐⇢ https://lnkd.in/d998MbmY 💡 From a clinical perspective, the deployment of AI in healthcare is not just about technological advancement; it's about significantly enhancing patient care quality and accessibility while addressing global health challenges. The outlined framework not only underscores the potential of AI to transform healthcare but also the importance of rigorous evaluation to realize this potential responsibly. ✅ Join our community of 29k curious minds! Subscribe to our newsletter for your front-row seat to the latest groundbreaking studies. Get started here: https://lnkd.in/eR7qichj.

Evaluating the training data for a model is a critical step that is often overlooked. #healthcare #AI #algorithm #data #dataintegrity #dataquality #quality

From a clinical standpoint, integrating AI into healthcare transcends mere technological progress; it profoundly amplifies the quality and availability of patient care while tackling worldwide health obstacles. This framework emphasizes not only AI's transformative capacity in healthcare but also the crucial need for thorough evaluation to ensure responsible realization of its potential.

<AI Generated> 3 Key Takeaways from "Healthcare AI's Elusive ROI" by Spencer Dorn - High Expectations, Limited Returns: Despite the promise of AI revolutionizing healthcare, achieving a positive return on investment (ROI) remains difficult. Many AI tools, especially in clinical settings, are still prone to errors and inefficiencies, preventing the expected financial gains. - Operational Hurdles: AI implementation often adds complexity rather than streamlining processes. For instance, automating tasks like documentation and patient communication sometimes increases the workload for healthcare providers, which paradoxically reduces productivity. - Incremental Gains Over Quick Wins: Healthcare organizations should temper expectations and focus on small, incremental improvements from AI rather than seeking revolutionary changes. AI may be more suited to administrative tasks like revenue cycle management, rather than high-stakes clinical applications for now. These insights remind us that while AI holds great potential, a long-term, cautious approach is necessary for realizing meaningful benefits in healthcare. </AI Generated> <Human> Interestingly, it missed the point of the article - that ROI is hard to quantify. Nothing in the AI summary was wrong; it just buried the lead. AI (and many other healthcare technologies) are hard to evaluate. Who is the user? Who is paying? How do they perceive ROI? As a patient, I'm not paying, at least not directly, for the AI, and it may or may not have value for me. Also, what is the expected time frame for ROI? It shouldn't be immediate. And lastly, what's the baseline you are comparing against? Is that accurate? Evaluating GenAI is interesting. They do really well on structured medical exams, which are the typical standard, but do less well in the "wild". Also, numerous studies evaluate mistakes by GenAI. What about the human mistakes? </Human> https://lnkd.in/g5pnvD5Z #roi #ai #llms #medical #healthcare

AI in Healthcare: Patients Have Spoken, Are We Listening? Healthcare organizations that are looking to integrate AI responsibly and effectively should take note. Patient attitudes towards AI in healthcare are crucial for acceptance. Here are 5 takeaways from a recent article on the topic: 1. 13,806 patients from 74 hospitals in 43 countries shared their views on AI in healthcare - the largest study of its kind! 2. 57.6% of patients have a positive attitude towards AI in healthcare, but trust varies widely depending on the application. 3. Patients strongly prefer explainable AI and physician-led decision-making, over either AI or physician-led decision-making. 4. Women and those with poorer health are less enthusiastic about AI in healthcare. 5. Lowest trust was in AI's accuracy in providing treatment response information. While the article doesn't provide a ranking of AI applications from most to least trusted, it's clear that patients generally prefer AI systems that: 1. Are explainable (can provide reasoning for their decisions) 2. Work alongside human physicians rather than independently 3. Are not solely responsible for treatment decisions As AI continues to change healthcare, understanding patient perspectives is crucial for successful implementation. What do you think we need to do to increase the trust in AI in healthcare?

Healthcare organizations could harness AI for various benefits, including improving quality of care, enhancing patient and staff experiences, accelerating research, and extracting more insights from data. They may also use AI to directly increase revenue by boosting volume, speeding throughput, increasing risk adjustment and service level coding, and improving revenue cycle management. Meanwhile, AI could cut costs by reducing staffing needs, decreasing staff turnover, and improving supply chain efficiency. As Goldman Sach's Head of Global Equity Research, Jim Covello, explained, "The substantial cost to develop and run AI technology means that AI applications must solve extremely complex and important problems for enterprises to earn an appropriate return on investment." But it may be too much to expect today's AI to solve complex and important healthcare problems. None of this is to say that AI in healthcare is worthless. AI can help make care more accessible, effective, and sustainable. Still, ROI pressures will intensify as AI's hype (and attendant FOMO) wears off. Love this analysis from Spencer Dorn always spot on when talking about intersection between AI & Healthcare ✅ #ai #healthcare #ROI https://lnkd.in/ebMjbuHf

Great to see that patients agree with us! At Pacmed we develop explainable AI to create a synergy between health care professional and computer. We provide our users with the underlying features that either contribute positively or negatively to the expected outcomes we predict. In line with the clinical reasoning of health care professionals on the ICU we cluster these features in organ systems to give a clear overview in one glance, but with the option to dive deeper into the underlying features if desired. We show trends in our predictions, to help interpretation further. We make the ICUs into a learning health care system where we know and monitor precisely how we go from raw data (both live and historical), to validated, standardized, harmonized and medically enriched information to clinical AI driven insights. We believe that multiple AI algorithms and other smart analytics should be brought together to clear overviews and insights on ward and patient level. We make health care professionals the focal point of all (and only all) information they need be supported in making the right decision. We give them more time and the right information to provide their patients with adequate and personalized care in full autonomy.

Generative AI: A New Era in Healthcare The healthcare sector is on the brink of a revolution, thanks to the advent of generative AI. This cutting-edge technology is predicted to reach a market value of $22 billion by 2032, as it offers unprecedented opportunities for personalization, efficiency, and innovation in healthcare services. Personalized Medicine and Clinical Support Generative AI is paving the way for personalized medicine, where treatments and healthcare plans are tailored to the individual's genetic makeup, lifestyle, and health history. This personalization extends to clinical decision support, where AI can assist in diagnosing and recommending treatment options, potentially reducing the risk of human error and improving patient outcomes. Operational Efficiency In the operational realm, generative AI is automating routine tasks, such as scheduling and patient communication, freeing up healthcare professionals to focus on more critical aspects of patient care. This efficiency is not just limited to administrative tasks but also extends to the generation of complex medical reports and research papers, streamlining the workflow in healthcare facilities. Challenges and Ethical Considerations Despite its potential, generative AI in healthcare is not without its challenges. Concerns around data privacy, security, and the ethical implications of AI decision-making are at the forefront of the debate1. As the technology advances, it is crucial to establish robust frameworks to address these issues, ensuring that generative AI serves as a beneficial tool for both healthcare providers and patients. The Future of Healthcare As generative AI continues to evolve, its integration into healthcare systems around the world is inevitable. From enhancing patient care to optimizing administrative processes, the potential benefits are vast. However, it is essential to navigate the ethical landscape carefully to fully realize the promise of generative AI in healthcare. In conclusion, generative AI holds the key to a more efficient, personalized, and innovative healthcare industry. As we move forward, it is imperative to harness its potential responsibly, ensuring that it contributes positively to the health and well-being of society.

85% of Healthcare Leaders Investing or Plan to Invest in Gen AI, According to the Largest Survey Reporting from the first day of #HLTHEurope in Amsterdam, it's clear that generative AI is a primary focus in healthcare! The Future Health Index (FHI) by Philips, a comprehensive study based on the insights of 3,000 healthcare leaders, is a significant resource. This extensive survey underscores the industry's increasing reliance on virtual care and AI advancements to combat pressing issues such as workforce shortages, financial constraints, and escalating service demand. Key highlights from the FHI 2024 report: 🔹 92% of healthcare leaders view automation as essential for addressing staff shortages by reducing administrative burdens. However, 65% acknowledge skepticism among healthcare staff about automation. 🔹 Nearly 89% have seen positive impacts from virtual care, including alleviating staff shortages, enhancing patient care delivery, and boosting staff satisfaction. 🔹 Generative AI is a hot topic, with 85% of leaders investing in or planning to invest in these technologies to improve clinical decision support and patient monitoring. 🔹 A significant 66% of healthcare leaders report a surge in burnout and mental health issues among their workforce, underscoring the urgent need for digital solutions to enhance efficiency and reduce workloads. This highlights the relevance of generative AI in healthcare. 🔹 Financial constraints impact patient care, with 81% of leaders noting challenges due to limited resources. Many are exploring subscription-based models for healthcare technology to mitigate upfront costs. Exciting times ahead for healthcare innovation! https://lnkd.in/eViWDAbu

Regard using AI to maximise the value from patient data Regard, a Los Angeles-based clinical decision software company, has secured $61 million in Series B funding to advance its AI-powered clinical-insights platform, scale its reach in healthcare, and invest in research on healthcare LLMs. Regard's tool utilizes AI to evaluate patient history, generate clinical decisions and documentation, and facilitate clinician-to-clinician communications. This is important because the World Economic Forum estimates that less than 3% of patient data is used. Unlocking the value in the extra 97% is a huge opportunity to improve healthcare outcomes for patients and support the clinicians. This is yet another example of the continued investment in AI for the health sector, and the innovations we can expect in the future. While not the only company doing this work, It's great to see Regard pushing the boundaries of what's possible with AI in healthcare. I can't wait to see what innovations they come up with next! Congratulations to Eli Ben-Joseph and the team at Regard on the success of the funding round. https://lnkd.in/ggD2NpX7