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Don't miss out on this insightful article in Calcalist כלכליסט, written by Eran Lerer from Shoni Health Ventures, about how AI is revolutionizing the field of digital health, and what will make Israel a global leader in the field.

Don't miss out on this insightful article in Calcalist כלכליסט, written by Eran Lerer from Shoni Health Ventures, about how AI is revolutionizing the field of digital health, and what will make Israel a global leader in the field.

ECG AI is proven to saves lives! We use AI every day at Clalit Innovation, to create disease prediction models, diagnosis tools and treatment recommendations. But, this week, a new study out of Taiwan showed, for the first time, that using ECG AI-based alerts to warn physicians about high-risk patients led to an astounding 31% drop in mortality and an even bigger drop in cardiac death. The trial showcased how by implementing AI-based ECG alert tools, mortality risks decreased significantly within a month. Prof. Ran Balicer, shares in this interview: "At Clalit Innovation, we are pioneers in integrating AI tools in medicine and have been doing so for over a decade, this is a momentous occasion. Our self-developed AI tools, such as the C-Pi platform, are already making a huge difference in hundreds of community clinics, significantly enhancing treatment quality. In fact, recent articles in prestigious scientific journals have highlighted how the C-PI tool improved disease identification by a 100 times!" This clinical trial serves as ultimate proof of the transformative power of AI in healthcare. It opens up new avenues for improving treatment quality and safety, allowing healthcare professionals to focus more on meaningful patient interaction. #HealthAI #AI #AIinHealth #healthcare #innovation #healthcareinnovation #AIresearch #AIclinicalresearch #healthcaretech #medtech #medicalinnovation #clinicalresearch

"Transparency means I can disagree with your conclusion, but still trust you." This was one (paraphrased) talking point made by Richard Cramer, Chief Strategist, Healthcare & Life Sciences at Informatica, during a panel at the #HIMSS24 AI forum that stuck with me. The point was that even if a result can be challenged, at least knowing the causal pathway to that outcome provides the context for stakeholders to make informed decisions on next steps. A key lesson for #AI development: Flawed output by a model can be fixed, but a lack of trust in the model could be even more damaging. #digitalhealth #machinelearning #GenAI #healthtechnology #digitalstrategy #healthcare #patientcare #digitaltransformation

📰 𝗗𝗮𝗶𝗹𝘆 𝗖𝗮𝗳𝗲́ 𝗡𝗲𝘄𝘀 📰 Your Top AI Stories for 10/13/24: 1️⃣ 𝗔𝗜 𝗮𝗻𝗱 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗼𝗻𝘀: The future of relationships is being shaped by AI companions, which challenge us to rethink companionship and ethical boundaries in the digital era. This exploration prompts important discussions about AI's role and its impact on social dynamics. 🤖 2️⃣ 𝗔𝗜 𝗶𝗻 𝗛𝗲𝗮𝗹𝘁𝗵𝗰𝗮𝗿𝗲: By customizing diabetes treatment plans, AI is poised to revolutionize patient care, leveraging vast data to address individual healthcare needs and improve outcomes, demonstrating AI's transformative potential in healthcare. 💉 3️⃣ 𝗦𝘆𝗻𝘁𝗵𝗲𝘁𝗶𝗰 𝗗𝗮𝘁𝗮 𝗗𝗶𝗹𝗲𝗺𝗺𝗮: Lying at the intersection of privacy and innovation, synthetic data promises immense value but also raises significant ethical issues. Its usage demands a delicate balance between technological advances and responsible oversight. 🌐 Dive deeper into this news at the AI Knowledge Café: https://lyl.ai/pgizdhee 👇Leave your thoughts or questions below👇 #AINews #AIFuture #AIHealthcareInnovation #SyntheticDataDebate #AiKnowledgeCafe

Despite efforts to expand #AI use to tackle #TB, AI adoption has been delayed in many endemic regions. One barrier to their implementation is constrained public health resources, which can make it difficult for TB care providing institutions to pay market price for useful technologies. To address this concern, #privatesector organizations can set up collaborative forums. One such initiative is the Friendship Data Trust, a program providing lower resourced institutions with access to free AI-powered tools in exchange for anonymized patient data. These types of win-win engagements allow physicians to improve case management and access leading technology. Explore how the private sector is overcoming challenges in the TB landscape: https://lnkd.in/dTjtcrwy #privatesector #stoptbpartnership #tuberculosis

QUOTE OF THE DAY: "Because a false security or expectation of assurance is as bad as or worse than no assurance, we feel obligated to question whether the approach toward trust-enabling health artificial intelligence (AI) discussed in the recent Special Communication with the proposed nationwide network of health AI assurance laboratories will be either feasible or scientifically valid. Regarding feasibility, consider that in the coming years AI will be developed and tested for hundreds to thousands of clinical decisions, spanning hundreds to thousands of diseases and subpopulations, multiplied by thousands of clinicians and sites, and multiplied by several hundred design and quality control checkpoints for each such model. The combined space is therefore in the many millions, and it is inconceivable that such an endeavor can be accomplished at speed by a finite number of laboratories. Regarding scientific validity, external evaluation of AI models just before and during clinical or enterprisewide deployment ignores the foundational need for rigorous AI method/tool development. Rigorous method and tools verification and validation, and model feasibility and early-stage usability and safety studies must precede evaluation of every AI model that is near clinical or other risk-sensitive application. Clinical-grade AI is no different than medications and devices where extensive preclinical research and development, with phase 1 and phase 2 trials, precede phase 3 and phase 4 trials. This omission can result in a tsunami of bad AI models that will overwhelm the proposed system of laboratories and could also result in good AI models being overlooked by the proposed laboratories." Response by William W. Stead, MD, and Constantin Aliferis, MD, MS, PhD, to: Shah NH, Halamka JD, Saria S, et al. Nationwide network of health AI assurance laboratories.   JAMA. 2024;331(3):245-249. doi:10.1001/jama.2023.26930 #AI4H #DigitalHealth #artificialintelligenceinhealthcare https://lnkd.in/dJGHgEGW

I'm interested to dig into this new publication from Jee Young Kim, PhD and team on a framework for healthcare delivery organizations to mitigate AI-related risk. The Health Equity Across the AI Lifecycle (HEAAL) framework has steps at each point in the product lifecycle to evaluate the bias associated with an #AI tool and then take steps to address, mitigate, or accept it. This type of rigor is critical! It shouldn't be lost that designing for health equity isn't just about the product or tool, but also about how it is implemented and used. So while frameworks like HEAAL are super important for anyone designing an AI tool, using one in isolation does not guarantee the product will support health equity. That said, it's exciting to have this new tool in the kit for improved design. Link to full text in the comments.

As providers explore the many beneficial opportunities that healthcare AI can offer, they also need to consider potential risks, like biases. Several factors can contribute to AI biases, like data, but there are proactive steps that can help address them.

As providers explore the many beneficial opportunities that healthcare AI can offer, they also need to consider potential risks, like biases. Several factors can contribute to AI biases, like data, but there are proactive steps that can help address them.