Sukhmeet Panesar

Thank you for sharing this insightful research, Dario Heymann, PhD. The increasing role of Big Tech in Digital Health is undeniable and, as your analysis highlights, their resources and technological capabilities are instrumental in driving innovation forward. The impressive growth rate of partnerships, particularly with a CAGR of 32%, underscores the strategic importance of these collaborations. In my experience, the synergy between Big Tech and Digital Health startups fosters a dynamic environment where advanced technologies like AI, machine learning, and blockchain can be seamlessly integrated into healthcare solutions. This not only enhances healthcare delivery but also aligns with patient-centric values and stringent regulatory requirements. Moreover, the shift of Big Tech overtaking Biopharma in partnership share is a clear indicator of the evolving landscape. It’s essential for startups, academic institutions, and healthcare organizations to leverage these partnerships to stay at the forefront of innovation. #eHealth #HealthTech #DigitalHealth

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ECOSYSTEM VISUALIZATION OF THE DIGITAL HEALTHCARE INDUSTRY ⬇ ▫️Market segments have been depicted as actors for better readability. ▫️The grey rectangles around multiple market segments represent generic roles. ▫️To further improve readability and reduce complexity traditional generic roles have been omitted (manufacturer, purchaser, investors and consultants, political and humanitarian groups, researchers, and regulators) as well as the value streams between cloud service provider and information platforms, fiscal and market intermediaries, and services for remote and on-demand healthcare. ✍ Hermes, S., Riasanow, T., Clemons, E.K. et al. The digital transformation of the healthcare industry: exploring the rise of emerging platform ecosystems and their influence on the role of patients. Bus Res 13, 1033–1069 (2020). DOI: 10.1007/s40685-020-00125-x

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The entire NHS healthcare bidding system is #fraudulant - pre allocated bid application means no transparency in public fund allocation. Time to change from imperialist health to circular health economics. Using AI power.

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In our hospital we deployed AI using a PDSA framework similar to this, but this infographic also comes with a link to published manuscripts, which is very handy. Lunit Cancer Screening DeepTek CARPL - Radiology AI Platform Synapxe

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This year I spent some time studying the impact of AI in Healthcare and how it can play a role particularly in emerging markets where resources are scarce and biases remain a challenge. By offering scalable and affordable solutions, AI enhances accessibility to quality medical care, overcoming geographical and socio-economic barriers. AI supports early disease detection and personalized treatments, crucial for managing non-communicable diseases and improving long-term patient outcomes. It also helps reduce healthcare and environmental impact by treating diseases at an earlier stage. Innovations such as telemedicine, mobile health apps, and digital health records break traditional barriers, empowering individuals and streamlining healthcare processes. In Africa, initiatives like Medsol AI Solutions' Wi-Fi-enabled ultrasound probes and AI remote cardiac diagnosis demonstrate the transformative power of AI in reducing mortality rates and improving access to care. Embracing AI and healthcare technology is crucial for building a future where quality healthcare is a fundamental human right. I am curious, what pragmatic AI solutions have you seen in emerging markets that have helped improve patient outcomes? #africa #healthcare #AI #accesstocare #emergingmarkets

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Aligning AI with Clinical Needs: Insights from Global Medical Associations A recent OECD survey, conducted with 18 medical associations globally, sheds light on the perspectives of healthcare providers regarding AI. Here are some key takeaways: link to article: https://lnkd.in/geHcPum5 1️⃣ Benefits Outweigh Risks: 72% of surveyed medical associations believe that AI’s benefits in healthcare outweigh its risks. They anticipate AI will improve care quality, enhance patient experiences, and reduce workforce burnout. However, ethical concerns and liability remain critical hurdles, with 94% expressing concerns about ethical challenges. 2️⃣ Data Accessibility is Key: The biggest barriers to AI implementation include challenges in accessing high-quality health data, ensuring interoperability, and addressing weak digital foundations. These must be addressed to unlock AI's full potential. 3️⃣ AI as an Augmentation Tool: The majority (70%) agreed that AI would dramatically change medicine but reaffirmed that physicians will remain central to care delivery. The role of AI is seen as an enabler, not a replacer. 4️⃣ Clinician Involvement is Critical: Many respondents emphasized that clinicians must be embedded in the design, development, and deployment of AI tools. Currently, fewer than 25% of medical associations are involved in designing the AI solutions they are expected to use—a significant gap that needs bridging. 5️⃣ Education and Training: There is unanimous agreement on the need for robust education and training for clinicians to build AI competency, ensuring they can effectively and responsibly use these tools in practice. This report highlights the path forward for AI in healthcare: involve clinicians early, address barriers like data and digital literacy, and establish ethical and regulatory safeguards. It’s not just about adopting AI; it’s about doing so in a way that aligns with the needs of both providers and patients. I see these findings as a call to action for greater collaboration, innovation, and advocacy. What are your thoughts on the evolving role of AI in healthcare?

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Increased and better targeted #digitalhealthinvestment helps improve health outcomes, increase operational efficiency, enable sustainable and scalable solutions, and increase return on investments! Transform Health and partners are calling for action at #WHA77 to optimise #digitalhealthinvestment by: 💪Prioritising digital health investment as part of national plans and strategies to strengthen health systems and deliver UHC. 📝Improving the classification and tracking of digital health investment as part of existing mechanisms and UHC reporting, including through a co-designed digital health investment taxonomy and other tools. Learn more about Transform Health's key asks at #WHA77: https://buff.ly/3wkH9cn #HealthForAll #DigitalHealth #UHC Ministry of Health - Sri Lanka Ministry of Health of the Republic of Indonesia Ministry of Health, Maldives Secretaría General del Sistema de la Integración Centroamericana (SG-SICA) OECD - OCDE Health Data Collaborative Africa CDC Fondation Botnar AeHIN RECAINSA The Pan African Health Informatics Association (HELINA) International Telecommunication Union health.enabled White Ribbon Alliance Speak Up Africa Young Experts: Tech for Health PATH Digital Square at PATH 

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𝗛𝗼𝘄 𝗦𝗵𝗼𝘂𝗹𝗱 𝗪𝗲 𝗥𝗲𝗴𝘂𝗹𝗮𝘁𝗲 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗶𝗻 𝗛𝗲𝗮𝗹𝘁𝗵𝗰𝗮𝗿𝗲? 🏥💡 As generative AI technologies like #ChatGPT and others rapidly advance, regulators face a unique challenge in adapting traditional frameworks for healthcare. Current FDA regulations, designed for specific drugs and devices, fall short when applied to AI models with vast and evolving capabilities. A novel approach is needed — one that treats these AI systems more like human clinicians, requiring rigorous training, supervision, and periodic reassessment. While challenges such as AI "hallucinations" and transparency in training data remain, a thoughtful regulatory framework can ensure these technologies safely enhance clinical practice. I was recently invited to deliver three keynotes during the 82nd International Pharmaceutical Federation (FIP) World Congress of Pharmacy and Pharmaceutical Sciences 2024 in Cape Town, South Africa from 1-4 September. During these sessions, I emphasized that we need a completely new approach and framework to regulate AI in healthcare — fully aligned with these insights. The future of #Healthcare and #HealthTech depends on it. 🚀 Special thanks to my friend Prinesh Reddy for sharing that article with me 🙏 #AIinHealthcare #GenerativeAI #DigitalHealth #Innovation #FIP2024 #FIPCongress Lars-Åke Söderlund (Exec MBA) Vice President Harvey Castro, MD, MBA. Professor Shafi Ahmed Dylan Attard MD, MRCSI, MEnt. Alex Diaconu Dr. Martha Boeckenfeld Dr. Khulood Alsayegh Dr. Mariam Mohamed Fatima Matar MD Phd Dr. Meshari F. Alwashmi Fady Hannah-Shmouni, MD FRCPC Xiaochen Z. Ricardo Moraes Virgilio Gibbon Ana Araujo Hassan Chaudhury HonFAPM Vijay K. Luthra Pilar Fernandez Hermida Jijo James Rémy LEVASTRE

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Decentralized HealthData Etheros HealthData Foundation 100 key terms easily explained No. 36 #OHDSI The Observational Health Data Sciences and Informatics (or OHDSI, pronounced "Odyssey") program is a multi-stakeholder, interdisciplinary collaborative to bring out the value of health data through large-scale analytics. This initiative brings together a diverse group of stakeholders, including researchers and observational health databases, to generate real-world evidence that can improve health decisions and care. OHDSI's solutions are open-source, and the program is coordinated by a central center at Columbia University. OHDSI https://www.ohdsi.org/

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It's great to see the growing focus on responsible AI use in healthcare, especially for Low and Middle-Income Countries (LMICs)! This addresses the long spoken about need for innovative solutions in resource-limited settings. So what are we seeing? - For Frontline Workers: 🩺 AI-powered diagnostic tools that could enhance accuracy and speed, a clear need in areas with limited specialist access. 📊 Predictive analytics that might help prioritize high-risk patients, optimizing our scarce resources. 🎓 AI-driven training modules that could provide continuous education, addressing the ongoing challenge of keeping skills current. -For Health Policy Makers: 📈 Data-driven insights that could inform evidence-based policies, maximizing impact of limited budgets - a persistent issue in LMICs. 🗺️ AI tools that might assist in resource allocation, potentially solving the long-standing challenge of equitable healthcare distribution. 🦠 Predictive modeling that could enhance epidemic preparedness, addressing a critical need highlighted by recent global events. Despite these opportunities the perceived lack of uptake/representation is often masked by real-world challenges, including: Limited compute power and infrastructure. Low AI literacy among end users. Financial constraints. Need for culturally appropriate and context-specific solutions. Data privacy and security concerns in resource-limited settings. The potential is immense, but the journey requires careful navigation. By embracing AI responsibly, we can bridge healthcare gaps and move closer to achieving universal health coverage in LMICs. What are your thoughts on addressing these LMIC-specific challenges in AI adoption for healthcare? #AIInHealthcare #GlobalHealth #ResponsibleAI #HealthEquity #LMICHealthcare

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🎙️A wide ranging discussion in the The Economist babbage podcast covering a wide range of healthcare benefits and concerns in healthcare artificial intelligence dealing with types of models including neural networks, generative AI, validation, monitoring and medical device regulation led by Alok Jha and Natasha Loder with contributions from myself, Pranav Rajpurkar, Peter Kecskemethy and the inimitable Dr Hugh Harvey I've always wondered how to pronounce Peter's surname, I think that's been nailed here 😊 #AI #healthcare #generativeAI https://lnkd.in/eJ__H_e6

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My latest for Forbes: OpenAI’s latest venture, #SearchGPT, and its impact on healthcare I take a look at the differences between & progress made from #ChatGPT, which is one of the most widely used consumer platforms. However, the internet search “game” is still largely dominated by industry veterans— especially Google, which has optimized this arena for nearly two decades. Microsoft’s Bing has also grown its market share. As for healthcare, there are a lot of potential opportunities— and with them, numerous risks. Whether this will be a blessing or a burden will largely be dependent on the privacy, safety and accuracy measures put into place. As always, keen to hear your thoughts & feedback! #digital #ai #healthcare #xai #tesla #nvidia #gpu #innovation #economy #inflation #GenAI #LLM #FoundationModel #compute

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Well this is interesting- a small, narrow study that cannot and should not lead to a definitive conclusion that ChatGPT should be relied on solely on accuracy of diagnosis. And yet…the most fascinating finding is in relation to the group of physicians who used ChatGPT to support diagnosis - it did not in all cases, change their initial diagnosis even where ChatGPT was generating greater accuracy. There will be a lot more studies coming down the pipe in the coming months… #AI #healthcare #digitalhealth

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What does successful implementation of innovation entail? : Insights into Implementing Machine Learning in Healthcare A recent systematic review of 34 empirical studies sheds light on critical factors and strategies that can guide successful implementation. Link to study: https://buff.ly/4fIKBii Key Observations: Where and How: 85% of ML applications were implemented in hospitals, primarily supporting prognosis (59%) and diagnosis (29%). Technology and Innovation: Applications designed with user-centric principles saw higher adoption rates (44%). Demonstrating clear advantages over traditional methods (41%) and minimizing complexity (41%) were pivotal for success. Implementation Factors by Domain: 1. Inner Setting: Access to knowledge and training (35%): Organizations with robust educational frameworks enabled better integration. Strong IT infrastructure (32%): Without scalable systems, even the best ML models falter. Culture of innovation (26%): Forward-thinking organizational culture promotes early adoption. 2. Processes and People: Stakeholder engagement (35%): Involving clinicians, IT specialists, and administrators early is non-negotiable. Continuous reflection and evaluation (32%): Implementation is not static; feedback loops are essential. Presence of leaders (26%): Champions within the organization play a critical role in navigating barriers. 3. Cross-Cutting Themes: Trust and transparency: Clinicians demand explainability in ML models to align with evidence-based practices. Ethical considerations: Ensuring fairness and addressing biases in algorithms is imperative. Data governance: Success hinges on robust data-sharing protocols while respecting patient privacy. ML adoption in healthcare isn’t just about deploying algorithms; it’s about transforming systems. Implementation frameworks like the Consolidated Framework for Implementation Research (CFIR) reveal that ML-specific challenges—like the "black box" effect—often intersect with broader barriers common to digital health technologies. Successful implementation requires: Tailored Strategies: Adapt approaches to the unique organizational and clinical contexts. Interdisciplinary Collaboration: Bring together clinicians, IT experts, and decision-makers. Iterative Processes: Implementation isn’t one-and-done; it evolves with continuous learning and adaptation.

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#AI in healthcare Assurance Labs: Who should make sure #AI is appropriate for clinical use? A letter written by members of Congress raises concerns about recent restructuring in the ONC and its expanded role in overseeing AI and health information technology. This letter reflects significant concerns about the governance of AI technologies in healthcare, emphasizing the need for clear authority, fair practices, and safeguards against market domination by large corporations. Based on the points below- who should be gatekeepers of this technology? For clinicians using #AI technologies I think it should be clinical representatives with significant input from medical societies. Medical societies provide guidelines for clinical management of diseases and #AI applications to improve diagnosis or management are no different. Key points from the document include: 1. Restructuring and Expanded Role of ONC: The ONC has been restructured and now operates as the Office of the Assistant Secretary for Technology Policy/ONC (ASTP/ONC). The ONC's expanded role includes oversight of AI strategy across HHS, raising questions about its statutory authority and coordination with other agencies. 2. Concerns About Assurance Labs: The proposed creation of "assurance labs" for evaluating AI tools is criticized for potential conflicts of interest. The labs could involve companies reviewing tools while competing in the same market. Fee-based models for these labs are seen as barriers to smaller companies and startups, which may discourage innovation. 3. Criticism of Coalition for Health AI (CHAI): CHAI's fee-based model for membership and lab reviews is highlighted as problematic, with concerns about regulatory capture and market consolidation. Questions are raised about conflicts of interest stemming from the ONC’s prior association with CHAI. 4. Questions for Clarification: The letter asks detailed questions about the ONC's new role, statutory authority, and justification for its expanded duties. It seeks assurances about how smaller companies will be protected from the financial burden of assurance labs and how conflicts of interest will be avoided. Concerns about data security, costs to taxpayers, and the potential bureaucratic slowdowns in innovation are also expressed. 5. Congressional Oversight and Accountability: The authors request transparency, accountability, and innovation-friendly policies in shaping regulatory frameworks. A December 20th, 2024, deadline is set for responses to their inquiries. Ami Bhatt, MD Samuel R. Browd, M.D., Ph.D. Ivy Lee Donald Palmisano, Jr., J.D., CAE American Heart Association American Society for Gastrointestinal Endoscopy (ASGE) American Academy of Dermatology College of American Pathologists (CAP)

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AI-powered workforce optimisation, data integration and #Accessibility in healthcare systems and digital therapeutics for #MentalHealth will be among key health tech trends of 2025, according to Dr Anas Nader of Patchwork Health, Manjul Rathee of BFB Labs, Julia Hawkins of LocalGlobe and Heiko Claussen of Aspen Technology: https://lnkd.in/etHnK27g

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