IntelliSOFT Consulting Ltd’s Post

Exploring the intersection of AI and public health has been a fascinating journey. AI tools such as machine learning algorithms and natural language processing have immense potential in revolutionising healthcare. From predictive analytics for disease outbreak detection to personalised treatment recommendations, AI can enhance decision-making and improve patient outcomes. One key area of exploration is the use of AI in analysing large-scale health data to identify trends, patterns, and risk factors, aiding in early intervention and preventive strategies. Additionally, AI-powered virtual assistants and chatbots are transforming healthcare delivery by providing round-the-clock support, answering queries, and even assisting in mental health management. However, ethical considerations around data privacy, algorithm bias, and equitable access to AI-driven healthcare solutions remain critical aspects that require careful attention and regulation for the responsible and beneficial integration of AI in public health. #AI #publichealth #learningneverstops

Attended the US National Academies Forum workshop today 'Diagnosis in the Era of Digital Health and Artificial Intelligence' was both interesting and insightful . Some key points that resonated with my work in digital health and AI: 1. Patients are increasingly turning to digital platforms for health information, with 1 billion daily health queries and doctors being the top profession on TikTok. 2. Diagnostic errors account for significant amount of medical errors, which sometimes results in patient harm, AI-supported decision-making may help reduce this. 3. The potential of natural language processing in capturing patient symptoms from EHRs, though challenges remain in truly capturing the patient experience. 4. The importance of addressing health equity in AI development, ensuring algorithms don't perpetuate existing disparities. As we advance in this field, it's crucial to focus on solving real clinical problems and ensuring equitable access to these technologies. #DiagnosticExcellence #HealthEquity #PatientEngagement #DigitalHealth #AIinHealthcare

GenAI in Chinese Healthcare China is leveraging AI, particularly large language models (LLMs), to enhance its primary healthcare system. Recent studies demonstrate LLMs' potential to improve both clinical competency and patient-provider communication in primary care settings. However, these innovations have primarily been tested in well-resourced urban areas, raising questions about their applicability in underserved regions. While AI shows promise, experts emphasize that technology alone cannot solve all primary healthcare challenges. Systemic changes, including provider incentives, community outreach, and clear policies on technology financing and regulation, are crucial. Further research is needed to assess LLMs' effectiveness in low-resource settings and their potential to address healthcare inequities. Read more at https://lnkd.in/gSUvcEZh - Amol Deshmukh| aud3@cornell.edu #artificialintelligence #workplaceAI #technology #work #labor #innovation #workplace #automation #neuralnetworks #LLMs #LargeLanguageModels #nlp #business #tech #law #legal #labor #workforce #ethics #aigovernance #healthcare #education

Happening this week: Join us for the Penn LDI conference, (Re)Writing the Future of Health Care With Generative AI! Discover how #AI is transforming clinical care and patient communication. 🤖 Learn more and register here: https://lnkd.in/epDHipiG

🎯 Artificial Intelligence, Taylor Swift and the US Presidential debate ⚠️ these are my thoughts - TLDR: responsible AI based on data integrity, privacy and accuracy is key in building trust in both clinicians and patients/users/communities. The recent U.S. presidential debate spotlighted #artificialintelligence as a critical issue. Taylor Swift was even “pushed” to voice her concerns about the potential for AI to spread #misinformation. As AI continues to reshape industries, these fears are particularly relevant in healthcare. 🫀 In medicine, accuracy of #data and truth of evidence are paramount. AI has immense potential to revolutionize clinical decision-making, enhance patient #care, and address longstanding #inequities, but it also raises critical questions: • 𝗗𝗮𝘁𝗮 𝗽𝗿𝗶𝘃𝗮𝗰𝘆: How can we safeguard sensitive health data from misuse or manipulation? • 𝗠𝗶𝘀𝗶𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻: If AI can distort facts, how do we prevent false or biased medical recommendations from influencing patient outcomes? • 𝗛𝗲𝗮𝗹𝘁𝗵 𝗲𝗾𝘂𝗶𝘁𝘆: How do we ensure AI helps bridge, not widen, disparities in care? 🔹Last year, our team presented our research at the #EACVI Congress in Barcelona, where we demonstrated AI’s potential to close race-based disparities in the timely management of severe aortic stenosis (AS) and mitral regurgitation (MR). We implemented a natural language processing (NLP) clinical decision support algorithm that automatically identifies severe AS/MR from #echocardiographic reports. It then sends a nudging notification to the referring clinician, recommending referral to the hospital’s Structural Heart Valve clinic. ❓The result? Improved access to timely, life-saving interventions for all patients, regardless of race. #AI is a powerful tool, but with great power comes great responsibility. How we implement it in #healthcare—ensuring trust, equity, and ethical oversight—will define its true impact. ⬇️📱Would love your thoughts on how we can leverage AI responsibly for a better, more equitable future in healthcare. #AIinHealthcare #DataPrivacy #HealthEquity #HealthcareInnovation #EthicalAI #medicine #cardiology #science #bioethics #trust #TaylorSwift

Are we ready for #GenAI therapists? The Stanford Institute for Human-Centered Artificial Intelligence (HAI) looks into it. More detailed research reports here: 🔹Large language models could change the future of behavioral healthcare: a proposal for responsible development and evaluation https://lnkd.in/efhrukfc 🔹Readiness for AI Deployment and Implementation (READI): A Proposed Framework for the Evaluation of AI-Mental Health Applications https://lnkd.in/eKX9rmHi

Stanford University recently released an interesting set of research showcasing how it has been making significant strides in exploring the applications of AI within psychotherapy. The research shows that there is immense potential for large language models (LLMs) to augment and even automate parts of the therapeutic process, such as note-taking and delivering therapy. However, the university also emphasizes the complexity and high stakes involved in psychotherapy, highlighting the need for cautious and responsible integration of AI. How to do this effectively is the essential question to answer today. Any process for evaluating AI readiness in clinical settings must focus on safety, confidentiality, equity, effectiveness, and implementation, ensuring that these technologies can truly benefit patients without compromising ethical standards. Our organization has focused deeply on understanding the responsible use of AI in various high-stakes environments, including government work. Our experience with 5G, telecom, Internet of Things, Digital Twin technologies and incorporating AI within those sectors demands that we understand the importance of robust security measures, data privacy, and transparency where one mistake can result in a catastrophe. For example, the recent Mirai botnet attack exploited IoT devices to launch massive DDoS attacks, highlighting the critical need for stringent security protocols MIMO is intimately familiar with. These principles are directly applicable to the integration of AI in psychotherapy. By ensuring that AI systems are secure, private, and transparent, we can help mitigate the risks associated with their use in mental health care, potentially improving accessibility and personalized treatment options for patients. Implementing techniques such as end-to-end encryption, federated learning, and zero-trust architectures can significantly enhance the security and privacy of AI systems in this sensitive domain. We believe that the government can play a pivotal role in shaping the future of AI in psychotherapy by collaborating with industry partners to ensure ethical and effective integration of AI technologies. Through public-private partnerships, the government can help develop standardized guidelines, secure data-sharing frameworks, and regulatory compliance to protect patient privacy and promote transparency. These collaborations can facilitate research on unbiased, personalized AI models and establish robust security measures, ultimately enhancing the accessibility and quality of mental health care services for the public. Thanks to Chris Kraft to share these resources. https://lnkd.in/e72N_rgx

🌟 Exciting Developments in AI & Healthcare 🌟 In our continuous journey towards innovation, we're witnessing the integration of technologies that challenge our understanding and push the boundaries of possibility. The advent of advanced large language models exemplifies this shift, showcasing an extraordinary breadth of capabilities - from enhancing our culinary experiences to advancing medical diagnostics. I find the rapid evolution of these technologies both exhilarating and thought-provoking. These models have transitioned from nascent tools to sophisticated systems capable of outperforming medical students in U.S. licensing exams within a mere three years. Their proficiency now spans texts, images, videos, sounds, and even molecular structures, transforming our approach to medicine and research.... The acceleration of AI capabilities brings to light the imperative need to address the emerging challenges they pose - from ethical dilemmas to the risk of misinformation. The recent RAISE (Responsible AI for Social and Ethical Healthcare) meeting, which saw experts from across the globe converge to discuss these topics, marks a significant step towards this goal. As we continue to explore the vast potential of AI in healthcare, we must remain vigilant and collaborative in our efforts to harness its benefits responsibly. I highly recommend reading the editorial “Policy in Progress — The Race to Frame AI in Health Care” by Isaac Kohane, MD, PhD, for an insightful exploration of these themes and the future of AI in healthcare: https://nejm.ai/48wIYQ9 #AIinHealthcare #Innovation #EthicalAI #DigitalHealth

And that’s a wrap for Day 1 of the #Healthcare #NLPSummit! It’s been a day full of insights from prominent #Healthcare NLP leaders:  - David Talby from John Snow Labs shared accuracy benchmarks of John Snow Labs’ Healthcare-GPT model, which is available through the medical chatbot platform, for three use cases - Yanshan Wang, PhD, FAMIA from the University of Pittsburgh discussed the pivotal role of cutting-edge technologies such as #GenerativeAI in accelerating #pharmaceutical innovation and enhancing patient outcomes. - Mike Schäkermann from Google introduced Articulate Medical Intelligence Explorer (AMIE), a research AI system based on a LLM and optimized for diagnostic reasoning and conversations - Dylan Wagenseller, Intermountain Health, and Yinxi Zhang, Databricks, explained the process that led them to reduce clinical document review time from 10 minutes to 3 minutes per document. - Nicoleta Economou, Duke University School of Medicine, and Mwisa Chisunka, John Snow Labs, explained the process of Developing Guidelines for Responsible Generative AI in Healthcare There’s still time for Day 2 on-demand videos. Register here and tune in tomorrow for more sessions and networking: nlpsummit.org #GenerativeAI #LargeLanguageModels #LLM #ResponsibleAI #DigitalTransformation #RAG #RetrivalAugmentedGeneration #HealthcareAI #ClinicalLLM #DigitalHealth #PromptEngineering #MedicalChatbot #MedicalGPT

I found the "RAG on FHIR: Using FHIR with Generative AI to Make Healthcare Less Opaque" session by Sam Schifman very interesting. Looking forward to Day 2!