Biswajeet Sahu’s Post

Healthy datasets are the cornerstone of effective AI initiatives - Healthcare IT news #Introduction Healthcare IT professionals must prioritize the quality and integrity of datasets for successful AI initiatives in the industry. #Challenges in healthcare datasets Issues such as data silos, lack of interoperability, and data quality hinder the effectiveness of AI initiatives in healthcare. #Strategies for improving datasets Implementing data governance policies, utilizing data standardization tools, and fostering collaboration among stakeholders are key strategies for enhancing healthcare datasets. #Benefits of healthy datasets High-quality datasets lead to more accurate AI algorithms, improved clinical decision-making, and better patient outcomes in healthcare settings. #Conclusion Healthcare IT professionals play a crucial role in ensuring the quality and integrity of datasets to drive successful AI initiatives in the industry. #HealthcareIT #AIinit ai.mediformatica.com #data #health #healthcare #healthsystems #learning #news #artificialintelligence #digital #healthcareit #intelligence #medicine #penn #digitalhealth #healthit #healthtech #healthcaretechnology @MediFormatica (https://buff.ly/3x8X517)

AI in healthcare depends on data, but sharing data is a challenge.   🩻 AI in medical imaging is enabling earlier diagnoses[1], reducing workload[2], and saving radiologists time[3]. These algorithms require training and validation on large and diverse datasets to ensure quality and reduce bias[4]. However, sourcing this data is challenging as it requires data sharing from complex organizations such as hospital systems.    🏥 Why is data sharing a challenge? A recent study in JAMA[5] concluded that motivation and capabilities impact data sharing. Frontier Economics[6] research identified several common issues for data sharing, including lack of incentives and knowledge, risks (commercial, reputational, ethical, regulatory, and legal), and costs.    1️⃣ People need the motivation to want to share data.  A lack of incentives and misalignment with organizational values drives data-sharing hesitancy. Reciprocal arrangements and financial incentives can help, but fundamentally, the reason for data sharing needs to align with an organization's mission.    2️⃣ Organizations need sufficient capabilities.  Robust infrastructure, governance, and resources are needed to effectively share data, especially since data can be pulled from multiple sites with differing formats. Curating this into something usable for AI development takes a wealth of resources. Lack of knowledge of the data's potential and the processes needed hinders data sharing. All parties require the expertise and systems to enable a smooth data-sharing relationship.   3️⃣ Risks need addressing and mitigating.  The prospect of the risks involved in data sharing can inhibit an organization's appetite. Some key risks that need consideration and mitigation include commercial, ethical, regulatory, and legal. Ensuring frameworks are in place, having robust de-identification processes, and having ethics boards in place can address some of these concerns.   4️⃣ The cost must be viable for all parties. Institutions considering data sharing may need to invest in building bespoke infrastructure and new technologies to complete the process ethically and compliantly. The approach used must be economically viable for all involved.   💡 How can we help? Having a data-sourcing partner helps overcome some of these barriers to data sharing. We partner with world-leading institutions to access large and representative datasets for training and validating medical AI, increasing the quality of products for patients and reducing the barriers to entry for developers. --- 🔗 https://lnkd.in/eHjHsxjR   #DataSharing #Innovation #HealthcareAI --- 📖 1. doi:10.1038/s41591-023-02625-9 2. doi:10.1016/S1470-2045(23)00298-X 3. doi:10.1016/j.jacr.2024.02.034 4. doi:10.1001/jama.2020.12067 5. doi:10.1001/jamanetworkopen.2023.48422 6. https://lnkd.in/dffvrtr

🚀 3 Must-Do Strategies when implementing AI, according to Albert Marinez, Chief Analytics Officer at Cleveland Clinic 👉 One of the big questions today in healthcare is how hospitals and health systems are going to make best use of the power of artificial intelligence across the enterprise. Cleveland Clinic is one of the most renowned hospitals for its commitment to patient-centered care, medical innovation, and clinical excellence. Get to know key tips to fully unleash the potential of artificial intelligence in transforming healthcare 👇 1. Foundational Data Platform 2. Innovation Ecosystem 3. Activation of Organization https://lnkd.in/ewW3WMHz

The Retrieval Augmented Generation (RAG) framework has gained immense popularity for building generative AI search and retrieval systems without baring the cost and effort of re-training or fine-tuning large language models (LLMs). This framework proves effective for many low-risk, back-office operational tasks in healthcare. However, it falls short in more complex production scenarios, especially in healthcare front-office applications. In this blog, hear from Converge VP of AI, Hanna Aljaliss, as he discusses the five critical challenges that any healthcare organization will face when implementing RAG use-cases for customer-facing roles. #AI #RAG #LLM #GenerativeAI

Exploring the intricacies of implementing Retrieval Augmented Generation (RAG) use-cases in healthcare? Read my latest article that delves into the unique challenges and advanced solutions that are shaping the future of AI in healthcare front-office applications. #healthcare #ai #genai #rag

👩⚕️ A typical example for combining AI and the promotion of sustainability is the Healthcare sector. 👩⚕️ Today, we would like to present you the FEAM white paper on “Sustainable AI to Drive Global Health.” This paper is aimed at showing how applying artificial intelligence (AI) can drive transformative change towards sustainable healthcare systems in all the European countries. 🧩 As key insights: - 🔬The potential of AI in the field of biomedical research and development - 🫂The fundamental question of trust within the healthcare systems - 🇪🇺 The creation of the European Health Data Space and other EU supportive legislations and policies. You want to know more? Here is the white paper: https://lnkd.in/dnkRBD4n #ai4greensme #ai4green #circulareconomy #sustainability #AIforGood #entrecomp #entrecompframework #AIbusiness #AI #GreenBusiness #SMEs #Innovation #ArtificialIntelligence #communitynews

I had the honor of getting to speak to Children's National Hospital new vice president and chief data and artificial intelligence officer Alda Mizaku about her top priorities, challenges to AI implementation and gaining buy-in from staff who may be apprehensive about the technology in healthcare. Read the full article to learn more about Alda's innovative approach and the future of AI at Children's National Hospital:

🚀 Excited to Share My Latest Project! 🚀 I'm thrilled to introduce my latest project: a cutting-edge AI-powered chatbot integrated with an SQLite database, designed specifically for clinics and hospitals! 🤖 Overview: This innovative chatbot brings a transformative experience to the healthcare sector by efficiently managing patient interactions and administrative tasks. Here’s what it can do: Retrieve User Information: Forgot your appointment details? No problem! Our chatbot can fetch your information directly from the database. Book New Appointments: Seamlessly book appointments with a check on availability, and execute SQL queries to confirm your booking in real-time. Cancel Appointments: Need to reschedule or cancel? It's all handled smoothly with our intuitive interface. Prescription Management: Check the availability of prescribed medications in the system and confirm your prescription booking with ease. Medical Consultation: Get preliminary medical consultation and advice, all from the comfort of your home. 🌟 Why It’s Unique & Powerful: Efficiency in Healthcare: By leveraging the power of AI, this chatbot can significantly reduce administrative burdens, allowing medical staff to focus more on patient care. Seamless Integration: The chatbot’s integration with an SQLite database ensures robust data handling and real-time responses. Future-Ready: This project is not just for today. It’s a forward-thinking solution that can evolve and be scaled for wider use in clinics and hospitals globally. 🔍 The Vision: Imagine a healthcare environment where routine queries and administrative tasks are automated, providing a seamless experience for patients and healthcare providers alike. My project harnesses the potential of AI to bring us closer to that future. I'm looking forward to seeing this project make a real impact in the healthcare industry and exploring further enhancements. Would love to hear your thoughts and any feedback you might have! #AI #ClinicAssistant #Chatbot #SQLite #Healthcare #NLP #FutureOfHealthcare

Building IQVIA Healthcare-grade AI™ using LLMs faces technical and governance challenges due to the complexity and sensitivity of healthcare data. This blog post will highlight key technical barriers to realizing the value of LLMs, including: ▪️ Low accuracy and reliability of generated outputs ▪️ Complexity and diversity of medical data ▪️ Limitations of traditional prompting methods https://bit.ly/3JL2EGe #HealthcareAI #LLMs #GenAI

This article from FORTUNE discusses the potential of AI to transform healthcare systems while addressing concerns about its implementation. Here's a summary of the key points: ** AI's Promise in Healthcare ** AI offers solutions to alleviate pressures on struggling healthcare infrastructures by: - Automating administrative tasks - Assisting with clinical decisions - Reducing wait times - Interpreting scans These applications could allow physicians to spend more time with patients while maintaining high standards of care. ** Challenges and Risks ** The article highlights several issues that have hindered AI adoption in healthcare: - Margin for error in popular AI tools, particularly large language models (LLMs) - Inconsistencies in AI-generated recommendations due to training on unfiltered internet data -- Lack of robust regulatory frameworks, especially in the U.S. - Concerns about data quality and accountability ** Rebuilding Trust in Healthcare AI ** To address these challenges and rebuild trust, the article suggests: - Increasing transparency in AI development and testing - Training AI tools exclusively on robust healthcare data - Implementing strong regulatory frameworks - Adhering to high standards of data integrity and patient safety The author emphasizes the need for AI in healthcare to be "more boring" - transparent, thoroughly tested, and grounded in science rather than presented as a magical solution. ** Potential Benefits ** If implemented correctly, AI has the potential to: - Enhance human capabilities in healthcare - Reduce errors and improve decision-making - Promote a more proactive, preventive model of care - Transform healthcare to be more efficient, effective, and patient-centered The article concludes that addressing the trust gap in AI is crucial to unlocking its potential to revolutionize healthcare systems. https://lnkd.in/ecbeCdG2 #healthcare #healthcareinnovation #healthcareit #healthcareai #ai #genai #generativeai #llm #ml #bigdata #healthcaredata #PatientEngagement #PatientCare #PatientOutcomes #AccessToCare