Deniz Coskun, Dr. tech.

Challenges of Using generative AI in medicine: 1. Data Quality and Availability: High-quality, large-scale datasets are essential for training generative models, but such data can be scarce, incomplete, or biased in the medical field. 2. Privacy and Security: Handling sensitive patient data requires stringent privacy measures. Ensuring that generative AI models comply with regulations like HIPAA is a significant challenge. 3. Regulatory Compliance: Generative AI applications in medicine must adhere to strict regulatory standards set by authorities such as the FDA, which can slow down development and deployment. 4. Bias and Fairness: Generative models can inadvertently learn and propagate biases in the training data, leading to unfair or inaccurate outcomes. Interpretability and Transparency: Medical professionals must understand how AI models make decisions. Many generative models, like deep learning networks, are often considered "black boxes." 5. Validation and Testing: It is crucial but challenging to ensure that generative AI models are thoroughly validated and tested in diverse, real-world scenarios. Integration with Existing Systems: Integrating generative AI solutions with existing healthcare IT systems (like EHRs) can be complex and costly. 6. Ethical Concerns: Using generative AI in medicine raises various ethical issues, including the potential for misuse, the impact on doctor-patient relationships, and the implications of AI-generated content. 7. User Trust and Acceptance: Building trust among healthcare providers and patients in AI-generated solutions is essential for widespread adoption. 8. Cost: Developing, implementing, and maintaining generative AI solutions can be expensive, particularly for smaller healthcare providers. 9. Scalability: Scaling generative AI solutions across different healthcare settings and geographies can be challenging due to variability in healthcare practices and infrastructure. 10. Responsibility and Accountability: Determining who is responsible and accountable for AI-generated decisions or errors is a complex issue. 11. Training and Expertise: Healthcare professionals need proper training to effectively use and understand generative AI tools. 12. Real-Time Processing: Generative AI models often require substantial computational resources, hindering real-time clinical applications. 13. Generalizability: It is crucial to ensure that generative AI models generalize well to different patient populations and medical conditions. 14. Data Annotation and Labeling: Annotating medical data for training generative models is labor-intensive and requires expert knowledge. 15. Patient Consent: Obtaining informed consent from patients for using their data in training generative AI models can be complicated.

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🌐💊Data Mesh Architecture: Fueling GenAI in Healthcare and Life Sciences 🚀   Building on our discussion about AI-ready data infrastructure from last week (newsletter - https://lnkd.in/erFjd527), let's explore why healthcare and life sciences (HCLS) organizations must revolutionize their data management for advanced GenAI applications. These sectors face unique challenges requiring robust, adaptable data systems. They handle diverse data types - clinical, genomic, research, and operational - while navigating strict regulations and fostering innovation. Real-time, scalable insights are crucial for critical decision-making. Empowering stakeholders to analyze data independently and providing quality, diverse training data for AI/ML models are essential. These factors underscore the need for infrastructure supporting next-gen AI-driven healthcare solutions.   While various data architectures have tried to address these challenges, data mesh stands out in the HCLS industry by decentralizing data ownership and treating data as a product. It empowers domain teams to manage their own data pipelines independently. This approach aligns well with the industry's diverse needs, where departments handle various types of data from clinical to operational.   Key Principles of Data Mesh architecture are - 1/ Assign data ownership to specific business domains, ensuring contextual management. 2/ Domains create and maintain discoverable, accessible data products for other teams. 3/ Federated governance ensures consistent quality, security, and privacy standards across domains, while preserving flexibility.   How will data mesh help HCLS firms adopt GenAI applications? It ensures diverse, quality data is discoverable for GenAI training and RAG solutions. Distributed ownership enables domain-specific security, reducing vulnerabilities. Data producers' quality responsibility enhances GenAI performance. Self-service access accelerates experimentation, speeding GenAI MVPs and innovation.   However, note that Data Mesh is complex and challenging to implement. It demands organizational change, executive support, and budget. Challenges include decentralized governance, higher skill needs in business units, less central control, increased complexity and costs, and potential tool sprawl. Careful planning is crucial for success.   Consult your technical partners, GSIs, and consultants for more on data mesh. Explore workshops to assess your data landscape, readiness for data mesh, and build a business case for executive support and budget allocation.   💬 How ready is your organization to adopt a data mesh architecture, and what specific challenges do you foresee in implementing this approach at your firm? 📢 Subscribe to my newsletter: https://lnkd.in/g3bdneR7 #genai #ai #aws #data #datamesh

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BPM- Summer School - Mission Possible won the book lottery by Oliver Zeller and just received this handsigned wonderful lecture "Mission Possible" from Etienne Kneschke and Simon Geisenberger. Thank you very much for this stunning lecture!📘 I'll use this to start my 🤓 BPM - Summer School 🤓 including a book review. BPM - Summer School Week 1 - Chapter 1 From complexity to digital transformation 🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀 Very interesting topic and there are many buzzwords around it. Here some of them get demystified. So difference between complexity and complicated was already known to me, but did anyone know that there's a difference between digitisation and digitalisation? Together with innovation this is building the pyramid of digital transformation. My key takeaway? I totally agree that we need to fix the basics including organizational change, more quality in process data and systems, before we achieve innovation and digital transformation. Especially to establish quality of process data it requires not a one off effort, but a continuous improvement process in the long term. PS: This is a chapter for real Star Trek fans!🚀 #BPM #ProcessModeling #ProcessMining #DigitalTransformation

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BPM- 🤓 Summer School🤓 - Mission Possible 🚀 Time to continue my summer school with the wonderful lecture "Mission Possible" from Etienne Kneschke and Simon Geisenberger. Thank you very much for this stunning lecture!📘 BPM SUMMER SCHOOL - WEEK 3 Chapter 3: From roles to smart processes 🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀 Review: This chapter is looking at roles and how they are used in processes. Similar to organization types, there are a lot of silos creating their own roles for the same purpose. Afterwards we learn how processes act as a backbone of an organization and learn one approach to segregate them. Finally a topic which is at my heart❤ is getting clarified and I would call it:  Why process modeling is not process documentation!   My take away: Content is not a visual flow!➖ Content is not documented - it is modeled.🤓 Content is creating transparency.🌤 Content is enriching our process design with information like roles, systems, work instrcutions and so much more.🍨 Content is setting the process mining results in the right context!❗ Content is what makes humans happy.😀 Content is what AI needs to breathe.🧠 Content is worth in investing / replaning or reprioritizing resources.➕ BECAUSE CONTENT IS CREATING ENORMOUS VALUE!💰 PS: I really liked the metaphor of anamnesis. It is kind of similar with the construction metaphor Caspar Jans once has used. #BPM #ProcessModeling #ProcessMining #DigitalTransformation

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Researchers at TU Wien and MIT have found similarities between artificial intelligence and natural visual systems. Neural networks can be trained to recognize objects in images with high success rates. Convolutional neural networks imitate biological neural networks in the eyes and brain. Filters in artificial neural networks develop patterns similar to those in biological nervous systems. Understanding these patterns can lead to faster and more efficient machine learning algorithms. What are your thoughts on the similarities between artificial intelligence and natural visual systems? #AI #ComputerVision #MachineLearning #neuralnetworks

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👉🏼 ChatGPT and Medicine: Together We Embrace the AI Renaissance 🤓 Sean Hacking 👇🏻 https://lnkd.in/e9xN8P4A 🔍 Focus on data insights: - ChatGPT enables the processing, interpretation, and summarization of vast data sets in medicine. - Integration of medical imaging data with other multiomics data is facilitated by ChatGPT. 💡 Main outcomes and implications: - ChatGPT enhances the accessibility and interpretability of complex biological data in medicine. - Physicians and researchers can utilize ChatGPT as a digital assistant to improve patient care. 📚 Field significance: - The rise of generative AI models like ChatGPT democratizes AI applications in modern medicine. - Incorporating AI tools such as ChatGPT into medical practices can provide a competitive edge for healthcare professionals. 🗄️: [#AI #medicine #datainsights #healthcare #ChatGPT]

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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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🚨 EU AI Act - Fundamental Rights Impact Assessment (FRIA) 🚨 #AI systems in #healthcare (high-risk, deployers, HCPs) need to start preparing to conduct a Fundamental Rights Impact Assessment (#FRIA) previously not undertaken, meaning organizations using and implementing #AI systems. Find out how conduct a #FRIA, key elements and key dates 📆 below by Sigrid Berge van Rooijen 👇

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OpenAI has introduced a method to make the decision-making process of #GPT4 more interpretable by breaking it down into human-understandable concepts. This approach aims to demystify how GPT-4 generates responses, providing a clearer view of the underlying mechanisms. For practitioners in the #LifeScience industry, this transparency is crucial as it enhances the reliability and trustworthiness of #AI applications in #research and #development. The Role of Sparse Autoencoders In addition to interpretability, OpenAI's (and other LLM companies') research on sparse autoencoders addresses the need for efficient data representation. Sparse autoencoders are designed to learn compact and efficient representations by enforcing sparsity constraints, which means using fewer active neurons to represent input data. This method is particularly relevant for handling large-scale datasets common in the Life Sciences, where efficient data processing and feature extraction are essential. Practical Implications for Life Sciences For professionals in the Life Science sector, these advancements offer several practical benefits: - Improved Model Transparency: Understanding the conceptual framework of GPT-4 can lead to more transparent AI models, which is critical for regulatory compliance and ethical considerations in biomedical research. - Enhanced Data Efficiency: Sparse autoencoders can streamline data processing workflows, making it easier to manage and analyze large volumes of complex biological data. - Scalability: These methods support the development of scalable AI solutions that can adapt to the growing data demands in the Life Sciences. By integrating these AI advancements, practitioners can enhance their research capabilities, drive innovation, and improve the overall efficiency of their R&D processes. https://lnkd.in/g9SyemSP #ResearchAndDevelopment #SparseAutoencoders #DataEfficiency #ModelTransparency #BiomedicalResearch #Scalability #Innovation #DigitalTransformation #AIinLifeSciences OpenAI MilliporeSigma Merck Group

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ISO 42001: A Framework for Responsible AI Adoption As AI rapidly transforms industries, ISO 42001 emerges as a standard for establishing robust AI governance. This framework, I believe will become crucial for organisations across healthcare and pharmaceutical sectors to ensure ethical, transparent, and accountable AI implementation. "How do you use AI in your organisation to get the 'Job' done?" Key benefits: > Mitigates AI risks: Identifies and addresses biases, errors, and unintended consequences. > Promotes ethical AI: Ensures transparency, fairness, and accountability. > Builds trust: Fosters confidence among stakeholders and the public. > Ensures compliance: Aligns with legal requirements and industry best practices. Challenges: > Evolving landscape: Requires continuous adaptation to keep pace with AI advancements. > Implementation: Demands expertise and resources for effective AI management. > Limited scope: Focuses primarily on technical aspects, not broader societal implications. > It is new! A full slide deck will be available to participants who attended the #PharmaDeviceForum in #melbourne on 23-24th May 2024. #QualityManagement #HealthcareExcellence #pharma #pharmaceutical #Governance PharmOut

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#DigitalTwins in #Healthcare Key Use Cases I. Enhancing #PersonalizedMedicine Disease Simulation and Treatment Response: DTs provide precise simulations of disease progression, enabling tailored medical interventions. Comprehensive Data Synthesis: DTs integrate diverse data types such as genetic, biomarker, and psychosocial information to create a multidimensional health profile. Genetic Variability Modeling: DTs model complex genetic and environmental interactions, addressing variations in patient responses to treatments. Predictive Analysis for Treatment Efficacy: DTs forecast disease progression and treatment impacts, aiding in proactive treatment planning. Tailoring Drug Dosages: DTs customize drug dosages based on personal health data. Advanced Diagnostic Capabilities: They utilize comprehensive scans to provide advanced diagnostic tools for more accurate treatment. II. #DecisionSupport and #PatientCare Improvement Proactive Decision Support: Digital twins act as sophisticated decision-support systems, crucial for managing chronic conditions. Health Data Integration: These technologies integrate data from various sources, enhancing the proactive management of health conditions. Enhancing Patient Engagement: Digital twins empower patients with access to personalized health data, encouraging active health management. Enhancing Patient Communication: They improve communication efficiency, leading to enhanced patient satisfaction. Advancing #Value-BasedHealthcare: Digital twins align treatments with patient outcomes, supporting efficient healthcare practices. III. Improving Healthcare Organizations Improving Caregiver Efficiency: DTs enable quick access to vast amounts of patient data. #ClinicalOperations and Surgical Planning: DTs simulate and refine surgical procedures, improving safety and outcomes. Optimizing Routine Operations: DTs streamline everyday healthcare operations. Cost-effective Care Delivery: By simulating operational strategies, DTs identify the most economical care delivery methods. Facility Layout Optimization: They assist in optimizing hospital layouts. Crisis Management and Planning: DTs are crucial for managing health crises. IV. Acceleration of R&D and Clinical Trials Simulation of Drug Effects and Medical Device Operations: DTs simulate how drugs and devices operate, speeding up development and increasing safety. Optimization of Drug Delivery: They precisely model drug delivery to specific body areas. Enhanced Pharmaceutical Testing: DTs streamline drug development processes. Enhanced Research on Diseases: Useful in studying diseases like Alzheimer's, DTs provide insights to accelerate therapy development. Refinement of #ClinicalTrials: DTs facilitate virtual testing of treatments, reducing reliance on physical trials and addressing ethical concerns with placebos. Continuous Monitoring and Trial Adaptation: They allow for ongoing monitoring and adaptation of trial parameters.

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👉🏼 Using Chat Generative Pre-trained Transformer in Academic Writing in Health: A Scoping Review 🤓 Isabelle Cristinne Pinto Costa https://lnkd.in/eCyuTgyY 🔍 Focus on Data Insights: - ChatGPT enhances scientific production and medical procedure descriptions. - Improves clarity of writing and aligns with scientific journal standards. - Benefits innovation and automation in academic writing. 💡 Main Outcomes and Implications: - Versatility of ChatGPT in academic writing. - Risks include lack of originality and ethical concerns. - Need for regulation, adaptation, and ethical balance in its use. 📚 Field Significance: - Transformative potential of ChatGPT in health academic writing. - Requires human supervision, regulation, and guidelines for responsible use. 🗄️: [#ChatGPT #academicwriting #health #datainsights #innovation]

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