Christina Busmalis

At HLTH Europe in Amsterdam, the Trustworthy & Responsible #AI Network (TRAIN) unveiled its ambitious expansion across Europe, aimed at enhancing the deployment of responsible AI within the healthcare sector. Esteemed organisations such as Erasmus MC, HUS Helsinki University Hospital, and others are collaborating, with Microsoft as the enabling technology partner, positioning TRAIN to revolutionise healthcare AI applications. This initiative focuses not merely on the adoption of technology but on ensuring that AI is utilised responsibly to improve patient outcomes, streamline processes, and reduce costs, all while adhering to the utmost standards of quality and safety. The inclusion of Foundation 29 underscores the consortium’s dedication to patient empowerment and data-driven healthcare innovations. #HealthcareInnovation #ArtificialIntelligence #ResponsibleAI #EuropeanHealthcare #AIinHealth #DigitalTransformation #Microsoft #HealthTech

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🔍⚖️ Practitioners Guide To Responsible AI - Part 3 Building on our previous discussions (https://lnkd.in/ekchutB4), today we delve deeper into the first layer of our Responsible AI framework: Model Training & Data Security. In the healthcare and life sciences sectors, the integrity and security of data are paramount. This layer addresses key concerns such as: 1/ Data Privacy: Ensuring patient data is protected and compliant with regulations. 2/ Bias Mitigation: Implementing comprehensive strategies to prevent bias in AI models, ensuring equitable outcomes across different demographics. 3/ Data Integrity: Maintaining the accuracy and consistency of data throughout its lifecycle. and 4/ Secure Data Handling: Safeguarding data during storage and transfer to prevent unauthorized access. So, what does this mean in practice? Let’s look at some examples: If your organization handles unique patient data, it's crucial to ensure that your AI models do not inadvertently favor specific races or genders. But when you want to do this at scale, you will need integrated tools to continuously validate, test, and audit both the data and the model through their lifecycle. Another example, when sharing data across lines of business, the data and its metadata should be self-contained and easily interpretable to prevent misinterpretation. Again, you will need tools to do this at scale – accounting for discovery, authentication and authorization. Well, it's easier said than done! How would one go about supporting these responsible AI practices at scale? AWS, for example, offers a range of integrated tools that allow for organizations to manage these issues at scale. Amazon SageMaker Clarify helps detect and mitigate bias during data preparation, model training, and deployment, providing insights into model predictions for greater transparency. Amazon DataZone facilitates governed data sharing and metadata management, ensuring data privacy and integrity across business lines. Additionally, AWS Identity and Access Management (IAM) and AWS Key Management Service (KMS) ensure secure data handling by managing access and encryption. These tools collectively enable healthcare and life sciences organizations to implement robust data governance and responsible AI practices effectively. In my next post, I will dive deeper and share insights on the next layer of our Responsible AI framework: Model Usage, Infrastructure, and Deployment. 💬 How are you integrating Responsible AI practices and tools into your operations at scale? 📢 Subscribe to my newsletter to get access to strategies and practical guidance on accelerating adoption of generative AI within your organization. Get started here: https://lnkd.in/g3bdneR7 #genai #ai #aws #ResponsibleAI #AIinLifeSciences #HealthcareAI #AIEthics #AIGovernance #AISecurity

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👉🏼 AI in the repurposing of potential herbs for filariasis therapy 🤓 Somsri Wiwanitmkit 👇🏻 https://lnkd.in/edD44fmh 🔍 Focus on data insights: - 📊 ChatGPT provided relevant medication suggestions for all ten hypothetical clinical scenarios, demonstrating its potential utility in clinical decision-making. - 🧠 The AI's recommendations aligned with current medical literature, indicating a strong foundation in existing research. - 🔄 Insights from ChatGPT highlighted possible drug repurposing strategies, which could enhance treatment options for filariasis. 💡 Main outcomes and implications: - ✅ ChatGPT's effectiveness suggests that AI can play a significant role in supporting healthcare professionals in selecting appropriate therapies. - ⚠️ Limitations were noted, including the need for more clinical context and the inability to replace traditional treatment protocols. - 🔍 Further research is necessary to refine AI applications in therapeutic settings, particularly for complex diseases like filariasis. 📚 Field significance: - 🌱 The study underscores the importance of integrating AI tools in healthcare, particularly for drug repurposing and enhancing treatment strategies. - 💡 It opens avenues for future exploration of AI in other therapeutic areas, potentially transforming how healthcare providers approach medication selection. - 🏥 The findings advocate for collaboration between AI technologies and medical expertise to improve patient outcomes. 🗄️: [#AI #filariasis #drugrepurposing #healthcare #ChatGPT #clinicaldecisionmaking #tropicalmedicine #pharmacology]

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🥂Celebrating Visionary Leaders and Encouraging Action 🚀 McKinsey estimates that Generative AI (GenAI) could generate $60B-$110B a year in economic value for pharma/medical products by boosting productivity, across drug compound identification, development & approval processes, and optimizing product marketing strategies - https://lnkd.in/efJ9Wkrx Many leading healthcare/life sciences organizations are starting to prioritize GenAI education and enablement for their teams. Over the last few week, I have had the pleasure of interacting with a few such visionary leaders who emphasized the importance of up-skilling their teams on GenAI. These leaders were happy to carve out half a day of their and their team’s time to understand GenAI, see multi-modal genAI in action, discuss GenAI hype vs reality, review GenAI adoption journey, and discuss areas where LoB and IT can start adoption exploration for their first MVP (minimal viable product). I couldn't agree more with this forward-thinking approach, and would like to share some action items to inspire leaders, across industries, to follow suit! Ask for your technology and/or GSI partners to conduct ‘Innovation w/ GenAI’ workshops with your cross-functional teams of LoB (line of business) and IT professionals on the fundamentals of GenAI and its various applications within your industry. Make sure these workshops are NOT designed as presentations or marketing services, but an interactive dialog where your teams can ask questions, see real world applications on how GenAI is being used, discuss sensitive topics, understand what a typical GenAI journey looks like, and get to understand the change management aspect of the adoption journey. Next, allow for collaboration between IT and business professionals to exchange ideas and explore GenAI-driven solutions for optimizing tasks and workflows for their most time consuming manual processes. I like to facilitate such sessions using design thinking constructs to allow for collective ideation, grouping, and voting to identify the best candidates for an MVP (minimal viable product). Important side note – I discourage POCs now because the technology has been proven and now being adopted at scale across industries. It’s time for action and accelerated adoption, so start with a MVP and iteratively develop/scale your GenAI application. Finally, provide a safe environment for experimenting with GenAI tools and techniques, allowing teams to develop solutions and evaluate their potential benefits. By setting time/scope/budgetary guardrails you can control the blast radius for safe experimentation and foster a learning culture for accelerated adoption. 💬 How are you enabling your cross-functional teams on GenAI adoption? 📢 Subscribe to my newsletter here: https://lnkd.in/g3bdneR7 #genai #ai #aws

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Healthcare is probably the most impactful utility of GenAI! Significant Impact & Future Potential: Generative AI is transforming healthcare, impacting areas like drug discovery, diagnostics, and treatment. Revolutionizing Drug Development: AI helps explore vast chemical spaces for new drugs, reducing failure rates in clinical trials. Focus on Healthcare Applications: Companies like Nvidia are developing AI-powered platforms for medical imaging, diagnostics, and robotic surgery. Real-time Decision Making: Generative AI, similar to self-driving cars, uses sensor data for real-time decision support in surgery. Post-operative Care & Beyond: AI can compile reports, review past surgeries, and improve decision-making in future procedures. #healthcare #innovation #ml #llm #data #drugdiscovery #patient #safety #clinicaltrials #clinicaldatamanagement https://lnkd.in/g_AEAUns

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Gen AI x Digital twin- interesting read. The implications are phenomenal for the #healthcare industry also, (in addition to examples cited in the article) by enabling personalized data driven approaches to #patientcare.

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