Frank Kumli’s Post

Theranostics and Artificial Intelligence: New Frontiers in Personalized Medicine! I. Theranostics Theranostics combines advanced imaging techniques with targeted therapy, offering insights into critical areas of precision oncology, such as optimized treatment regimens and efficient monitoring. II. Key Areas of AI Application 1. Patient Selection and Risk Stratification Given the unique nature of each patient, aligning patients with the optimal treatment options necessitates a thorough analysis taking into account both the patient's individual characteristics and the tumor's specifics. Artificial intelligence holds strong promise for this process by integrating diverse types of patient- and tumor-specific data, including multi-omics (such as genomics and proteomics) 2. Tumor Dosimetry By predicting and identifying the absorbed radiation doses by the target, organ-at-risk, and healthy tissues, dosimetry studies provide essential insights into the objective assessment of a treatment's safety and efficacy for each patient. The utilization of AI-based tools in dosimetry studies for theranostics holds the promise of significantly enhancing study accuracy and efficiency 3. Disease Monitoring By harnessing adaptive learning algorithms, AI holds the revolutionary potential to leverage traditional parameters to advance therapy monitoring. Various AI-based platforms offer e-consults to clinicians and patients across numerous specialties, enriching and refining the clinical experience 4. Drug Discovery Radiopharmaceutical development is a challenging endeavor, encompassing a myriad of facets including target identification, lead compound identification, radionuclide selection, vector molecule formulation, synthesis, evaluations, and drug approvals. These processes are tedious and costly, but AI-powered theranostics drug discovery studies promise optimization, paving the way for quicker development and approval of innovative radiopharmaceuticals. III. Outlook Despite these advances holding significant promise, adopting AI algorithms to the routine clinical practice raises several concerns, including security of data, accountability of outcome, and comprehensiveness and diversity of outcomes. Therefore further research and guidelines outlining the responsible use of AI are needed to harness the full potential of AI, while considering its limitations and ensuring its successful integration into clinical practice Check out the insightful article by the team at Mayo Clinic here: https://lnkd.in/dwPgc3mD #innovation #healthcare #ai #ml #theranostics #radiopharmaceuticals #digitalhealth #healthtech #oncology #genomics #proteomics

GenAI: The Open Medical-LLM Leaderboard: Benchmarking Large Language Models in Healthcare! I. Potential Over the years, Large Language Models (LLMs) have emerged as a groundbreaking technology with immense potential to revolutionize various aspects of healthcare II. Challenge When models are used for recreational conversational aspects, errors have little repercussions; this is not the case for uses in the medical domain however, where wrong explanation and answers can have severe consequences for patient care and outcomes III. Benchmark To fully utilize the power of LLMs in healthcare, it is crucial to develop and benchmark models using a setup specifically designed for the medical domain IV. Open Medical-LLM Leaderboard The Open Medical LLM Leaderboard aims to track, rank and evaluate the performance of large language models (LLMs) on medical question answering tasks V. Assessment It evaluates LLMs across a diverse array of medical datasets, including MedQA (USMLE), PubMedQA, MedMCQA, and subsets of MMLU related to medicine and biology VI. Key Findings 1. Commercial models like GPT-4-base and Med-PaLM-2 consistently achieve high accuracy scores across various medical datasets, demonstrating strong performance in different medical domains 2. Open-source models, such as Starling-LM-7B, gemma-7b, Mistral-7B-v0.1, and Hermes-2-Pro-Mistral-7B, show competitive performance on certain datasets and tasks, despite having smaller sizes of around 7 billion parameters 3. Both commercial and open-source models perform well on tasks like comprehension and reasoning over scientific biomedical literature and applying clinical knowledge and decision-making skills 4. Google's model, Gemini Pro demonstrates strong performance in various medical domains, particularly excelling in data-intensive and procedural tasks like Biostatistics, Cell Biology, and Obstetrics & Gynecology. However, it shows moderate to low performance in critical areas such as Anatomy, Cardiology, and Dermatology, revealing gaps that require further refinement for comprehensive medical application Check out this important initiative by Ankit Pal at Saama, Pasquale Minervini, PhD at School of Informatics, University of Edinburgh and Clémentine Fourrier at Hugging Face here: https://lnkd.in/gf637xED #innovation #healthcare #ai #genai #llm #healthtech #digitalhealth

Metroverse: An Urban Economy Navigator! Metroverse is an urban economy navigator built at the Growth Lab at Harvard University. It is based on over a decade of research on how economies grow and diversify and offers a detailed look into the specialization patterns of cities. As a dynamic resource, the tool is continually evolving with new data and features to help answer questions such as: * What is the economic composition of my city? * How does my city compare to cities around the globe? * Which cities look most like mine? * What are the technological capabilities that underpin my city’s current economy? * Which growth and diversification paths does that suggest for the future? As city leaders, job seekers, investors and researchers grapple with 21st century urbanization challenges, the answer to these questions are fundamental to understanding the potential of a city Make sure to check out the navigator by Harvard's Growth Lab Lab here: https://lnkd.in/dW3-dpcq Harvard University #innovation #strategy #cities #ecosystem #clusters #policy

Foresight for Foresighters! I. Uncertainty Until 2035 the uncertainty and complexity will only be increasing, giving rise to a Multi Crisis Environment II. Foresight Strategic Foresight is key for navigating complex and uncertain futures. III. Foresight Expertise Consulting and Strategic Foresight expertise will transition from being exclusive to a select class of professionals to becoming universally accessible through natural language IV. Recommendations for Foresight Professionals 1. Leverage fine-tuned AI models specific to the field of Foresight and Consulting and establish inhouse data science expertise 2. Become a lighthouse Foresight Institution in order to front run the future in a credible way 3. Focus on the value of original thought as the key area for human labor in successfully applying AI tools and making knowledge scale 4. Be part of the next generation of platforms in order to leverage open source and on-chain practitioners for your business model 5. Benefit from the rise in demand for Strategic Foresight driven by the rise of uncertainty by offering orientation and deep insights 6. Transform Marketing and Sales activities to fit a new era of AI gatekeeping by actually adding value for the customer 7. Become cutting edge by applying Foresight in the context of AI Alignment 8. Offer faster Time to Value products that utilize knowledge-automation fully 9. Adapt to the democratization of knowledge work and the deflationary force of automation by scalable products and new business models 10. Shift your attention towards implementation & actionable insights 11. Position yourself in the renaissance of Forecasting 12. Leverage open source for its competitive advantage 13. Participate in the shift towards inhouse expertise 14. Offer Immersive experiences, digital twins of the future and persistent images of the future in order to move beyond PDF and PPT when communicating results 15. Expand your areas of expertise beyond technology focusing on solutions for the next generation Check out the report by Julia Lampert and Patrick Duffner at 2b AHEAD ThinkTank GmbH here: https://lnkd.in/d2kHPkh3 #innovation #strategy #foresight #futurethinking #scenarioplanning #uncertainty #ai #genai #dao #digitaltwins #complexity

The People-Centric Lab of the Future: AI-supported Innovation to Meet Tomorrow’s Needs! I. Roles of AI in Innovation Teams 1. Scribe: generating content across all media, based on specific prompts 2. Librarian: answering questions, lookup, and search 3. Analyst: summarizing data series and underpinning pattern recognition and extrapolation 4. Engineer: aiding task definition, analysis, and optimization for problem solving 5. Scientist: causal inference of general laws based on empirical observation and counterfactual reasoning 6. Craftsman: directing and manipulating physical tools and objects in uncontrolled environments 7. Artist: aiding ideation and design, bringing together diverse influences and perspectives to help innovators create original ideas and concepts II. Concepts of the Lab of the Future A. Democratization: making AI available, customized for everyone and adopted as a natural way of working, closing the gap between AI users and AI providers B. Collaboration: using AI and other digital tools to help supercharge the sharing of data and knowledge, forming a virtuous circle unconstrained by the limitations of current data management C. Ambidexterity: setting the organizational context to facilitate and encourage AI-supported breakthrough innovation and exploration, at the same time as maintaining R&D efficiency and productivity III. How to get started A. Democratization 1. Identify and prioritize AI use cases and adoption, integrated across the R&D and innovation cycle, with defined AI skill-level targets and competences 2. Focus on end-to-end processes so technical experts can seamlessly adopt AI into their working routines 3. Ensure no separation between “innovating with AI” and “innovations with AI.” B. Collaboration 1. Develop and build out future requirements in terms of data architecture and engineering, as well as user experience 2. Strengthen and diversify the wider innovation ecosystem and increase permeability between roles, teams, and the company at large 3. Redefine the skills and expertise that are needed and use this to drive hiring, training, and team composition C. Ambidexterity 1. Strengthen the role and caliber of project leadership and solution engineering and increase the iteration steps in end-to-end innovation with more frequent optimization. 2. Focus on optimized decision making — reduce hierarchies and redesign governance to differentiate between types of decisions 3. Foster an innovation culture that is familiar and comfortable with uncertainty and ambiguity — essential for being able to experiment and learn along the AI adoption journey Check out the report by Michaël Kolk, Marten Zieris and Michael Eiden at Arthur D. Little here: https://lnkd.in/dgx2X4-K #innovation #ai #genai #cocreation #strategy #learning #skills #ambidextry #democratization #collaboration

World Creativity and Innovation Day: What is the World without Innovation! Innovation is the lifeblood of progress and the driving force behind the advancement of sustainable development goals In a business context, it’s the ability to conceive, develop, deliver, and scale new products, services, processes, and business models. Innovation can create value, enhance productivity, and foster competitiveness The potential of innovation fuels hope. But all too often, that hope is dashed by ineffective execution. What does it take to really spur innovation? On this World Creativity and Innovation Day, check out the insights by McKinsey & Company to learn how top innovators outperform peers, drive innovation with generative AI, and create a culture that accounts for the human side of innovation https://lnkd.in/dKcgy2h2 #innovation #strategy #creativity #ai #culture #genai

Implementing the European Health Data Space Across Europe! Findings: I. Governance 1. The conditions and processes to access data for secondary use vary significantly between countries 2. There is variation in countries' approaches in the run-up to adoption of the EHDS 3. A single model of centralised national EHDS governance will not fit all countries II. Capacity and Skills 1. Digitalisation in national health systems varies significantly between countries and areas of healthcare provision 2. Data systems in healthcare remain highly fragmented and infrastructures to connect them are scarce 3. Data-sharing initiatives exist at EU, national, regional levels 4. Industry will be a driving force for implementation III. Resources and Funding 1. The total budget and the upfront costs of implementation could not be precisely estimated 2. Available funding and resources for the EHDS were considered insufficient 3. Countries with limited national resources are also the ones where the investments required will be greatest 4. Existing and future European projects of common interest could facilitate implementation IV. Data Quality 1. Vast disparities exist in the implementation of standards for health data 2. Data quality is inconsistent and interoperability remains a universal challenge 3. Capacities and budgets for quality improvement measures are low in healthcare 4. Patients and citizens could have a role to play in controlling and enriching their own data 5. Extensive work is happening at EU level to help develop a common approach to quality V. Primary and Secondary Use 1. Current workflows and data management structures are not geared towards the need for collection of datasets for secondary use. 2. The reliance on paper records keeps entire territories and categories of health data inaccessible for research 3. New technologies could facilitate data collection and motivate healthcare professionals to record high-quality data 4. Paths for the introduction of data-driven innovation into clinical pathways exist 5. Public-private and multistakeholder collaborations are accelerating the development of data-driven solutions p VI. Awareness, Education, Communication 1. Awareness of the upcoming EHDS regulation is low among stakeholder groups 2. Citizens' acceptance of health data sharing for secondary use is variable 3. Patients are generally more conscious of the importance of data-sharing p 4. Data-sharing initiatives and projects completed in the course of the COVID-19 pandemic can serve to demonstrate tangible results and benefits Check out the report by EIT Health and European Union here (thanks Danny Van Roijen for the share!): https://lnkd.in/d9m9HTqh #innovation #healthcare #healthdata #digitalheath #healthtech #ehds

High-Impact Generative AI Use Cases in Finance! I. Broad Categories of Value Capture 1. Cost reduction Reduce cost, typically by 30% or greater, primarily through automating job functions and then undertaking job substitutions 2. Process efficiency Create process efficiencies through automating standard tasks and reducing manual interventions 3. Growth Increase revenue generation through hyper-personalized marketing for target customers 4. Accelerating innovation Increase the pace of new product or new service development and speedier go-to-market 5. New discovery and insights Uncover new ideas, insights, and questions and generally unleash creativity 6. Government citizen services Increase accuracy of various federal and local programs and create easier access for at-risk populations II. Finance Use Cases 1. Finance Insights Engine Platforms powered by generative artificial intelligence (Gen AI) can review and analyze data, identify gaps and suggest ways to fix them, and provide leaders with on-demand insights 2. Autonomous Close Generative AI could create a true “lights out” close process by improving leader visibility, minimizing rote work, and ultimately managing and completing tasks 3. Dynamic Risk Assessment AI, including generative AI, will continue to elevate risk assessments, driving a streamlined and value-added integrated risk management approach that could transform today's periodic risk assessments into a state of continuous monitoring 4. Cash Flow Forecasting Generative AI can improve the accuracy of cash flow forecasting, reduce manual processes, and provide greater insights to business leaders 4. Order to Cash A mix of AI can fundamentally transform traditional order to cash processes. AI, generative AI, and machine learning can automate and improve tasks and work flows across the order to cash cycle, resulting in cost savings and faster collections 6. Procure to Pay Generative AI can boost efficiencies and unlock value across the procure-to-pay processes 7. Working Capital Optimization Generative AI can help companies keep a pulse on their working capital by continuously monitoring asset efficiency and identifying opportunities for a company to free up trapped capital and create shareholder value 8. Engage with my Tax Data Generative AI can enable tax professionals to access, analyze, and gain insights from their tax data by automating the process of data extraction, transformation, and loading. This can reduce the time spent on routine tasks, thus allowing professionals to focus on deriving insights from the data 9. Investor Communications Generative AI can be used to create consistent, standardized investor communications in multiple formats with minimal manual effort Check out the report by Deloitte here https://lnkd.in/dtCFScQM #innovation #ai #genai #finance #fintech

Supercharge Healthcare through GenA! Use Cases: I. Life Sciences 1. Drug discovery * Identification of potential drug targets using literature mining * Personalization of immunotherapies 2. Clinical development * Authoring of protocol to optimize clinical trials * Generation of insights to design clinical trials by scanning scientific literature or databases * Personalized messaging to engage trials patients and estimation of enrollment rates * Generation of code to expedite regulatory submission 3. Operations * Interpretation of telemetry to preempt predictive maintenance * Generation of recommendations to design sustainable products * Data augmentation to improve quality control models * Improvement of supplier risk assessment based on historical data 4. Sales and Marketing * Generation of hyper-personalized content for customer engagement * Generation of custom sales pitches based on sentiment analysis 5. Regulatory * Summaries of latest medical literature to keep PV professionals up to date * Detection of safety weak signal from social media or patient forum * Support in regulatory report generation * Generate responses with a smart companion to engage with regulatory authorities during document submission II. Public Health 1. HCPs training * Generation of realistic patient scenario for training HCPs * Drugs response prediction using digital twining * Generation of synthetic patient data 2. Patient Flows & administrative management * Streamlining hospital operations with flow twining and predictive models and personalized schedules for patients and HCPs * Automation of HCP notetaking during consultations to focus on patients 3. Diagnosis, treatment & follow up * Augmentation of medical datasets like images to train machine learning models used in medical imaging and diagnostic * Generation of personalized treatment plans considering patient data, genetic information and medical history * Dosage adjustment or better alternative recommendations based on patient monitoring of patients * Patient support through medical chatbots to answer questions, facilitate appointment scheduling and provide advices on managing health conditions III. Health Insurance 1. Operations * Identification of potential violations related to operational procedures * Detection of potential fraudulent behaviors * Creation of personalized insurance plans * Verification of claim completeness and coverage by scanning documents * Automation of data entry for claim management 2. Patient services * Automatic filling of questionnaire to reduce drop out * Redirection of patient guided by a chatbot * Providing information on health coverage or reimbursement rate * Promotion of preventive health behaviors * Patient segmentation to launch adequate prevention actions https://lnkd.in/duHtQiQB #healthcare #innovation #ai #genai #healthtech #digitalhealth Capgemini Capgemini Invent