Piotr Maślak

Underlines the importance of #artificialintelligence in the transformation of our society. Hype or not. Over time, I am convinced #AI will drive more insights and automate value chains. In the life science area, for sure we will we the benefits both within the industry, but more importantly in the healthcare system ultimately benefiting patients. Will it be a straightforward path? No, for sure not. But we will see the benefit, and the more we collaborate across areas - and in particular public / private partnerships are key here.

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#WEF report on Future of Jobs 2025-2030: essential read on workforce skills and industry plans. Top job growth: "Big Data Specialists," followed by many roles we need in #Pharma and #Biotech #R&D. It highlights critical questions on training the next generation and determining optimal geographical footprint. https://lnkd.in/e885TjKy

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🚀 Transforming Drug Discovery: The AI Revolution In the rapidly evolving landscape of pharmaceutical R&D, artificial intelligence (AI) is proving to be a game-changer. A recent published in Drug Discovery Today has highlighted some groundbreaking trends that underscore the potential of AI in revolutionizing drug discovery and development*. Exponential Growth in AI-Driven Discoveries Over the past decade, there has been a significant surge in the number of drugs and vaccines discovered through AI. This growth is not just in numbers but also in the speed with which these discoveries are moving from the lab to clinical trials. AI-native biotech companies, often in collaboration with big pharma, are leading this charge, showcasing an impressive annual growth rate in clinical entries*. Doubling Success Rates One of the most striking findings from the study is the success rate of AI-discovered molecules in Phase I clinical trials, which stands at 80-90%. This is nearly double the historical average*. Such high success rates in early trials are indicative of AI's capability to design molecules with optimal drug-like properties, potentially transforming the economics of drug development. Accelerating Drug Development AI is not only improving success rates but also significantly speeding up the drug discovery process. Traditional methods take approximately 4-6 years to discover new small molecules. With AI, this timeline is dramatically reduced. For instance, a recent example from the industry showed that a drug candidate was developed in just 8 months using AI, compared to the years it would typically take**. The Future is Now As we look to the future, the integration of AI in drug discovery promises not only faster development times and improved success rates but also a potential shift in how we approach complex diseases. With every pharmaceutical giant now investing in AI, the next decade could see a dramatic transformation in how new therapies are developed, tested, and brought to market. Call to Action For those of us in the life sciences sector, the message is clear: embracing AI is no longer optional but a necessity to stay competitive and lead in innovation. The opportunities to improve patient outcomes and reduce development costs are immense and within our grasp. Let's discuss how we can further integrate AI into our strategies to not only keep up with the pace of innovation but to set new benchmarks in drug discovery and development also with tools such as our #AIDDISON solution #Digital #AI #ArtificialIntelligence #DrugDiscovery #DrugDevelopment #Technology #Innovation #creativity #futurism https://lnkd.in/gdrdCNfC * https://lnkd.in/gbaiwB4X ** https://lnkd.in/gfF2kuAk

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What sets innovative solutions in health economics and outcomes research (HEOR) apart in healthcare delivery? Are innovations defined by their technological advancements and research methodologies? Every innovation has a potential impact, but how is that impact truly realized and perceived by healthcare stakeholders? Many may think it's all about advanced analytics and data, but there's much more to HEOR innovations than just technology and methodologies. Well, here are the key elements that truly define innovative solutions in HEOR and set them apart in this eye-opening exploration. Here's a snippet of what we explored: ▶️ Enhanced Healthcare Delivery: HEOR innovations aren't just about data and technology; they're about crafting more effective and efficient healthcare strategies that improve patient outcomes. ▶️ Personalized Care: Innovations in HEOR enable the development of personalized healthcare plans, tailoring treatments to individual patient needs and conditions. ▶️ Resource Optimization: Innovative #HEOR methods help optimize the use of healthcare resources, ensuring that interventions are both cost-effective and impactful. ▶️ Cost Management: Addressing the challenges of rising healthcare costs through innovative HEOR solutions ensures sustainable and affordable healthcare delivery. ▶️ Equitable Access: Ensuring equitable access to quality care is paramount. #HEOR innovations work towards minimizing disparities and improving health outcomes across different populations. ▶️ Evidence-Based Decisions: Providing robust, real-world evidence allows for better decision-making processes in healthcare, guiding policy and clinical practice. ▶️ Preventive Health: Beyond treatment, HEOR innovations focus on preventive health measures, aiming to reduce the incidence of diseases and promote overall well-being. ▶️ Stakeholder Confidence: Establishing trust and confidence among healthcare providers, patients, and policymakers is crucial. Effective HEOR innovations instill confidence through transparency and reliability. ▶️ Competitive Advantage: Leveraging innovative HEOR solutions provides a competitive edge, demonstrating the ability to deliver superior healthcare outcomes and efficiency. ▶️ Enhanced Collaboration: Innovative HEOR fosters collaboration across different sectors, bringing together diverse expertise to tackle complex healthcare challenges. Remember, investing in HEOR innovations is an investment in the future of healthcare. It's the cornerstone of achieving better health outcomes and sustainable success in today's healthcare landscape. #Hero #Innovation #Technology #Economic #Growth #Business

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Key Differences b/w Randomized Clinical Trial (RCT) vs RWE (Real-World Evidence): Learn more about Real World Data: https://lnkd.in/gRGZdJJM Reference: https://lnkd.in/gN2vf6Hp #clinicaltrials #clinicareserach #data #healthcare #innovation #patient #regulatory #india #rct #rwe

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Developing Clinical Analytics Solution? Understanding Role-Specific Challenges to Drive Analytics and Technology Solutions! Identifying and addressing the pain points of individuals involved in clinical trials is paramount to the development of effective analytics solutions and technology platforms. By understanding the unique challenges faced by each role, we can create tools that streamline processes, improve efficiency, and enhance decision-making. CuMinds Innovations #clinicaltrials #clinicaldatamanagement #clinicalresearch #data #biometrics #innovation #technology #healthcare #patient

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🚀 Are #AI Ideas More Creative but Less Practical? 🤔 A recent study found that AI-generated ideas tend to be more creative, but not quite as practical as those from human experts. It got me thinking🙄does this mean AI is pushing boundaries in some ways but falling short in others? 🧠 Over 100 researchers in natural language processing (NLP) were asked to come up with fresh ideas and evaluate both AI and human suggestions. 🔒 The reviews were anonymous, so nobody knew which ideas came from AI or people. 🌟 AI ideas stood out for being more original, but they were seen as a bit harder to put into action. 🧩 The study pointed out some challenges for AI, like trouble evaluating its ideas and sometimes getting stuck in similar patterns. Why does this matter? It’s an important question for anyone interested in AI’s future: Can it help us think in new ways and move science forward? According to this research, it’s definitely contributing fresh ideas, but we might still need that human touch to make them work. What’s your take on this? Can AI bring fresh ideas to the table, or does it need more refining before we can count on it for practical solutions? I’d love to hear your thoughts! 👇 #AI #NLP #Research #AIinAction #TechTalk #AIandHumans

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Data Governance Act explained The economic and societal potential of #data is enormous: it can enable new products and services based on novel technologies, make production more efficient, and provide tools for combatting societal challenges. In the area of #health, for example, #data can contribute to providing better #healthcare, improving personalised treatments and helping cure rare or chronic diseases. It is also a powerful engine for #innovation and new jobs, and a critical resource for start-ups and SMEs. However, this potential is not being realised. #data sharing in the EU remains limited due to a number of obstacles (including low trust in #data sharing, issues related to the reuse of public sector #data and #data collection for the common good, as well as technical obstacles). In order to truly capitalise on this enormous potential, it should be easier to share #data in a trusted and secure manner. The Data Governance Act (DGA) is a cross-sectoral instrument that aims to regulate the reuse of publicly/held, protected #data, by boosting #data sharing through the regulation of novel #data intermediaries and by encouraging the sharing of #data for altruistic purposes. Both personal and non-personal #data are in scope of the DGA, and wherever personal #data is concerned, the General Data Protection Regulation (GDPR) applies. In addition to the GDPR, inbuilt safeguards will increase trust in #data sharing and reuse, a prerequisite to making more #data available on the market. #data #health #healthcare #innovation

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RBQM Silence: Every Speaks, less Follows- Why or Why not? Key Factors! #clinicaltrials #clinicalresearch #rbqm #rbm #data #innovation #healthcare #data #biometrics CuMinds Innovations

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It is this time of the year when the Stanford Institute for Human-Centered Artificial Intelligence (HAI) release its report. So what's in the 2024 AI-Index report? 📌 #AI has surpassed human performance on some benchmarks, such as in #ImageClassification, #VisualReasoning, and #EnglishUnderstanding, but still trails behind on more complex tasks like competition-level mathematics [Personal note: I read recently that some LLMs could do Olympiad-level maths ...], visual commonsense reasoning, and planning. 📌 Industry continues to dominate frontier AI research, producing 51 notable machine learning models in 2023 compared to only 15 from academia. There were also 21 notable models resulting from industry-academia collaborations. 📌 The training costs of state-of-the-art AI models have reached unprecedented levels, with OpenAI's GPT-4 estimated to have used $78 million worth of compute and Google's Gemini Ultra costing $191 million. 📌 The United States leads China, the EU, and the U.K. as the source of the most top AI models, with 61 notable models originating from U.S.-based institutions in 2023. 📌 There is a significant lack of standardization in responsible AI reporting, as leading developers use different benchmarks to test their models, complicating efforts to systematically compare risks and limitations. 📌 Private investment in generative AI surged in 2023, nearly octupling from 2022 to reach $25.2 billion, while overall AI investment declined slightly. 📌 Studies suggest AI can make workers more productive and improve the quality of their work, but caution that using AI without proper oversight can lead to diminished performance. 📌 AI has accelerated scientific progress, with the launch of significant applications like #AlphaDev for more efficient algorithmic sorting and #GNoME for materials discovery. 📌 The number of AI-related regulations in the U.S. has risen significantly, with 25 such regulations in 2023 compared to just one in 2016 [Personal note: not to mention the AI-Eu Act] 📌 People globally are more cognizant of AI's potential impact and more nervous about it, with 52% expressing nervousness toward AI products and services, up 13 percentage points from 2022. Enjoy the read! https://lnkd.in/eFy4NEKZ

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The KGARevion model showcases a groundbreaking method in AI, combining language models with knowledge graphs for improved medical reasoning. - Merges language models (LLMs) with knowledge graphs. - Enhances accuracy by combining creative generation with structured validation. - Employs triples and embeddings for dynamic feedback. - Refines LLM outputs based on verified medical knowledge. - Improves contextual understanding for complex medical inquiries. - Potential for revolutionizing other knowledge-intensive fields beyond medicine. How might this integrated approach impact other domains reliant on accurate and complex data analysis? #AI #llm #DataIntegration #knowledgegraphs #medicalai

Excellent article by my colleague Julia Sundari, highlighting the significance of sustainability in clinical trials and how intentional digital adoption can enhance it. If you are exploring digital solutions in this space, this is a must-read. Consider leveraging your investment to further your green initiatives. #clinicaltrials #sustainability #digitaladoption #appliedclinicaltrials

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Another potential of AI to revolutionise dementia diagnosis and treatment, bringing us one step closer to better patient outcomes worldwide! Dementia poses a significant global healthcare challenge, affecting over 55 million people worldwide and costing an estimated $820 billion annually. The number of cases is expected to almost treble over the next 50 years. The main cause of dementia is Alzheimer’s disease, which accounts for 60-80% of cases. Early detection is crucial as this is when treatments are likely to be most effective, yet early dementia diagnosis and prognosis may not be accurate without the use of invasive or expensive tests. A team led by Cambridge scientists has developed an AI machine learning model capable of predicting the progression of Alzheimer’s disease in individuals with mild memory and thinking problems. This model outperforms existing clinical diagnostic tools in accuracy. "AI models are only as good as the data they are trained on. To ensure ours is viable for healthcare settings, we trained and tested it using routinely-collected data from both research cohorts and actual memory clinic patients. This demonstrates its potential for real-world application.” - Professor Zoe Kourtzi, University of Cambridge

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Where Data-Driven Decision-Making Can Go Wrong Five pitfalls to avoid By Michael Luca and Amy C. Edmondson Published in Harvard Business Review, September–October 2024 Decision-making and problem-solving Summary by the authors “When considering internal data or the results of a study, often business leaders either take the evidence presented as gospel or dismiss it altogether. Both approaches are misguided. What leaders need to do instead is conduct rigorous discussions that assess any findings and whether they apply to the situation in question. Such conversations should explore the internal validity of any analysis (whether it accurately answers the question) as well as its external validity (the extent to which results can be generalized from one context to another). To avoid missteps, you need to separate causation from correlation and control for confounding factors. You should examine the sample size and setting of the research and the period over which it was conducted. You must ensure that you’re measuring an outcome that really matters instead of one that is simply easy to measure. And you need to look for—or undertake—other research that might confirm or contradict the evidence. By employing a systematic approach to the collection and interpretation of information, you can more effectively reap the benefits of the ever-increasing mountain of external and internal data and make better decisions.” https://lnkd.in/eBvcdmA2 #Data #DataDriven #DataDrivenDecision #DataDrivenDecisionMaking #DataApproach #DataAnalysis #DataOutcome #DataDecisions #DecisionMaking #ProblemSolving

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2024 was a year of transformation, growth, and innovation! 🚀 لقدكان عامامليئا بكرم الله ولطفةوستره فالحمدلله على ماتبقي وعلى ما أتى وعلى ماهو قادم والحمدلله دائما وابدا It taught me that healthcare isn't just about algorithms it's about bridging AI innovation with real-world pharma impact. 🌟 𝐇𝐞𝐫𝐞’𝐬 𝐡𝐨𝐰 𝐢𝐭 𝐚𝐥𝐥 𝐮𝐧𝐟𝐨𝐥𝐝𝐞𝐝: 🌟 𝐊𝐞𝐲 𝐌𝐢𝐥𝐞𝐬𝐭𝐨𝐧𝐞𝐬 🌐 Reached 111,921 views sharing insights on AI and healthcare innovation! 📝 Published my first research paper on Value Innovation Strategy in pharma, introducing new frameworks for strategic decision-making. 🎓 Achieved 15 certifications in AI & leadership, including IBM AI Academy and Microsoft's Generative AI programs. 📊 Led an analytics transformation that boosted sales by 25% through smarter, data-driven decisions. 🔥 𝗧𝗼𝗽 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝘆 𝗠𝗼𝗺𝗲𝗻𝘁𝘀 1️⃣ "First Research Publication" How Value Innovation Strategy is shaping the pharmaceutical market. 👉 "Challenges, learning, and growth are what make the journey worthwhile." Read more here "https://lnkd.in/dkS3NT-c". 2️⃣ "AI Empowerment at EPSF Congress" Sharing AI knowledge with future pharmacists. 👉 "Every idea shared is a step closer to progress." Catch the highlights!"https://lnkd.in/dDfq7puQ" 3️⃣ "ChatGPT-5 Predictions" Analyzing AI’s future capabilities and their impact. 👉 "We aren’t ready for what’s coming." Get the insights here."https://lnkd.in/dKfjYuaP" 🔭 𝐋𝐨𝐨𝐤𝐢𝐧𝐠 𝐀𝐡𝐞𝐚𝐝 𝐭𝐨 𝟐𝟎𝟐𝟓 Next year is all about scaling impact Transforming pharma and healthcare analytics through AI while continuing to share knowledge and insights. The convergence of AI and healthcare opens up endless opportunities for innovation, and I’m excited to be at that intersection! To our growing community of healthcare innovators and AI enthusiasts T♥H❤️A♥N❤️K♥❤️ ♥Y♥O♥U for engaging, challenging, and growing with me. 🙌 Together, we’re rewriting the future of healthcare, one line of code at a time. 💻💡 Let’s keep building. The best is yet to come! 🌟 #LinkedInRewind #2024Wrapped #AI #HealthcareInnovation #DataAnalytics #GrowthJourney

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"Data is the fuel AIs run on, you should be aggressively collecting any data you can own" This guidance was shared during the AI session at Impatient Health's Pharmageddon in Amsterdam last month, and it resonated with me because there is one source of rich customer data and insights that, as an industry, we are ignoring and allowing to evaporate lie rain from a hot tin roof - the insights field teams uncover in their discussions with HCPs. For commercial teams these will be insights around the source of business or growth opportunity in front of them, such as the number of target patients seen, current treatment choices, treatment goals and treatment challenges. The insights field medical uncover will be far wider in scope, but both sets of insights, regardless of the value to future AI implementations, should captured now so they can inform strategy, segment customers, drive the adoption ladder, track sentiment and KPIs over time and critically, supercharge your omnichannel engagements with those HCPs, enabling much more impactful, personalised, needs based and value adding engagements. Here's my challenge to you, if your best rep or MSL moved to a competitor tomorrow, what insights or value (beyond who they saw and how often) is left in your CRM for their replacement to leverage with their customers? IT IS TIME FOR A CHANGE IN HOW WE CAPTURE, SHARE AND ACT ON FIELD DERIVED CUSTOMER DATA AND THE INSIGHTS IT DELIVERS - DATA IS A COMMERCIAL ASSETT If you would like to know how we can help you enable and empower your field team to capture and act on insights as well as how the data can supercharge your omnichannel campaigns, then come see us on the twentyeightb (28b Ltd) stand at next month’s Veeva Systems EU Commercial Summit, or, if you are not lucky enough to be attending, then ping me on here and we can set up some time together. Hope to see you at the summit or on a call soon. #pharmamarketing #pharmasales #crm #data #ai

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Why Don’t Scientists Apply the Scientific Method to #AI Results? Scientists are trained to approach new problems with rigorous methodology and an open mind. Yet, when AI-generated insights challenge established beliefs, many hesitate to apply the same scientific scrutiny they would to traditional hypotheses - my experience is that AI-generated results often face heightened skepticism purely because they originate from artificial intelligence, and not from human intuition. In medicinal chemistry, for example, chemists often rely on intuition—a “feel” for when a molecule seems promising. But intuition is subjective and limited by personal experience. Considering the vastness of the druggable chemical space — estimated at 10^{60} compounds — even the most experienced chemist has explored only a tiny fraction of what’s possible. AI algorithms can navigate this immense landscape with less bias (not completely without but it's getting there! 😜), proposing novel molecules that might defy conventional wisdom. However, these AI-generated suggestions often face skepticism instead of being evaluated with rigorous scientific methods. Is the mathematical foundation of AI any less scientific than traditional approaches? If we’re committed to the scientific method, shouldn’t AI-derived results be subjected to the same experimental validation? Embracing AI isn’t about replacing human expertise but enhancing it. By adopting an AI-inclusive mindset, scientists can expand their exploration and accelerate discoveries. Bridging the gap between AI and bench scientists involves education, collaboration, and a willingness to apply the scientific method consistently — regardless of the source of the hypothesis. After all, the essence of science is to remain open to new approaches that push the boundaries of what’s possible. #AIinScience #ScientificMethod #MedicinalChemistry #ArtificialIntelligence #DrugDiscovery #ScientificRigor #AIinResearch #EmbracingAI #Innovation #OpenMindset #AIDiscovery #ScienceAndTechnology #Chemistry #AIInsights #FutureOfScience

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When working with AI, there are fundamental elements to an AI solution. There are two approaches: Model-centric approach - Operations with data only occur once - Data quantity is more important than data quality - The model is the primary focus Data-centric approach - Data is constantly evolving - Prioritize data quality over quantity - Effective data processing is crucial Data-Centric Approach > Model-Centric Approach If you want to learn more about Big Data and AI - the link to my free newsletter is in the comments below.

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We often talk about AI revolutionizing clinical trials, but let's not forget the human element. A 1989 study (PMID: 2676340) examined two data collection methods in clinical trials. Surprisingly, they found 22% of data discrepancies were due to how people interpreted the questions on standardized forms, even with provided guidelines! This highlights a challenge that persists even in our age of AI and EDC. While technology can automate tasks and improve efficiency, human cognition and interpretation still play a crucial role in data accuracy. So how can we bridge this gap? How do we best leverage both technological advancements and human expertise to ensure the highest quality data in clinical trials? What strategies should we employ to optimize the interplay between humans and technology in our clinical trials? #AIinClinicalTrials #DataQuality #ClinicalDataScience #ClinicalDataQuality #ClinicalTrials #DataHistory

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