AI in Healthcare Innovation

Right now, every CEO is wondering the same thing: “How can artificial intelligence help maximize our impact?”   Delivering on the promise of AI isn’t just good business, it has the potential to help us address some of society’s most pressing challenges. So today, I wanted to offer a closer look at how AI is helping us discover new medicines at Novartis.   The process of identifying a new drug, running patient clinical trials, and bringing it to market takes over a decade. Each new medicine costs on average $2 billion to develop, and we know nearly 9 in 10 of the treatments we work on will fail before they ever reach patients.   A major early step in that process is identifying individual targets in the body that we want to design a drug for. Once we identify that target, which most commonly is a protein, we look for molecules that might address the target’s underlying issue – ultimately those molecule structures form the basis for every successful treatment.   Unlocking the right protein and molecular structures is complex stuff – each step often takes years to get right and our scientists consider billions of potential chemical structures that might lead to effective and safe drug candidates.   AI offers us the chance to accelerate that process. Working with partners at Isomorphic Labs – including members of the Google DeepMind team that were awarded the Nobel Prize this year – we’re now able to do things like model how a protein folds and interacts with the molecules we design. AI models also make it possible for us to analyze different chemical structures simultaneously. It has the potential to add up to significant time savings for our drug development scientists and their work to predict what molecules might treat specific diseases better and faster.   We’re just at the beginning of what this technology can do. As we incorporate AI throughout Novartis’ work, I’m excited to see all the ways it helps us unlock the mysteries of human biology, so we can deliver better medicines that improve and extend patients’ lives.

This paper explores how AI is shifting from a promising concept to practical application in clinical medicine, highlighting its transformative potential, existing limitations, and future needs. 1️⃣ AI now rivals expert clinicians in diagnostic tasks—deep convolutional neural networks match dermatologists in classifying skin lesions, and ML improves cancer prognosis prediction accuracy. 2️⃣ LLMs like ChatGPT support emergency care decisions, generate clinical notes, and aid surgical workflows with up to 90% instrument recognition accuracy. 3️⃣ AI enhances operational efficiency by automating documentation, enabling real-time translation, and optimizing EHR management through autoML. 4️⃣ Core limitations include lack of transparency ("black box" AI), bias in training data, poor generalizability, usability gaps in clinical settings, and weak regulatory oversight. 5️⃣ Ethical concerns focus on accountability, clinician overreliance, patient privacy, and informed consent in data use, especially affecting marginalized groups. 6️⃣ Explainable AI (XAI) is essential to gain clinician trust—tools must align with clinical reasoning, not just technical transparency. 7️⃣ Bias mitigation requires more than diverse datasets; adaptive learning and real-time fairness audits are needed for equitable outcomes. 8️⃣ Real-world adoption challenges persist—future studies must evaluate AI’s impact on workload, decision-making, and patient outcomes in dynamic settings. 9️⃣ Regulatory evolution is critical—unlike drugs, AI tools often bypass RCTs. Continuous post-deployment monitoring is needed to ensure safety and accountability. 🔟 The paper calls for interdisciplinary collaboration and deliberate implementation strategies to ensure AI enhances care rather than widens healthcare inequities. ✍🏻 Ariana Genovese, Sahar Borna, Cesar Abraham Gomez Cabello, MD, Syed Ali Haider, Prabha Srinivasagam, Maissa Trabilsy, Antonio Jorge de Vasconcelos Forte. From Promise to Practice: Harnessing AI’s Power to Transform Medicine. Journal of Clinical Medicine. 2025. DOI: 10.3390/jcm14041225 ✅ Sign up for our newsletter to stay updated on the most fascinating studies related to digital health and innovation: https://lnkd.in/eR7qichj

The next generation of drug discovery will be built on integrated, AI-native systems that connect automation, experimentation, computation, and machine learning into a continuous cycle of learning where every experiment makes the next one smarter. A recent article in Scientific American by Patrick Sisson explores how this new research infrastructure is beginning to take shape and highlights Recursion's pioneering work in this space. At Recursion, we run up to 2.2 million experiments each week and leverage more than 50 petabytes of proprietary biological, chemical, and patient data as part of an end-to-end learning engine for drug discovery and development. But scale alone isn't the differentiator. The real opportunity lies in transforming multimodal data into biological understanding and ultimately into new medicines. Take our neuroscience collaboration with Roche and Genentech. For decades, neuroscience drug discovery has been constrained by repeatedly investigating the same well-studied targets. To move beyond those limitations and explore entirely new biology, our teams developed advanced cell manufacturing capabilities to produce more than 100 billion human iPSC-derived microglia, the brain's resident immune cells, which are notoriously difficult to generate and study at scale. The result is a first-of-its-kind whole-genome Microglia Map comprising 46 million cellular images across 17,000 genes. This systems-level view of biology allows our AI models to move beyond traditional approaches, uncover novel biological insights, and identify therapeutic opportunities that may have otherwise remained hidden. What excites me most is what comes next. These maps – and the AI models trained on them – are the foundation. The real opportunity is translating them into novel, first-in-class therapeutic programs. That's the frontier we're pioneering: turning systems-level biological understanding into medicines for patients. There's still important work ahead, but we're making meaningful progress, and I'm excited about what's possible as we continue to push the boundaries of AI-native drug discovery. Stay tuned. #AI #DrugDiscovery #TechBio #Biotechnology #MachineLearning

From CommunityLIVE by Hyland: AI that helps clinicians and patients, today. I sat down with Dr Shankar Sridharan, baby heart doctor, Chief Clinical Information Officer at Great Ormond Street Hospital, and National Clinical Lead for AI at NHS England. We spoke about taking AI out of pilots and into frontline care. Scale and design - The NHS has run the largest generative AI program with ambient voice across nine care settings so far. - 17,000 patients included with a staged, scientific approach. - Phase zero in the innovation lab, then professional actors and doctors in a safe test EHR, then real clinicians with real patients, then scale. Measured outcomes - 25% increase in direct care time. - In Accident and Emergency, documentation time reduced by 51%. - Independent analysis by York Health Economics Consortium shows each A&E doctor can see at least one extra patient per shift. - At NHS scale, that is 9,279 additional patients per day, a capacity benefit north of 650 million with a further documentation benefit of 160 million. Why now - Algorithmic AI needed high digital maturity and often lived in imaging. - Generative AI can work with unstructured data and needs operationalisation more than heavy new infrastructure. How to operationalise - People, process, technology in that order. - Train clinicians. Integrate into workflow. Measure time to decision, accuracy, and rework, not just model scores. Governance and assurance - Build at the speed of trust. Clear governance for data, cyber, and clinical safety, and assurance so the public can see how risk is managed. - Move beyond pilotitis Tiny pilots with no owner and no scale plan slow us down. Create a national plan with strategic delivery, then expand by use case. - What good looks like in practice Ambient voice reduces documentation load so clinicians focus on patients. Better throughput and clinicians who feel less drained at the end of a shift. The next step - Use AI as the tenth voice in multidisciplinary teams. - Not to replace clinicians, but to widen perspective, surface risks, and nudge better decisions. As Shankar put it, the clinician is the superhero. AI is the cape. This is how AI earns trust in healthcare. Results first. Safety always. Scale with discipline. Full interview link in the comments. #data #ai #CommunityLIVE25 #hyland #theravitshow

As a surgeon, I've seen the potential of AI to diagnose diseases and streamline the entire patient journey, from the moment patients walk through the door to the day they're discharged. Imagine a patient arriving at the hospital with a suspected heart condition. Traditionally, this could involve multiple appointments, tests, and specialist consultations, causing delays and potential anxiety for the patient. With AI, this process can be expedited and personalized. Algorithms can quickly analyze medical records, lab results, and imaging scans to identify potential issues, flagging them for immediate attention. AI-powered chatbots can guide patients through the process, answering questions, scheduling appointments, and providing educational resources. For example, AI can help identify patients at high risk of readmission, allowing for proactive interventions and follow-up care that reduces hospital stays and improves outcomes. But AI's potential goes beyond efficiency. It can also enhance the patient experience by: ◾️Personalizing care plans: Tailoring treatment based on individual patient data. ◾️Providing 24/7 support: Offering virtual consultations and access to information anytime. ◾️Empowering patients: Giving them the tools and information they need to actively participate in their own care. I'm excited about AI's possibilities for improving healthcare delivery. By seamlessly integrating AI into the patient journey, we can create a more efficient, effective, and, ultimately, human-centered healthcare system. #AI #healthcare #innovation #patientjourney #efficiency #heart

🤖 As AI tools become increasingly prevalent in healthcare, how can we ensure they enhance patient care without compromising safety or ethics? 📄 This multi-society paper from the USA, Canada, Europe, Australia, and New Zealand provides comprehensive guidance on developing, purchasing, implementing, and monitoring AI tools in radiology to ensure patient safety and ethical use. It is a well-written document that offers a unified, expert perspective on the responsible development and use of AI in radiology across multiple stages and stakeholders. The paper addresses key aspects of patient safety, ethical considerations, and practical implementation challenges as AI becomes increasingly prevalent in healthcare. 🌟 This paper… 🔹 Emphasizes ethical considerations for AI in radiology, including patient benefit, privacy, and fairness 🔹 Outlines developer considerations for creating AI tools, focusing on clinical utility and transparency 🔹 Provides guidance for regulators on evaluating AI software before clearance/approval 🔹 Offers advice for purchasers on assessing AI tools, including integration and evaluation 🔹 Underscores the importance of understanding human-AI interaction and potential biases ❗ Emphasizes rigorous evaluation and monitoring of AI tools before and after implementation and stresses the importance of long-term monitoring of AI performance and safety (this was emphasized several times in the paper) 🔹 Explores considerations for implementing autonomous AI in clinical settings 🔹 Highlights the need to prioritize patient benefit and safety above all else 🔹 Recommends continuous education and governance for successful AI integration in radiology 👍 This is a highly recommended read. American College of Radiology, Canadian Association of Radiologists, European Society of Radiology, The Royal Australian & New Zealand College of Radiologists (RANZCR), Radiological Society of North America (RSNA) Bibb Allen Jr., MD, FACR, Elmar Kotter, Nina Kottler, MD, MS, FSIIM, John Mongan, Lauren Oakden-Rayner, Daniel Pinto dos Santos, An Tang, Christoph Wald, M.D., Ph.D., M.B.A., F.A.C.R. 🔗 Link to the article in the first comment. #AI #radiology #RadiologyAI #ImagingAI

We are proud to present our latest paper on physics-informed AI for drug design appearing in PNAS special issue on machine learning in chemistry . Standard data-driven AI does not work well on examples that are significantly different from training data. This can result in unphysical predictions that are clearly wrong. To limit this type of unphysical result in the realm of drug design we introduced a new machine learning model called NucleusDiff, which incorporates a simple physical idea into its training, greatly improving the algorithm's performance. NucleusDiff ensures that atoms stay at an appropriate distance from one another, accounting for physical concepts such as repellant forces that prevent atoms from overlapping or colliding. Rather than accounting for the distance between every single pair of atoms in a molecule, which would be expensive, NucleusDiff estimates a manifold, and on that manifold, it then establishes main anchoring points to watch, making sure that the atoms never get too close to one another. We predicted binding affinities of a newer molecule that was not included in the training dataset: the COVID-19 therapeutic target 3CL protease. NucleusDiff showed increased accuracy and a reduction of atomic collisions by up to two-thirds as compared to other leading models.

Yesterday, we released MedGemma a open medical vision-language model for Healthcare! Built on Google DeepMind Gemma 3 it advances medical understanding across images and text, significantly outperforming generalist models of similar size. MedGemma is one of the best open model under 50B! How MedGemma Was Trained: 1️⃣ Fine-tuned Gemma 3 vision-encoder (SigLIP) on over 33 million medical image-text pairs (radiology, dermatology, pathology, etc.) to create the specialized MedSigLIP, including some general data to prevent catastrophic forgetting. 2️⃣ Further pre-trained Gemma 3 Base by mixing in the medical image data (using the new MedSigLIP encoder) to ensure the text and vision components could work together effectively. 3️⃣ Distilling knowledge from a larger "teacher" model, using a mix of general and medical text-based question-answering datasets. 4️⃣ Reinforcement Learning similar to Gemma 3 on medical imaging and text data, RL led to better generalization than standard supervised fine-tuning for these multimodal tasks. Insights: - 💡 Outperforms Gemma 3 on medical tasks by 15-18% improvements in chest X-ray classification. - 🏆 Competes with, and sometimes surpasses, much larger models like GPT-4o. - 🥇 Sets a new state-of-the-art for MIMIC-CXR report generation. - 🩺 Reduces errors in EHR information retrieval by 50% after fine-tuning. - 🧠 The 27B model outperforms human physicians in a simulated agent task. - 🤗 Openly released to accelerate development in healthcare AI. - 🔬 Reinforcement Learning was found to be better for multimodal generalization. Paper: https://lnkd.in/dBTiH_cJ Model: https://lnkd.in/dnyxWPju

This study could change how every frontline clinic in the world delivers care. Penda Health and OpenAI revealed that an AI tool called AI Consult, embedded into real clinical workflows in Kenya, reduced diagnostic errors by 16% and treatment errors by 13%—across nearly 40,000 live patient visits. This is what it looks like when AI becomes a real partner in care. The clinical error rate went down and clinician confidence went up. 🤨 But this isn’t just about numbers. It’s a rare glimpse into something more profound: what happens when technology meets clinicians where they are—and earns their trust. 🦺 Clinicians described AI Consult not as a replacement, but as a safety net. It didn’t demand attention constantly. It didn’t override judgment. It whispered—quietly highlighting when something was off, offering feedback, improving outcomes. And over time, clinicians adapted. They made fewer mistakes even before AI intervened. 🚦 The tool was designed not just to be intelligent, but to be invisible when appropriate, and loud only when necessary. A red-yellow-green interface kept autonomy in the hands of the clinician, while surfacing insights only when care quality or safety was at risk. 📈 Perhaps most strikingly, the tool seemed to be teaching, not just flagging. As clinicians engaged, they internalized better practices. The "red alert" rate dropped by 10%—not because the AI got quieter, but because the humans got better. 🗣️ This study invites us to reconsider how we define “care transformation.” It's not just about algorithms being smarter than us. It's about designing systems that are humble enough to support us, and wise enough to know when to speak. 🤫 The future of medicine might not be dramatic robot takeovers or AI doctors. It might be this: thousands of quiet, careful nudges. A collective step away from the status quo, toward fewer errors, more reflection, and ultimately, more trust in both our tools and ourselves. #AIinHealthcare #PrimaryCare #CareTransformation #ClinicalDecisionSupport #HealthTech #LLM #DigitalHealth #PendaHealth #OpenAI #PatientSafety

Google unveils AI-powered healthcare innovations spanning drug discovery, enhanced search, and integrated medical records: 💊In drug discovery, new open AI models (TxGemma) are designed to understand both text and molecular structures to help predict the safety and efficacy of potential therapies 💊An AI co-scientist tool built on Gemini 2.0 assists biomedical researchers by parsing scientific literature, generating novel hypotheses, and proposing experimental approaches 💊These tools will be available through the Health AI Developer Foundations program, aiming to streamline the early stages of drug development 🔎 In search, expanded health knowledge panels now cover thousands more topics and use AI to provide quick, credible answers to health-related queries 🔎 The "What People Suggest" feature aggregates user discussions from online platforms to offer personalized insights based on shared experiences with specific health conditions 🔎 These enhancements support multiple languages, including Spanish, Portuguese, and Japanese, and are initially rolling out on mobile devices in the U.S. 💿The global launch of Medical Records APIs for the Health Connect platform on Android enables apps to read and write standardized medical data, such as allergies, medications, immunizations, and lab results 💿The APIs support over 50 data types, integrating everyday health tracking with official medical records from healthcare providers 👇Links to source articles in comments #DigitalHealth #AI #Google