Ankita Negi

Full digitization could improve NHS by 20%. Unlocking UK health data would transform UK and global health progress and outcomes and unlock huge value for UK economy #ProjectEqual with Jaan Nellis Francisco Ruiz A Vahid Mojtahed, PhD aims to identify undiagnosed patient populations around the world using AI, #syntheticdata and NHS open source data as a global ‘spine’ in a anonymized and privacy compliant fashion.

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Egos are the biggest challenge to NHS tech adoption, says Tang - Digital Health Egos are the primary barrier to technology adoption in the NHS, asserts Ming Tang, Chief Data Analytics Officer at NHS England. Speaking at the Healthcare Excellence Through Technology event, she emphasized the need for leaders to embrace common solutions over individual preferences. The NHS aims to implement a single patient record, starting with maternity care, to enhance data accessibility and interoperability. Tang highlighted that overcoming ego-driven resistance is crucial for collaboration and innovation, especially during challenging times. Ultimately, successful tech integration hinges on aligning hearts and minds with the 10-year health plan. #NHS #HealthTech #DigitalTransformation #Interoperability #PatientCare #DataAnalytics #HealthcareInnovation ai.mediformatica.com #singlepatientrecord #data #health #digital #maternity #technology #10yearhealthplan #analytics #chiefdigitalinformation #chiefdigitalinformationofficer #dataanalytics #dataanalyticsofficer #digitalhealth #healthit #healthtech #healthcaretechnology @MediFormatica (https://buff.ly/VvbHSQY)

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#HealthTechNews: Funding for #NHS tech may hit a snag - capital spending could prioritise hardware over digital tools, potentially slowing down AI roll-out. For health-tech providers and hospital IT teams, it underscores how critical thoughtful investment strategy is. Rolling out AI, ambient-voice tech or data platforms requires more than cash, it needs alignment of infrastructure, governance and long-term planning. If we want smarter, safer, scalable digital care, we have to make sure budgets support more than just the “plumbing.” https://buff.ly/pgb6YjZ #HealthTech #DigitalHealth #AIinHealthcare #HealthcareIT #Interoperability

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🔬 Exciting Shift in Medical AI: From Narrow Solutions to Foundation Models As AWS's Global Chief Medical Officer, Dr Rowland Illing is witnessing a fascinating evolution in healthcare AI. While we've seen over 1,000 FDA-approved AI medical devices, mostly focused on specific conditions, the industry is now embracing broader, more versatile foundation models. 🚀 At Amazon Web Services (AWS), we're partnering with healthcare giants like Illumina, Johnson & Johnson Medtech, Medtronic, and Abbott to harness this technology. The cloud is proving essential for deploying AI at scale. ☁️ 📈 Exciting times ahead! Read more here. 👉 https://go.aws/44vbYJC #AWSforHealthcare #Healthcare #DigitalHealth #generativeAI #DigitalTransformation

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💡 “Imagine the ideal healthcare system…” Alex Zhavoronkov brings the big ideas talking to Thomas Clozel in our recent podcast, covering how AI is changing pharma and what it takes to make it in AI healthcare. 25 mins long. Watch here. https://lnkd.in/eqRgs4UA

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Today, I saw firsthand how Dragon Copilot will help physicians across the NHS spend more time on patient care and less on paperwork. The work at Manchester University NHS Foundation Trust is a powerful example of how tech can be used by clinicians to focus on what matters most. https://lnkd.in/g8UbcBR7

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The latest in our series of explainers for popular topics in lifesciences, artificial intelligence and regulation covers the many ways in which AI is being used to plan and deliver more effective clinical trials, irrespective of whether the technology under investigation is software, hardware or drug. Get up to speed on all the key trends in less than 5 minutes in this brilliant summary from Romilly Life Sciences' Lilian Hall. Life Sciences Hub Wales Academy of Medical Sciences ABHI CERSI-AI – The Centre of Excellence for Regulatory Science & Innovation in AI & Digital Health UK CEiRSI - The UK Centre of Excellence on in-silico Regulatory Science and Innovation Work Experience Cardiff University

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Everyone's fundraising for healthcare AI. Almost no one is fundraising for healthcare AI adoption. That's backwards. We see the same pattern play out in hospitals everywhere. In the boardroom, the pilot looks perfect. The accuracy scores are high. The integration is clean. But walk down to the hospital three months later, and the reality is different. The platform has become shelfware. Why? It’s not because the technology failed. It’s because the clinicians are drowning. They don't have the bandwidth to learn "just one more system" while managing a full patient load. They aren't resisting innovation; they are just trying to survive the shift. 𝐇𝐞𝐚𝐥𝐭𝐡𝐜𝐚𝐫𝐞 𝐢𝐬𝐧’𝐭 𝐒𝐚𝐚𝐒. You can't ship a login, send a Loom video, and walk away. These are professionals making life-and-death decisions, already buried in documentation. They don't need another platform. They need someone standing next to them, making sure the technology actually fits their reality. That's expensive. It's slow. It doesn't scale the way investors want. 𝐁𝐮𝐭 𝐢𝐭’𝐬 𝐭𝐡𝐞 𝐨𝐧𝐥𝐲 𝐭𝐡𝐢𝐧𝐠 𝐭𝐡𝐚𝐭 𝐰𝐨𝐫𝐤𝐬. If your healthcare AI company doesn't have more implementation people than engineers, you're not serious about adoption. The companies that win won't have the best algorithms. 𝐓𝐡𝐞𝐲’𝐥𝐥 𝐛𝐞 𝐭𝐡𝐞 𝐨𝐧𝐞𝐬 𝐰𝐡𝐨 𝐬𝐡𝐨𝐰𝐞𝐝 𝐮𝐩. #HealthcareAI #DigitalHealth #HealthTech #GCCHealthcare #ClinicalWorkflow Boston Health AI

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Thought-provoking session at the 𝗟𝗮𝗸𝗲 𝗡𝗼𝗻𝗮 𝗜𝗺𝗽𝗮𝗰𝘁 𝗙𝗼𝗿𝘂𝗺 exploring the 𝗡𝗲𝘄 𝗙𝗿𝗼𝗻𝘁𝗶𝗲𝗿𝘀 𝗼𝗳 𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆 𝗮𝗻𝗱 𝗟𝗶𝗳𝗲 with Christina Farr, Peter Lee, Lorraine Bardeen, and Lerrel Pinto. The discussion spanned everything from the rapid evolution of generative AI to the emergence of embodied AI, robots that can perceive, move, and interact with the physical world. What struck me most was the reminder that today’s breakthroughs build on decades of progress in AI, motor intelligence, coordination, balance, and movement — now combined with advances in visual and language intelligence. The next frontier: reasoning. As Gloria Caulfield, President of the Lake Nona Institute, aptly noted, what once lived in the realm of science fiction is rapidly becoming science fact. In healthcare, we’ve been quietly laying the same foundation: building the digital, data, and AI capabilities that can finally enable Intelligent Health, where technology helps us understand the whole person and move from reactive care to proactive health. #ArtificialIntelligence #FutureOfTechnology #EmbodiedAI #Innovation #LakeNonaImpactForum #LNIF26 #IntelligentHealth

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Say hello to Clarence! Proud to see another strong collaboration between Druid AI & Robotics AI, driving innovation across the healthcare sector.

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Healthcare AI is entering its Foundation Model Era The floodgates just opened for clinical-grade, healthcare-specific AI models...and VCs are rushing in. As Ngai Yeung mentioned in her recent article: 👉 Aidoc raised $150M from General Catalyst and NVIDIA’s venture arm to flag critical findings in imaging exams. 👉 Ash by Slingshot AI: a therapy chatbot trained on real session transcripts just extended its Series A to $93M with backing from a16z. 👉 OpenEvidence landed $210M (yes, B round!) for a search engine trained on medical journals. this was called “the most competitive deal” by a GV investor. What ties these together? *They’re all built on foundational models: LLMs trained not just on text, but on multimodal healthcare data: imaging, audio, notes, EMR dumps, and beyond. These models don’t just summarize: they reason, flag, search, predict, and increasingly... act. And we’re just getting started. Expect foundational model-native startups to pop up across every specialty, especially those with highest cost to payors and lowest consumer access: - MSK - Oncology - Cardiology - Women’s health The next phase? *Agentic workflows built on top of these models. Think ambient scribe → care coordinator → revenue cycle → diagnostics → population health. All wrapped into modular, intelligent assistants. Clinical productivity jumps, staffing headaches shrink, and a new UX layer is born. We’re watching a new platform shift that is purpose-built for healthcare.

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📍 Great Asia AI Summit happening live now: https://lnkd.in/d8T8X3zX Just experienced Multi-Agent Systems: The Foundations Powering Tomorrow’s Enterprise AI with Prof. Michael Wooldridge, at the Great Asia AI Summit 2026. He shared how intelligent systems make decisions, collaborate with humans, and operate in complex enterprise environments. Key Takeaways: 1️⃣ Agents can proactively assist humans by automating tasks and supporting decision-making. 2️⃣ Standards like Model Context Protocol enable smoother interaction between agents and tools. 3️⃣ Agentic systems can coordinate workflows and tackle complex problems at scale. Onward to more ideas, conversations, and inspiration.🌟 #GreatAsiaAISummit #EnterpriseInnovation #FutureOfWork #AppliedTechnology #Leadership Salesforce

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AI bias in healthcare is being managed by standards, regulation and clinical validation working together 👍 #HealthcareAI #AIinHealthcare #AIGovernance #ResponsibleAI #AIethics #DigitalHealth #HealthTech #ClinicalAI #AIRegulation #HealthInnovation #TrustworthyAI #HealthData #AIBias #HealthLeadership

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Rare diseases affect hundreds of millions of people worldwide — yet they've long been underfunded and underdiagnosed. Dr. Rowland Illing, AWS Chief Medical Officer and Director of Healthcare and Life Sciences, explores how AI and cloud are helping close the gap — from whole genome sequencing through Genomics England to open genomic datasets enabling global research collaboration. Learn more on the AWS Blog:

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How important is AI in the healthcare industry? 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. Link to the complete interview in the comments 👇 #data #ai #CommunityLIVE25 #hyland #theravitshow

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The NHS is entering a new era of AI-driven, patient-centred care. As part of the NHS 10-Year Health Plan, we’re seeing a strong push toward integrating AI, robotics, and digital innovation into everyday healthcare delivery. From real-time safety monitoring systems to robotic assistants and AI-supported clinical pathways - the future is no longer on the horizon. It's already here. Recently, WesStreeting explored some of these cutting-edge technologies designed to: 🔍 Detect patient safety issues in real time 🏥 Trigger earlier inspections before harm occurs 💡 Enable faster, more personalised treatments For those of us working in clinical practice, digital health, or medical entrepreneurship, this is a major opportunity to rethink how we deliver care - safer, smarter, and more scalable. As a Physician Associate & healthtech founder, I’m deeply encouraged by this direction. We must continue to invest in the right tools, training, and infrastructure to make this transformation sustainable and equitable. 🔗 Let’s connect if you’re exploring: Health innovation Digital transformation in primary or specialist care AI in dermatology or medical aesthetics #AIinHealthcare #DigitalHealthUK #NHSInnovation #PatientSafety #HealthTech #WesStreeting #MedicalLeadership #10YearPlan #HealthPolicy #PhysicianAssociate #TechForGood #NHSReform #SkincareTech #FellySaas #WomenInHealthTech

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Great insights, this is a space that’s moving fast. The demand is there. The momentum is real. But capturing it is where things get difficult. FemTech sits at the intersection of consumer health, clinical validation, employer benefits and insurance reimbursement. The user isn’t the buyer, the buyer isn’t always the decision-maker, and adoption is driven by outcomes, not features. At the same time, the market is shifting fast, from fragmented solutions to integrated health platforms, from wellness to clinical-grade diagnostics and therapeutics, from DTC to employer and insurer-led models, and from reactive care to predictive, AI-driven prevention. Where I see most companies struggle is not demand, but translation. Translating early DTC traction into scalable revenue. Translating product engagement into measurable outcomes. Translating innovation into something a payer or employer will actually fund. And most importantly, aligning product, clinical validation and GTM before trying to scale. #FemTech #HealthTech #WomensHealth #GoToMarket #HealthcareInnovation

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