AI in Healthcare Innovation

Ambient AI is no longer a future concept in healthcare, it’s already reshaping how care is delivered. AI-enabled clinical documentation is changing how physicians experience technology, making it feel supportive rather than burdensome. By reducing the administrative load of documentation, clinicians can spend more time practicing medicine instead of managing systems. At the same time, clinical documentation, which has long been a source of friction, burnout, and risk, has the potential to become a powerful source of real-time clinical insight. At Elevance Health, we’re focused on applying digital technologies, such as ambient and clinical insights - responsibly - not just to document care, but to enable earlier intervention, better coordination, and more effective cost management. Several principles guide our approach: 🚣 Move upstream: Embed payer intelligence, such as risk signals and care gaps, directly into clinical workflows rather than surfacing insights after the fact. 🕵 Focus on moments that matter: Earlier detection of risk allows action before acute events occur. 🩺 Keep humans in the loop: AI should support clinical decision-making, not replace clinical judgment. 🔃 Reduce friction, not add it: Seamless data flow means less manual work for providers and faster, more comprehensive care. By integrating real-time clinical documentation with actionable insights, ambient AI can help surface relevant information at the moment of care, supporting more comprehensive diagnosis, improved coordination, and more affordable outcomes without increasing burden or compliance risk. The opportunity ahead isn’t about adding more AI tools. It’s about turning data into action at the right time, in the right workflow, for the right member. I look forward to continued collaboration across payers, providers, and technology partners as we shape what responsible, AI-enabled healthcare should look like.

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.

The impact of technology becomes real when it improves the lives of those you care about. It’s the case of the collaboration between Roche and IBM that puts #AI to work to help diabetes patients better manage their condition.   The Accu-CheckSmart Guide Predict app combines continuous glucose monitoring sensors with predictive algorithms powered by AI to assist with glycemic control and reduce the risk of hypoglycemia or hyperglycemia.   Being able to anticipate potential adverse events hours before they happen allows people with diabetes to take preventive measures. For example, one feature that I find fascinating is “Night low predict”, a night guardian that estimates the likelihood of hypoglycemia during the 7-hour window of a patient’s sleep, so they can consider eating a bedtime snack before falling asleep. That seemingly simple gesture can significantly impact diabetes management, offering peace of mind and improved quality of life for patients.   Find out more about this cross-industry partnership and how European-born digital innovation can transform patient care: https://lnkd.in/d3mcWcRA

The future of elder care hinges on innovation. I know this first hand, and I lost my mother over a year ago. Through my experience caring for my mom, I saw how AI can transform how we support our aging population. Here’s how AI can revolutionize care for the elderly: 🤖 Personalized Care at Scale: AI analyzes health data to create customized care plans. This means better health outcomes tailored to each individual’s unique needs. 🏡 Promoting Independence: Smart home technologies powered by AI help seniors live independently longer. From fall detection to medication reminders, AI supports seniors in their desire to live independently longer and facilitates daily living. 👥 Reducing Caregiver Burden: AI tools can take over routine tasks, freeing up caregivers to focus on what matters most—human connection and emotional support. 🩺 Proactive Health Monitoring: AI tracks vital signs in real-time, predicting potential health issues before they become serious. Early intervention keeps seniors safer and healthier. 🚶♀️ Empowering Aging in Place: AI-enabled devices assist with mobility, home safety, and social engagement, helping seniors remain in their homes, surrounded by familiarity and comfort. Here’s how you can leverage AI to transform elder care: 🔍 Adopt AI-Powered Tools: Explore AI solutions that offer real-time health monitoring, personalized care plans, and smart home integrations. 🤝 Collaborate with Tech Providers: Work closely with AI developers to ensure that the tools meet the specific needs of the elderly population. 🌐 Educate and Empower: Provide training and resources for caregivers and seniors to integrate AI into their daily routines seamlessly. . 💡 Focus on human-AI collaboration: For the best outcomes, combine AI's strengths with human caregivers' empathy. . Did you know that by 2050, the global population aged 60 and over is projected to double? AI isn’t just an option—it’s essential for future care. Empower independence. Transform care. Embrace AI.

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

AI in healthcare is not simply another technology upgrade. It is a matter of trust, safety, and ultimately, human life. In many sectors, an AI error might lead to inconvenience or financial loss. In healthcare, an AI error can mean a missed diagnosis, an inappropriate treatment pathway, or avoidable harm. That is why AI adoption in healthcare must be held to a higher standard than in almost any other industry. It requires deeper validation, stricter governance, and human guardrails at every stage. A framework I find particularly helpful is 𝐀𝐈 + 𝐑𝐀𝐂𝐓⁣, strengthened through a Human-Centred AI lens. 𝐑 = 𝐑𝐞𝐚𝐝𝐢𝐧𝐞𝐬𝐬⁣ ⁣⁣⁣⁣⁣⁣⁣⁣⁣The risk begins long before deployment. If clinical data is incomplete, biased, or unrepresentative, AI systems can fail quietly, often affecting the most vulnerable populations first. Readiness must include: →Data integrity and provenance →Regulatory compliance →Clear clinical problem definition →Ethical and patient safety accountability 𝐀 = 𝐀𝐝𝐨𝐩𝐭𝐢𝐨𝐧⁣ ⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣ ⁣⁣⁣⁣⁣⁣⁣⁣In healthcare, adoption is not about installing a tool, it is about integrating it into clinical judgment. The risk is over-reliance, alert fatigue, or the introduction of friction into already pressured workflows. Human-centred adoption means: →Clinicians remain firmly in the loop →AI outputs are explainable and challengeable →Training supports human-AI collaboration, not replacement 𝐂 = 𝐂𝐚𝐩𝐚𝐛𝐢𝐥𝐢𝐭𝐲⁣ ⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣ ⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣Healthcare AI is not static. Models drift, populations change, and clinical practice evolves. The risk is that a system that appears safe today may not remain safe tomorrow. Capability requires: →Continuous monitoring and evaluation →Governance structures spanning clinicians, data, ethics and risk →Ongoing validation, not one-off approval 𝐓 = 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧⁣ ⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣ ⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣True transformation is not automation for its own sake. The risk of scaling without safeguards is amplified inequity, diminished patient trust, and decision-making that feels outsourced. Transformation must prioritise: →Better patient outcomes and experience →Equity across communities →Shared decision-making, supported, not replaced, by AI The central truth is this: 𝐇𝐞𝐚𝐥𝐭𝐡𝐜𝐚𝐫𝐞 𝐀𝐈 𝐢𝐬 𝐧𝐨𝐭 𝐜𝐨𝐧𝐬𝐮𝐦𝐞𝐫 𝐭𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲. 𝐈𝐭 𝐢𝐬 𝐬𝐚𝐟𝐞𝐭𝐲-𝐜𝐫𝐢𝐭𝐢𝐜𝐚𝐥.⁣ ⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣ ⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣Progress must be ambitious, but responsibility must be uncompromising. The question is not whether AI will shape the future of care. It is whether we shape it with the rigour, humility, and human focus that patients deserve. What is the single most important gate check you insist on before scaling AI in clinical environments? ♻️ Share if this resonates ➕ Follow (Jyothish Nair) for reflections on AI, change, and human-centred AI #ResponsibleAI #AI #DigitalTransformation #HumanCentredAI

Did you see the recent news??? Microsoft recently unveiled its latest AI Diagnostic Orchestrator (MAI DxO), reporting an impressive 85.5% accuracy on 304 particularly complex cases from the New England Journal of Medicine, compared to just ~20% for physicians under controlled conditions . These results—quadrupling the diagnostic accuracy of human clinicians and more cost-effective than standard pathways — have gotten a lot of buzz. They may mark a significant milestone in clinical decision support and raise both enthusiasm but also caution. Some perspective as we continue to determine the role of AI in healthcare. 1. Validation Is Essential Promising results in controlled settings are just the beginning. We urge Microsoft and others to pursue transparent, peer reviewed clinical studies, including real-world trials comparing AI-assisted workflows against standard clinician performance—ideally published in clinical journals. 2. Recognize the value of Patient–Physician Relations Even the most advanced AI cannot replicate the human touch—listening, interpreting, and guiding patients through uncertainty. Physicians must retain control, using AI as a tool, not a crutch. 3. Acknowledge Potential Bias AI is only as strong as its training data. We must ensure representation across demographics and guard against replicating systemic biases. Transparency in model design and evaluation standards is non-negotiable. 4. Regulatory & Liability Frameworks As AI enters clinical care, we need clear pathways from FDA approval to liability guidelines. The AMA is actively engaging with regulators, insurers, and health systems to craft policies that ensure safety, data integrity, and professional accountability. 5. Prioritize Clinician Wellness Tools that reduce diagnostic uncertainty and documentation burden can strengthen clinician well-being. But meaningful adoption requires integration with workflow, training, and ongoing support. We need to look at this from a holistic perspective. We need to promote an environment where physicians, patients, and AI systems collaborate, Let’s convene cross sector partnerships across industry, academia, and government to champion AI that empowers clinicians, enhances patient care, and protects public health. Let’s embrace innovation—not as a replacement for human care, but as its greatest ally. #healthcare #ai #innovation #physicians https://lnkd.in/ew-j7yNS

Skin cancer is the most common cancer in the United States, and its early and accurate detection is essential for successful outcomes. But it can take anywhere from three to six months for a patient to get a clinical assessment from a dermatologist, meaning that ~2/3rds of cases are first managed in primary care. That bottleneck – partly caused by the rise of cosmetic procedures like botox that compete for dermatologist time – is highly problematic, not least because skin disease issues, while common, can be tricky to diagnose. It’s estimated that non-specialist physicians misdiagnose ~50% of cases because they don’t recognize them. This is where Techstars portfolio company Piction Health™ comes in. Founded in 2019, by Susan Conover – a skin cancer survivor who trained as a mechanical engineer before moving into management consulting – and Pranav Kuber, who has over 15 years experience in software R&D, including artificial intelligence (AI), at IBM, Intel, and ByteLight, Piction Health is a virtual dermatology provider that uses dermatologists alongside artificial intelligence. Skin cancer detection is a medical field where the use case for AI has been compelling for some time. “Through the analysis of large datasets, AI algorithms have the capacity to classify clinical or dermoscopic images with remarkable accuracy.” (Source: Journal of Investigative Dermatology.) Piction’s founders have built the largest, most actionable database globally of photos of skin diseases, across all skin tones. That in turn has enabled the team to develop AI that’s on-par with dermatologists in evaluating skin conditions. By incorporating AI into their workflow, Piction’s dermatologists can manage 15X as many patients as an in person clinician. With a five year target of building the largest dermatology practice in the world, the startup has grown an average of 50% month-over-month in patients and revenue since going live in January 2023. The team’s mission is to ensure that anyone can access expert care when they need it most. Having been diagnosed with melanoma for the first time when she was 22, Susan knows all too well that speedy access to a dermatologist is critical. “I was told it would take three months to get in to see a dermatologist,” she says. “I ended up being diagnosed with stage 2 melanoma by my primary care doctor.” If that physician hadn’t had the foresight to take a biopsy of her mole and accelerate the process, she knows she may not be around today. “We founded Piction Health to make sure that doesn’t happen to anyone else,” she says. #startups #founders #Entrepreneurs #healthtech #healthtechnology #digitalhealth #AI #dermatology https://lnkd.in/ekRKf8kE

FDA rolls out generative AI tool ‘Elsa’ to speed up reviews and streamline regulatory tasks >> 💊The FDA is rolling out Elsa, a secure generative AI tool that helps staff accelerate clinical reviews, summarize adverse events, compare drug labels, and even generate code for internal systems 💊Elsa is built on a large language model and housed in a high-security GovCloud environment, ensuring sensitive regulatory data stays in-house and not trained on by external models 💊Early results from pilot testing with FDA scientific reviewers were positive, leading to the accelerated, under-budget deployment across all centers (original target launch date was June 30th) 💊Elsa’s debut is seen as the first step in a broader AI integration strategy that will expand to include advanced analytics and further generative AI use cases 💊FDA leadership is positioning AI as a lever to boost performance without compromising scientific rigor, describing Elsa as a tool that “enhances and optimizes the potential of every employee.” 💊Elsa launches amid a proposed 4% FDA budget cut and loss of up to 3,500 staff, potentially helping offset pressure on review timelines #digitalhealth #ai #pharma

Patients are changing fast. Healthcare must change faster. The old model is dead. Today’s patients are not waiting in line. They are searching, clicking, and asking AI for answers—before they ever see a doctor. One in four UK patients already use generative AI for health advice. Nearly a third would rather ask AI or social media than wait for a clinician. This is not a threat. It is a signal. Digital curiosity is the new front door to care. The best healthcare leaders see this as a chance to build something better. Not more apps. Not more portals. But a true bridge—where technology and empathy work together. Here’s the new playbook for Connected Care: 1/ Welcome the digital first step • Treat every online search, chatbot, or AI query as the start of the care journey. • Build systems that catch these signals and guide patients into real care, not dead ends. 2/ Make AI a bridge, not a barrier • Use AI to handle admin, triage, and routine questions. • Free up clinicians to spend less time on screens, more time in eye contact. • Let AI reduce friction, but never erode trust. 3/ Design for transparency and control • Give patients clear, simple access to their records, appointments, and care plans. • Let them see the whole journey, not just the next step. • Make them feel like part of the team, not just a case number. 4/ Connect the dots, break the silos • Stop building one-off tools that don’t talk to each other. • Create platforms where every digital touchpoint feeds into a single, human-centered care experience. 5/ Build trust at every step • Use technology to inform, not overwhelm. • Keep the human touch at the center, even as AI does more heavy lifting. This is not theory. This is the new roadmap for healthcare. When you treat AI-curiosity as the entry point—and connect it to a seamless, human care journey—you unlock the future. Your patients are already digital. Your care model must be, too. The future belongs to those who connect, not those who compete. Build the bridge. Welcome the search. Lead the change. Here's the link to the report: https://lnkd.in/eUfJ7aab Semble Christoph Lippuner Mikael Landau