Mobile Health Applications

AQ AI Health App is one of the most interesting AI healthcare products to emerge from China in the last year. It was launched by Ant Group (the Alibaba affiliate behind Alipay) in 2025 and represents a strategic move from financial services into healthcare. Rather than building another chatbot, Ant has tried to create what it calls an "AI-native healthcare operating system" I have been fortunate to have travelled throughout China, visiting some of their clinics and hospitals in Shanghai, Beijing, Chengdu, and Guangzhou, and the scale and digitalisation are incredible. Visiting the West China Hospital was a real highlight AQ is an AI health assistant designed to accompany users throughout their healthcare journey and combines: AI symptom assessment, Medical report interpretation, Personal health records, Hospital navigation, Appointment booking, Doctor recommendations, Medication guidance, Longitudinal health management AQ is deeply integrated into China's healthcare infrastructure. It connects to over 5,000 hospitals and approximately 1 million physicians across China. It has 30 million monthly active users by January 2026, and more than 10 million health questions are answered every day. AQ aims to be a digital front door to healthcare, and Ant has described it as offering over 100 AI-powered healthcare services rather than a single chatbot. This "multi-agent" approach mirrors the direction many AI companies are now pursuing. In my opinion, China possesses several structural advantages for deploying healthcare AI: Massive patient volumes, highly digital hospitals, Widespread, mobile-first healthcare, integrated payment systems through Alipay, large-scale electronic medical records, and seemingly fewer interoperability barriers than many Western health systems. This allows companies like Ant to embed AI directly into clinical workflows Many Western discussions focus on AI scribes, documentation, or single diagnostic models. AQ instead aims to become the patient's continuous health companion across prevention, navigation, diagnosis support, and follow-up. In June, AQ launched a nationwide campaign to encourage people to lose a combined 50 million kilograms through healthy, science-based weight management. AQ will procure body composition scales and make them available to users at affordable prices. AQ will also upgrade its AI capabilities to serve as a personalised health coach for every user and analyse an individual’s health, including weight, body fat percentage, and muscle mass, and provide tailored exercise and nutrition recommendations based on their specific needs and goals. With these tools in place, AQ will launch a health challenge program featuring incentives designed to keep users motivated, build healthy habits, and help them stay committed to their weight-loss journey. I look forward to seeing the results.

Here is my new newsletter "AI and Healthcare: What’s Working, What’s Not and What’s Next." This is a detailed analysis of how AI is transforming healthcare right now. I discuss more than 70 real-world examples, and seven major themes stand out: 1. Diagnostics & Imaging AI is acting as a “second reader”, detecting cancers, strokes and eye disease with specialist-level accuracy. In some cases it’s cutting treatment times and reducing diagnostic error. 2. Predictive Analytics & Risk Assessment From sepsis and cardiac risk to falls and suicide prevention, AI models are identifying high-risk patients earlier, enabling proactive, preventative care rather than reactive treatment. 3. Personalised Medicine & Drug Discovery AI is accelerating drug design, protein modelling and genetic interpretation. AI-designed drugs are already in clinical trials, and tools like AlphaFold are reshaping biomedical research. 4. Remote Monitoring & Telemedicine AI-powered home monitoring, symptom checkers and other smart tools are extending care beyond hospital walls. 5. Robotic Surgery & Assistance Robotic systems are improving precision in theatre, while AI-enabled assistive robots support logistics, rehab and aged care. 6. Administrative Workflow Optimisation AI scribes, coding tools and hospital command centres are reducing clinician burnout and improving system efficiency. 7. Mental Health Support AI chatbots, crisis triage systems and predictive models are expanding access to mental health care at scale. The key takeaway? AI isn’t replacing clinicians. It’s augmenting capability – and shifting healthcare towards earlier, smarter, more personalised care.

#AI is quietly transforming healthcare for Early Disease Detection. Imagine catching cancer, diabetes, or even rare diseases before symptoms appear! That’s not science fiction. AI-powered tools are already helping doctors spot early warning signs in everything from #breastcancer to #Alzheimer’s, often faster and more accurately than traditional methods. Take #cancer detection: Google’s AI model for #mammograms reduced false positives by 5.7% and false negatives by 9.4% compared to human radiologists. In pancreatic cancer, Harvard Medical School researchers showed AI could predict who’s at highest risk up to three years before diagnosis (https://lnkd.in/dWzFbG_D)) For #rarediseases, AI platforms like Face2Gene and FABRIC GEM INC. are cutting years off the diagnostic journey. (https://lnkd.in/dQyHAU43); (https://lnkd.in/duEWYHHW). In chronic conditions like #diabetes and heart disease, AI-driven wearables and apps are helping patients and clinicians manage care in real time. (https://lnkd.in/dDbjr4UM). The research is booming: a recent review found a surge in AI studies on non-communicable diseases, with top institutions like Harvard and the Ministry of Education of China leading the way. (https://lnkd.in/dhYYxEVy)). Policy is catching up too! The US HHS released its 2025 Strategic Plan for AI in Healthcare, outlining regulatory priorities for safety, transparency, and compliance. (https://lnkd.in/d3--iAmG). But hurdles remain: AI models need diverse, high-quality data to avoid bias and ensure real-world accuracy. Regulatory standards are evolving, and healthcare leaders must balance innovation with patient safety and privacy. The solution? Collaborate early with clinicians, data scientists, and regulators; invest in robust trials; and prioritize transparency and equity. Healthcare leaders: AI isn’t just the future... it’s here now. If you want to explore how to bring these breakthroughs to your organization, connect with me. Let’s shape the next wave of healthcare together.

AI can quietly fix the gaps your clinicians see every day. Mental health waiting lists stretch for months. Women's health concerns get dismissed or overlooked. The system struggles to meet demand. This is where AI-driven platforms step in. Mood tracking tools monitor patterns that might take weeks to surface in traditional therapy sessions. Crisis intervention systems provide immediate support when human resources are stretched thin. Gender-specific health monitoring catches early warning signs that often slip through routine appointments. These platforms offer something your current infrastructure might struggle to provide: accessibility. A woman experiencing postpartum anxiety at 2am gets real-time support. A patient in a rural area tracks symptoms that inform their next specialist visit. Someone hesitant about traditional therapy finds a low-barrier entry point to mental health care. The technology handles what it does best: continuous monitoring, pattern recognition, data collection. Your clinicians handle what they do best: personalized care, complex decision-making, human connection. I spoke with a healthcare administrator last week. She was skeptical about AI in these sensitive areas. After exploring the applications, she realized something important. AI tools free up her clinical team to focus on the patients who need them most. The platforms handle routine monitoring and early intervention. Her specialists tackle the complex cases requiring human expertise. This approach reaches underserved populations who face the biggest barriers to care. It delivers tailored solutions at scale. It turns healthcare from reactive to proactive. The question becomes: how can you integrate these tools to amplify your existing care delivery?

“CBT-based app” is one of the most overused and least useful phrases in digital mental health. As a psychiatry doctor, I see the unmet need every day: long waits for psychiatric care, long waits for talking therapies, and people left trying to manage distress without timely support. I do believe digital health can improve access. But access to an app is not the same as access to therapy. For digital mental health to be clinically meaningful, we need to know what is actually being delivered. Right now, very different interventions are being bundled under the same neat label. One “CBT-based” app might offer mood tracking and psychoeducation. Another might include behavioural activation, exposure therapy, cognitive restructuring, problem-solving, goal setting or habit formation. Same label. Completely different therapeutic ingredients. That is why this new meta-analysis of 169 RCTs in npj Digital Medicine is so important. Instead of treating mental health apps as one broad category, it looks at the specific components that may be driving outcomes. The “active ingredients” might include: • Behavioural activation: scheduling meaningful activities to improve mood • Cognitive restructuring: identifying and challenging unhelpful thoughts • Exposure therapy: gradually approaching feared situations or triggers • Problem-solving therapy: breaking problems into manageable steps • Goal setting and habit formation: supporting structured behaviour change • Psychoeducation and symptom monitoring: learning about symptoms and tracking patterns This is the level of precision the field urgently needs. Not “Is it CBT-based?” But: What is the therapeutic mechanism? For whom? In what context? With what level of human support? And does it work in the real world, where people are busy, distressed, distracted, unsupported, and far more complex than a trial population? The future of digital mental health should not be an endless marketplace of more apps. It should be better evidence, clearer mechanisms, stronger engagement, and measurable clinical benefit. Dr Faith Ndebele | Liz Ashall-Payne | Kate Newhouse, CMgr CCMI | Jo Aggarwal #digitalmentalhealth #psychiatry #mentalhealthinnovation #healthtech

This paper explores the applications of large-scale AI models in medicine, focusing on Medical Large Models (MedLMs), including LLMs, Vision Models, 3D Large Models, and Multimodal Models. 1️⃣ LLMs process clinical text, aiding in electronic health records (EHR) analysis, medical question-answering, and treatment planning. Examples include MedPaLM and MedGPT, which support medical education and diagnostics. 2️⃣ Vision models based on CNNs assist in medical imaging tasks like cancer detection and anomaly detection, achieving dermatologist-level accuracy in skin cancer diagnosis. Vision-Language Models (VLMs) enhance zero-shot learning for medical images. 3️⃣ 3D large models analyze volumetric medical data, aiding in tumor segmentation, virtual surgery simulations, and anatomical modeling for prosthetics. 4️⃣ Multimodal models integrate clinical text, imaging, and genomic data to improve diagnostic accuracy and personalized treatment planning, particularly in oncology. 5️⃣ Graph large models (LGMs) use graph neural networks (GNNs) in medical knowledge graphs, drug discovery, and genomics, aiding in disease risk prediction and biomarker identification. 6️⃣ Drug discovery is accelerated by MedLMs such as AlphaFold and GraphDTA, which predict protein structures and drug-target interactions, improving efficiency in molecular design. 7️⃣ AI-driven models assist in summarizing patient records, generating diagnostic reports, and enhancing clinical documentation, reducing physician workload. 8️⃣ Biomedical image generation using GANs and diffusion models produces high-quality synthetic medical images for data augmentation, improving AI training in pathology and radiology. 9️⃣ AI-driven models enhance precision medicine by integrating multi-source patient data, enabling individualized diagnosis and treatment strategies. 🔟 Challenges include high computational costs, ethical concerns, and potential inaccuracies (AI hallucinations), which limit real-world implementation. ✍🏻 YunHe Su, Zhengyang Lu, Junhui Liu, Ke Pang, Haoran Dai, Sa Liu, Yuxin Jia, Lujia Ge, Jing-min Yang. Applications of Large Models in Medicine. arXiv 2025. DOI: 10.48550/arXiv.2502.17132v1

The war for the next healthcare infrastructure platform has begun. I have been saying it for years: health will be AI-based. Now look at the last few months. → OpenAI launched ChatGPT Health — medical records + wellness apps + personalized AI guidance → Anthropic released Claude for Healthcare — HIPAA-ready enterprise tooling → Google shipped MedGemma 1.5 — open medical models for imaging and speech → NVIDIA launched Clara + BioNeMo — AI for drug discovery, imaging, robotics → Amazon deployed Health AI — agentic assistant with One Medical + Amazon Pharmacy → Microsoft launched Copilot Health — 50,000+ hospitals, 50+ wearables, goal: medical superintelligence Six players. Six health products. All within months. Big Tech is also partnering with Big Pharma: → NVIDIA × Eli Lilly: $1B AI lab for drug discovery → Microsoft × Bristol Myers Squibb: AI-driven lung cancer detection via FDA-cleared algorithms The approaches differ: Consumer-first (Amazon, OpenAI, Microsoft) — building where patients interact, manage meds, book visits. Infrastructure (NVIDIA, Google) — building the compute and models that power everything. Enterprise (Anthropic, Microsoft) — building for providers, payers, and pharma. The potential: → Better-informed health decisions → Earlier disease detection → Faster drug discovery → Less friction across the care journey The risks: → Who controls your health data? → How are commercial decisions made when tech owns the health interface? → Who sets the safety standards — regulators, tech, or pharma? This will be one of the defining battles of the next decade. The infrastructure is being built right now. Health will be AI-based. We are watching the proof form in real time.

Virtual Mental Health Assistants (VMHAs) are changing the game in mental health care. My colleagues at HoloMD are vanguards. Let me explain. For many, accessing mental health support has always been a challenge—scheduling appointments, finding the right therapist, or simply overcoming the stigma. But here’s how VMHAs are making a difference: → 24/7 Availability Unlike traditional therapy, VMHAs are available whenever you need them. Whether it's the middle of the night or early morning, support is just a message away. → Personalized Support These AI-driven assistants learn from each interaction, adapting to provide coping strategies and resources tailored to your unique needs. → Reducing Stigma Anonymity can be powerful. VMHAs offer a judgment-free zone, encouraging people to seek help without fear of judgment. → Consistent Care VMHAs follow established protocols, ensuring users receive reliable, standardized support every time. → Complementing Traditional Therapy While they’re not a replacement for human therapists, VMHAs provide ongoing support between sessions, help track progress, and offer insights that can be shared with therapists. In short, virtual mental health assistants are bridging gaps in mental health care, making it more accessible, personalized, and stigma-free. As technology advances, these tools will play an even more significant role in supporting mental wellness. Could this be the future of mental health care?

Healthcare AI is changing medical practice across multiple critical areas, from diagnostic accuracy to personalized patient care. Recent analysis shows AI applications span eight key domains: disease diagnosis, medical imaging analysis, pharmaceutical research, tailored treatment plans, robotic surgical assistance, digital health records management, clinical research optimization, and epidemic forecasting. Medical professionals are really optimistic about AI's potential to accelerate diagnosis timelines to enhance diagnostic precision, while also improving clinician workflow efficiency and treatment selection accuracy. The technology shows promise in revolutionizing drug discovery processes, enabling more targeted therapeutic interventions, and streamlining administrative healthcare operations through intelligent data management systems. Advanced medical robotics and AI-powered imaging diagnostics are already demonstrating measurable improvements in surgical outcomes and early disease detection rates. Therefore, successful implementation requires careful consideration of patient privacy, clinical validation, and seamless integration with existing healthcare infrastructure. These developments signal an important shift toward data-driven medicine, where AI serves as a powerful tool to augment human clinical expertise rather than replace it. The convergence of these applications suggests healthcare AI adoption will continue accelerating, driven by proven outcomes in patient care quality & operational efficiency.