Early detection saves lives. In cancer care, the sooner a diagnosis is made, the higher the chance of successful treatment. Yet, traditional screening methods often come too late or are too limited in scope. This is where&nbsp;Artificial Intelligence (AI) is changing the game.<h2>The Promise of AI in Early Cancer Detection</h2>AI systems can analyze massive amounts of data from imaging scans, pathology slides, genomics, and even wearable devices - to detect subtle patterns invisible to the human eye.<ul><li>Medical Imaging: AI models can flag early-stage tumors in CT, MRI, or mammogram images with remarkable accuracy.</li><li>Genomic &amp; Multi-Omics Data: Advanced algorithms detect genetic mutations and molecular signatures linked to cancer risk.</li><li>Wearables &amp; Real-Time Monitoring: Continuous tracking of vital signs and biomarkers provides early warning signals for potential abnormalities.</li></ul><h2>Real-World Examples</h2>AI for cancer detection is not just a concept; it’s already being used worldwide:<ul><li>Google Health (UK NHS, 2020): AI outperformed radiologists in detecting breast cancer from mammograms, reducing both false positives and false negatives.</li><li>Tempus (US): Uses AI-driven genomic profiling to help oncologists predict tumor growth and recommend targeted therapies.</li><li>Lunit INSIGHT (South Korea): Deployed in hospitals across Asia and Europe, this AI system detects lung cancer nodules in chest X-rays with high accuracy.</li><li>Paige.AI (US): FDA-approved AI pathology platform that assists pathologists in detecting prostate cancer earlier and more reliably.</li></ul>These examples show that AI is no longer futuristic; it is becoming part of standard oncology workflows.<h2>Challenges to Consider</h2>While the benefits are enormous, adoption comes with important questions:<ul><li>Accuracy &amp; Bias: AI models must be validated across diverse populations to ensure fair and reliable predictions.</li><li>Integration with Clinical Practice: AI should support doctors, not replace them. The best systems act as co-pilots, enhancing clinical decision-making.</li><li>Data Privacy &amp; Security: Sensitive medical data requires strict governance to maintain patient trust.</li></ul><h2>How VMO Supports This Transformation</h2>At&nbsp;VMO, we collaborate with healthcare providers, pharmaceutical companies, and medtech innovators to bring&nbsp;AI-powered cancer detection closer to reality. Our team helps by:<ul><li>Building&nbsp;early detection AI models</li><li>Designing&nbsp;secure data pipelines that combine imaging, clinical, and multi-omics data.</li><li>Embedding&nbsp;compliance and governance into every solution (HIPAA, GDPR).</li><li>Delivering platforms that integrate smoothly into existing clinical workflows.</li></ul>Our role is to ensure that AI systems in healthcare are&nbsp;not just innovative but also reliable and trusted, so that patients and clinicians can benefit with confidence.<h2>Final Thought</h2>Would you trust AI with a life-changing early warning? The future of cancer care may well depend on it. &nbsp;

Early detection saves lives. In cancer care, the sooner a diagnosis is made, the higher the chance of successful treatment. Yet, traditional screening methods often come too late or are too limited in scope. This is where Artificial Intelligence (AI) is changing the game.

Early Cancer Warnings: Would You Rely on AI?

early-cancer-warnings-would-you-rely-on-ai

<h2>GenAI for Environmental Sustainability: Turning Data into Green Action</h2>Environmental sustainability is no longer just a policy goal; it’s a technology challenge. With rising global emissions, waste, and resource scarcity, businesses and governments are seeking smarter ways to predict, manage, and reduce their environmental footprint.&nbsp;Generative AI (GenAI) is emerging as a powerful enabler in this mission.<h2>How Generative AI is Driving Sustainability</h2><h3>Energy Optimization</h3>GenAI models can simulate and design more efficient energy systems—whether optimizing power grids, forecasting renewable output, or generating strategies for energy storage.Real Case: Google’s DeepMind applied AI to optimize energy usage in data centers, cutting cooling costs by&nbsp;40%, directly reducing energy footprints.<h3>Smart Manufacturing</h3>In manufacturing, AI can generate optimized production workflows, minimizing waste and reducing energy consumption. Generative design techniques allow engineers to create lighter, stronger components using fewer raw materials.Real Case: Airbus used generative design to reimagine aircraft parts, reducing material usage by up to&nbsp;45% and lowering fuel consumption across fleets.<h3>Climate Modeling &amp; Prediction</h3>Traditional climate simulations are computationally heavy. GenAI accelerates predictive modeling by generating synthetic but accurate climate scenarios.Real Case: NVIDIA’s&nbsp;Earth-2 project uses AI-powered “digital twin” technology to simulate global climate systems with higher speed and precision, helping policymakers plan for extreme weather.<h3>Sustainable Agriculture</h3>AI can generate optimized crop rotation strategies, irrigation patterns, and soil treatments.Real Case: The Climate Corporation leverages AI to simulate and recommend farming practices that reduce water and fertilizer use, helping farmers cut both costs and emissions.<h3>Circular Economy &amp; Recycling</h3>Generative AI can propose innovative ways to recycle materials, design reusable packaging, and predict the lifecycle of products.Real Case: IBM’s&nbsp;AI for recycling initiative uses machine learning to generate new sorting and reuse strategies, improving recycling rates and reducing landfill waste.<h2>Why This Matters - The Numbers</h2><ul><li>AI could help reduce global greenhouse gas emissions by up to&nbsp;4% by 2030 (PwC).</li><li>Generative design can lower material usage in manufacturing by&nbsp;up to 45%.</li><li>AI-optimized energy systems can reduce&nbsp;10 - 20% of energy waste in utilities and industry.</li><li>In agriculture, AI-driven practices can cut water usage by&nbsp;up to 30% while increasing yield. &nbsp;</li></ul>These figures show that&nbsp;GenAI is not just a futuristic concept, it’s delivering measurable environmental and economic value today.<h2>VMO’s Role</h2>At&nbsp;VMO Holdings, we’ve seen how AI can reshape industries. From&nbsp;AI-driven predictive analytics in healthcare and logistics to&nbsp;sustainability-focused digital transformation, our teams help enterprises apply generative AI not just for efficiency but also for long-term environmental responsibility.The question is no longer whether AI can help, but how fast organizations are ready to adopt it to secure a greener future.

Environmental sustainability is no longer just a policy goal; it’s a technology challenge. With rising global emissions, waste, and resource scarcity, businesses and governments are seeking smarter ways to predict, manage, and reduce their environmental footprint. Generative AI (GenAI) is emerging as a powerful enabler in this mission.

GenAI for Environmental Sustainability: Turning Data into Green Action

genai-for-environmental-sustainability-turning-data-into-green-action

<h2>Predictive Cold Chain Analytics for Pharma Supply</h2>How AI is safeguarding medicine quality and reducing risks in pharma logisticsIn pharmaceutical supply chains,&nbsp;temperature control isn’t optional; it’s life-critical. From vaccines and biologics to specialty drugs, a deviation of just a few degrees can compromise safety, efficacy, and patient trust. Yet, traditional cold chain monitoring often responds&nbsp;only after a problem has occurred. By then, it’s already too late.This is where&nbsp;Predictive Cold Chain Analytics (PCCA) powered by AI comes in.<h2>The Challenge: Billions Lost, Lives Affected</h2><ul><li>The&nbsp;WHO estimates that up to 50% of vaccines are wasted annually, largely due&nbsp;to failures in temperature control and logistics.</li><li>In India,&nbsp;wastage at service delivery points reaches 56.8% for BCG and over 40% for several other vaccines (UNICEF).</li><li>A study across primary care practices showed only&nbsp;32% of medical refrigerators maintained correct ranges; nearly half recorded dangerous high or low temperatures.</li><li>Globally,&nbsp;2.7 billion people lack dependable vaccine access due to weak cold chain systems (SEforALL). &nbsp;</li></ul>Clearly, the current approach is unsustainable.<h2>Why Predictive Analytics Matters</h2>Instead of relying only on sensors that alert when a shipment is already out of range, predictive systems use AI models to&nbsp;forecast risks before they happen. They analyze:<ul><li>Real-time sensor data (temperature, humidity, vibration, light exposure)</li><li>External conditions (weather, customs delays, transport disruptions)</li><li>Historical patterns (routes, carriers, seasonal risks)</li></ul>The result? Logistics teams can proactively intervene, rerouting a shipment, adding cooling packs, or adjusting handling long before the medicine is at risk.<h2>The Technology Behind Predictive Cold Chain Analytics</h2>PCCA is powered by an integrated tech stack:<ol><li>IoT &amp; Smart Sensors <ul><li>Temperature, humidity, vibration, and GPS sensors track shipments in real-time.</li><li>Data is collected continuously at the edge, reducing dependency on delayed manual logs. &nbsp;</li></ul></li><li>Edge &amp; Cloud Computing <ul><li>Edge devices filter and preprocess sensor data (removing noise, flagging anomalies).</li><li>Cloud platforms aggregate this data across regions for advanced analytics and visualization. &nbsp;</li></ul></li><li>AI &amp; Machine Learning Models <ul><li>Time-series forecasting (ARIMA, Prophet, LSTM networks) predicts future temperature drifts.</li><li>Anomaly detection algorithms spot unusual patterns, e.g., a container warming faster than expected.</li><li>Predictive risk scoring integrates external data like weather APIs, traffic patterns, and customs clearance trends to calculate the probability of a breach. &nbsp;</li></ul></li><li>Digital Twin Simulations <ul><li>Virtual replicas of shipments or storage facilities simulate “what-if” scenarios, e.g., how a 4-hour flight delay affects drug stability.</li><li>These simulations guide proactive interventions before real-world risks occur. &nbsp;</li></ul></li><li>Dashboards &amp; Alerts <ul><li>Centralized platforms provide logistics managers with predictive insights.</li><li>Automated alerts recommend interventions: rerouting, dry ice replenishment, or emergency transfer.</li></ul></li></ol><h2>Real Impact on Pharma Supply</h2><ul><li>Reduced spoilage &amp; waste: Cutting into the billions of dollars lost every year.</li><li>Regulatory compliance: Predictive logs simplify audits for FDA, EMA, and GDP standards.</li><li>Cost efficiency: Fewer ruined batches mean major savings.</li><li>Patient safety &amp; trust: Medicines reach patients safely, effectively, and reliably.</li></ul><h2>Case in Action</h2>Imagine a biologics shipment traveling from Europe to Asia. The AI platform predicts a&nbsp;6-hour customs delay under high temperatures. Before exposure happens, the system flags the risk and recommends dry ice replenishment at the nearest hub. The pharma company prevents a&nbsp;multi-million-dollar loss and ensures patients receive effective medicine.How VMO Can HelpAt&nbsp;VMO - an ALTEN Company, we bring deep expertise in&nbsp;AI for healthcare and pharma supply chains:<ul><li>Predictive AI models tailored for cold chain logistics</li><li>IoT + edge computing + cloud analytics integration</li><li>End-to-end compliance (HIPAA, GDPR, GDP)</li><li>Dashboards with real-time predictive insights</li></ul>Our experience in&nbsp;AI-powered healthcare solutions, from radiology to patient monitoring extends naturally into&nbsp;supply chain resilience. With predictive cold chain analytics, VMO helps pharma companies not just monitor, but&nbsp;outsmart risks before they happen. &nbsp;

In pharmaceutical supply chains, temperature control isn’t optional; it’s life-critical. From vaccines and biologics to specialty drugs, a deviation of just a few degrees can compromise safety, efficacy, and patient trust. Yet, traditional cold chain monitoring often responds only after a problem has occurred. By then, it’s already too late.

Predictive Cold Chain Analytics for Pharma Supply

How AI is safeguarding medicine quality and reducing risks in pharma logistics

predictive-cold-chain-analytics-for-pharma-supply

<h2>The Challenge of Drug Repurposing</h2>Developing a new drug can take over a decade and cost billions of dollars.&nbsp;Drug repurposing - identifying new therapeutic uses for existing drugs - offers a faster, more cost-effective path. However, finding meaningful drug-disease associations requires analyzing massive biomedical datasets, integrating molecular structures, biological pathways, and clinical outcomes. Traditional methods struggle to capture the complex relationships between these data points.<h2>Enter Graph Neural Networks (GNNs)</h2>Graph Neural Networks are a class of deep learning models designed to work directly on graph-structured data - where entities (nodes) like drugs, proteins, and diseases are connected by edges representing interactions, pathways, or similarities.In drug repurposing, GNNs can:<ul><li>Integrate diverse biomedical data (drug molecular graphs, protein-protein interaction networks, and disease-gene associations).</li><li>Learn complex relationships between drugs and diseases beyond simple pairwise similarities.</li><li>Predict novel links between existing drugs and potential new indications with higher accuracy.</li><li>Reduce false positives by leveraging structural and functional knowledge embedded in biological networks.</li></ul>Example: A GNN model could learn from known drug-disease interactions, molecular structures, and gene expression profiles to predict that a cancer drug may also benefit patients with certain neurodegenerative disorders.<h2>By the Numbers: The Case for AI-Powered Drug Repurposing</h2><ul><li>30–40% of FDA-approved drugs in the late 2000s originated from repurposing efforts.</li><li>Repurposed drugs have an average&nbsp;≈30% approval rate, compared to&nbsp;≈10% for new drugs.</li><li>In real-world GNN applications, models like&nbsp;Dr-COVID identified 150 candidate drugs for COVID-19,&nbsp;46 of which were already in clinical trials, with known treatments ranking in the&nbsp;top 15 predictions.</li></ul>These figures highlight why GNN-powered repurposing is not just a theoretical breakthrough but a&nbsp;statistically proven accelerator for drug discovery.<h2>Why GNNs are Game-Changers in Healthcare AI</h2><ul><li>Scalable insights: Can process millions of nodes and edges from large biomedical databases (e.g., DrugBank, PubChem, CTD).</li><li>Precision predictions: Outperforms traditional machine learning in link prediction for drug-disease associations.</li><li>Rapid validation: Integrates real-world clinical data for faster hypothesis testing.</li></ul><h2>VMO’s Role in AI-Driven Drug Discovery</h2>As part of our&nbsp;AI in Healthcare portfolio, VMO supports life sciences organizations in building&nbsp;end-to-end AI pipelines for drug repurposing:<ol><li>Data Engineering &amp; Integration <ul><li>Curating and cleaning heterogeneous biomedical datasets.</li><li>Building graph databases combining chemical, genomic, and clinical trial data. &nbsp;</li></ul></li><li>Model Development <ul><li>Designing and training&nbsp;custom GNN architectures tailored to drug discovery use cases.</li><li>Incorporating domain-specific features like molecular fingerprints, omics profiles, and clinical notes. &nbsp;</li></ul></li><li>Validation &amp; Deployment <ul><li>Integrating GNN predictions into existing&nbsp;R&amp;D workflows.</li><li>Deploying scalable AI services on secure, compliant cloud environments (HIPAA, GDPR, PDPA). &nbsp;</li></ul></li><li>Ongoing Optimization <ul><li>Model retraining with new biomedical data.</li><li>Continuous monitoring to ensure reproducibility and compliance with regulatory standards.</li></ul></li></ol><h2>Potential Impact</h2>With VMO’s expertise in&nbsp;AI model design, data pipelines, and healthcare compliance, pharmaceutical companies can:<ul><li>Reduce early-stage discovery costs by&nbsp;30–50%.</li><li>Shorten validation timelines from years to months.</li><li>Increase the success rate of drug repurposing candidates entering clinical trials.</li></ul><h2>Looking Ahead</h2>The convergence of&nbsp;graph learning and&nbsp;healthcare AI is transforming how we discover treatments. By enabling models to understand the complex “network of life,” GNNs unlock the potential of existing drugs in ways previously unimaginable.At&nbsp;VMO – an ALTEN Company, we help you harness AI for better, faster, and more affordable healthcare innovation.

Developing a new drug can take over a decade and cost billions of dollars. Drug repurposing - identifying new therapeutic uses for existing drugs - offers a faster, more cost-effective path. However, finding meaningful drug-disease associations requires analyzing massive biomedical datasets, integrating molecular structures, biological pathways, and clinical outcomes. Traditional methods struggle to capture the complex relationships between these data points.

Smarter Drug Repurposing with AI-Powered Graph Neural Networks

smarter-drug-repurposing-with-ai-powered-graph-neural-networks

Artificial Intelligence (AI) has revolutionized the way healthcare professionals interpret X-rays, CT scans, MRIs, and histopathology slides. Behind the simple “AI detected abnormality” result lies a complex, multi-stage process powered by computer vision, machine learning, and medical domain expertise.In this article, we’ll explore the technology behind AI medical imaging, walk through a real case study, and show how&nbsp;VMO delivers world-class AI healthcare solutions.<h2>How AI “Sees” Medical Images</h2>AI medical imaging pipelines typically involve&nbsp;five core steps:<h3>Step 1: Data Acquisition &amp; Preprocessing</h3><ul><li>Modality Support: AI can work with various imaging formats - DICOM for CT/MRI, TIFF for pathology slides, and PNG/JPEG for ultrasound exports.</li><li>Noise Reduction: Algorithms like Gaussian smoothing or Non-Local Means filtering remove grain and artifacts.</li><li>Normalization: Adjusts brightness/contrast and scales pixel values to ensure consistent inputs.</li><li>Resizing &amp; Cropping: Standardizing resolution (e.g., 512×512 pixels for CT) while keeping key anatomy intact. Example: In CT brain scans, preprocessing can remove bone shadows so the AI focuses on brain tissue.</li></ul><h3>Step 2: Feature Extraction with Deep Learning</h3>Modern AI models - especially&nbsp;Convolutional Neural Networks (CNNs)v - automatically learn complex patterns:<ul><li>Edge Detection: Identifying organ boundaries and lesion shapes.</li><li>Texture Analysis: Spotting abnormal tissue patterns.</li><li>Multi-scale Processing: Detecting both large tumors and microcalcifications.</li></ul>Popular architectures include:<ul><li>ResNet / DenseNet: For general medical image classification.</li><li>U-Net: For precise segmentation of lesions or organs.</li><li>Vision Transformers (ViT): For handling high-resolution images with global context.</li></ul><h3>Step 3: Detection, Segmentation &amp; Classification</h3><ul><li>Object Detection Models (e.g., Faster R-CNN, YOLOv8): Locate regions of interest (ROI) like tumors or hemorrhages.</li><li>Segmentation Models: Create pixel-level maps to outline disease boundaries.</li><li>Classification Models: Predict disease type or severity (e.g., “COVID-19 positive” vs. “negative” on chest X-ray).</li></ul><h3>Step 4: Clinical Context Integration</h3>AI alone can’t make a diagnosis, it must integrate:<ul><li>Patient metadata (age, sex, lab results).</li><li>Historical scans for progression analysis.</li><li>Radiology report generation using NLP to ensure the output is clinically actionable.</li></ul><h3>Step 5: Human-AI Collaboration</h3>The AI’s findings are reviewed by radiologists:<ul><li>AI assists in triaging urgent cases first.</li><li>Radiologists confirm or adjust AI annotations.</li><li>Feedback is used to&nbsp;continuously retrain and improve the model.</li></ul><h2>Real Case - AI for Stroke Detection &amp; Prioritization</h2>Scenario: In a large urban hospital, emergency CT scans are processed by an AI model trained to detect&nbsp;large vessel occlusion (LVO) in stroke patients.Workflow:<ol><li>Scan Acquisition: A patient arrives with stroke symptoms. CT angiography (CTA) images are uploaded to PACS.</li><li>AI Processing: The model runs a&nbsp;U-Net segmentation + classification head to identify arterial blockages.</li><li>Alert System: If LVO is detected, an alert is sent to the neurologist’s smartphone within&nbsp;3 minutes.</li><li>Outcome: The patient is sent directly to interventional radiology for thrombectomy, reducing treatment delay by&nbsp;25–40 minutes.</li></ol>Impact:<ul><li>Door-to-treatment time drops significantly.</li><li>Improves survival rates and reduces long-term disability.</li></ul><h2>How VMO Delivers AI Healthcare Solutions</h2>At&nbsp;VMO - An ALTEN Company, we build&nbsp;end-to-end AI medical imaging platforms that combine cutting-edge technology with regulatory compliance.<h3>Our Capabilities:</h3><ul><li>Custom AI Model Development: CNN, Transformer, and hybrid architectures for radiology, pathology, and dermatology.</li><li>PACS/RIS Integration: Seamless plug-in to existing hospital workflows.</li><li>Regulatory Compliance: HIPAA, GDPR, PDPA ready.</li><li>Edge &amp; Cloud Deployment: Optimized for both on-prem hospital servers and secure cloud environments.</li><li>Continuous Learning: Active learning loops for model updates without full retraining.</li></ul><h3>VMO Case Example – AI Oncology Imaging</h3>We developed an AI pipeline for a regional cancer center:<ul><li>Multi-modal input: Histopathology slides + MRI scans.</li><li>Tasks: Tumor segmentation, stage classification, and treatment response tracking.</li><li>Results: Improved detection sensitivity by&nbsp;14% and reduced reporting time by&nbsp;33%.</li><li>Deployment: Hybrid cloud to ensure scalability and compliance.</li></ul>The Future - Beyond DetectionThe next frontier is&nbsp;AI-powered precision medicine:<ul><li>Predicting tumor growth patterns.</li><li>Personalized treatment recommendations.</li><li>Integrating genomic + imaging data for holistic patient profiles.</li></ul>VMO is already working with hospitals and healthtech firms in&nbsp;Singapore, Vietnam, and Europe to bring these capabilities to life.

Artificial Intelligence (AI) has revolutionized the way healthcare professionals interpret X-rays, CT scans, MRIs, and histopathology slides. Behind the simple “AI detected abnormality” result lies a complex, multi-stage process powered by computer vision, machine learning, and medical domain expertise.

The Science of Seeing: AI Models in Medical Images

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<h2>Tech Behind Telemedicine: What You Don’t See Behind the Screen</h2>Telemedicine often looks simple from the outside - log in, meet your doctor, and get diagnosed from the comfort of home. But behind that seamless user experience is a complex, interconnected tech stack that keeps the engine running, compliant, and secure.At VMO, we’ve helped healthcare providers build and scale telemedicine platforms that go far beyond video calls. In this post, we pull back the curtain on what’s happening behind the scenes and share a real example of how our technology empowered one provider to transform care delivery.<h2>More Than a Video Call: The Real Engine of Telemedicine</h2>Behind every telehealth session lies a mix of cloud infrastructure, security layers, AI models, and medical data pipelines. A modern telemedicine platform includes:<ul><li>Real-Time Communication (RTC): Built with WebRTC or third-party SDKs for high-quality, low-latency video/audio calls.</li><li>AI-Assisted Triage &amp; Chatbots: NLP-powered chatbots support patient intake, symptom checking, and follow-ups.</li><li>EHR Integration: Syncing with Electronic Health Records ensures continuity of care and proper documentation.</li><li>Scheduling &amp; Notifications: Automated reminders, appointment booking, and calendar integrations improve attendance.</li><li>HIPAA/GDPR/PDPA Compliance: Security protocols, encrypted transmission, role-based access controls, and audit logs are non-negotiable.</li><li>Scalability: Platforms must handle surges in traffic without downtime - requiring auto-scaling cloud architecture (AWS, Azure, GCP).</li><li>Localization: Multilingual support, currency formatting, and region-specific clinical workflows.</li></ul><h2>Building a Scalable Telemedicine Platform for a Southeast Asian Healthtech Leader&nbsp;</h2>VMO Case study in Healthcare<h3>The Challenge</h3>A fast-growing health tech startup in Southeast Asia needed to expand its telemedicine services across borders, connecting patients, hospitals, and insurers within a single ecosystem. Their goals:<ul><li>Support thousands of concurrent consultations</li><li>Integrate with partner hospitals and insurance APIs</li><li>Ensure HIPAA and PDPA compliance</li><li>Launch new features (e.g., AI symptom checker, e-prescriptions) fast</li></ul><h3>The Solution</h3>VMO provided a dedicated development team, including backend engineers, frontend developers, QA specialists, and DevOps experts. We:<ul><li>Designed a modular architecture using microservices, ensuring each function (chat, video, prescription, billing) could scale independently</li><li>Built a secure backend with Node.js and PostgreSQL, deployed on AWS with auto-scaling groups and CI/CD pipelines</li><li>Integrated AI for symptom checking, using pre-trained NLP models tailored for regional languages and medical terminology</li><li>Established EHR and insurer APIs, allowing real-time eligibility checks and claims processing</li><li>Created a doctor dashboard with scheduling, call logs, e-medical notes, and analytics</li></ul><h3>Results</h3><ul><li>400% increase in weekly consultations without downtime</li><li>Reduced patient onboarding time by 70% with chatbot + form autofill</li><li>Real-time insurer approval cut claim wait time from 3 days to 5 minutes</li><li>Successfully launched in 3 countries within 12 months</li></ul><h2>3. Why This Matters: A Blueprint for Healthcare Innovators</h2>What this shows is clear:&nbsp;telemedicine is not just a feature—it’s a platform.If you’re a healthcare provider, insurer, or healthtech company looking to:<ul><li>Scale teleconsultation across regions</li><li>Integrate with existing hospital or payer systems</li><li>Comply with cross-border data regulations</li><li>Innovate faster with AI and automation</li></ul>… you need more than an app. You need a partner who understands the full ecosystem and how to build it for real-world care.<h2>4. Let’s Talk</h2>At VMO, we develop AI, cloud, and data-driven platforms for healthcare clients worldwide, combining compliance, scalability, and performance. &nbsp;

Telemedicine often looks simple from the outside - log in, meet your doctor, and get diagnosed from the comfort of home. But behind that seamless user experience is a complex, interconnected tech stack that keeps the engine running, compliant, and secure.

Tech Behind Telemedicine: Behind the Screen Revealed

Tech Behind Telemedicine: What You Don’t See Behind the Screen

tech-behind-telemedicine-behind-the-screen-revealed

We are looking for an Data & AI Executive (who need to be collaborative, organized, think out-of-the-box, and be ready to pursue new opportunities.

AI Engineer

ai-engineer

Tham gia dự án outsource cho khách hàng thị trường Nhật Bản
Là cầu nối giữa khách hàng Nhật Bản và đội dự án Offshore ở Việt Nam.
Làm tài liệu của dự án ( Dự án domain làm về Insurance )
Làm việc trực tiếp với khách hàng Nhật: Giao tiếp, thương lượng, nhận, phân tích các yêu cầu của dự án phần mềm… và truyền đạt cho đội dự án ở Việt Nam.
Lập kế hoạch, theo dõi tiến độ, quản lý nhóm,....

IT BUSINESS ANALYST (SONG NGỮ ANH NHẬT)

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- Tham gia thiết kế mô hình tổ chức, chức năng nhiệm vụ, hệ thống vị trí chức danh công việc:
- Tham gia xây dựng hệ thống quản trị hiệu suất
- Tham gia xây dựng hệ thống phát triển năng lực lãnh đạo, quản lý và quản trị đội ngũ cán bộ kế cận.
- Tham gia xây dựng các quy trình, quy định, quy chế Quản trị Nhân sự
- Tư vấn cho các Phòng Ban trong việc soạn thảo, ban hành, áp dụng các tài liệu nội bộ hoặc các vấn đề liên quan hệ thống khác
- Hỗ trợ các công việc khác của phòng theo hướng dẫn của quản lý trực tiếp

Organizational Development Executive

organizational-development-executive

Tham gia dự án outsource cho khách hàng thị trường  u – Mỹ/Sing/Nhật…
- Phân tích và chi tiết hóa yêu cầu của khách hàng
- Viết các tài liệu đặc tả yêu cầu nghiệp vụ, yêu cầu người dùng, yêu cầu hệ thống
- Viết tài liệu hướng dẫn cho người sử dụng,…
- Viết tài liệu SRS, FSD

Business Analyst

business-analyst

1. Đề xuất lập kế hoạch và theo dõi, cập nhật việc triển khai kế hoạch công việc của Phòng Pháp chế
2. Tổ chức thực hiện, chỉ đạo nhân sự Phòng Pháp chế thực hiện công việc thuộc chức năng nhiệm vụ của Phòng, bao gồm các mảng việc chính như:
- Thẩm định Pháp lý: Thẩm định, kiểm soát về mặt pháp lý hồ sơ, văn bản, hợp đồng liên quan đến hoạt động mua sắm thường xuyên của Công ty; Thẩm định pháp lý các dự thảo hợp đồng kinh doanh; Chủ trì soạn thảo các loại mẫu hợp đồng phục vụ toàn bộ hoạt động sản xuất kinh doanh của Công ty.
- Tư vấn pháp lý: Tư vấn pháp lý nội bộ cho HĐQT, Ban Giám đốc, Phòng/Ban đảm bảo tất cả các hoạt động của Công ty đều thực hiện theo đúng pháp luật liên quan; Tư vấn và tham gia thực hiện các thủ tục pháp lý liên quan hoạt động đầu tư, M&A, thành lập hiện diện thương mại tại các quốc gia khác của Công ty.
- Tổ chức phổ biến, giáo dục pháp luật: Chủ trì tiến hành cập nhật/phổ biến, giáo dục pháp luật, điều lệ, nội quy, quy chế của Công ty cho CBCNV; Kiểm tra mức độ hiểu biết pháp luật của CBNV trong Trung tâm; Tổ chức đôn đốc, kiểm tra việc thực hiện pháp luật, điều lệ, nội quy, quy chế, quy định trong Trung tâm.
- Quản lý hệ thống văn bản nội bộ Công ty: Xây dựng và ban hành văn bản quản lý của Công ty liên quan đến công tác pháp lý; quản lý việc ban hành và lưu trữ các văn bản nội bộ trong Công ty; đảm bảo các Phòng/ban trong Công ty tuân thủ đúng quy chế, quy định, chính sách nội bộ.
- Bảo đảm phân quyền: Chủ trì soạn thảo, theo dõi, giám sát việc phân quyền và hoạt động ủy quyền trong Công ty và các công ty con, công ty liên quan.
- Tham gia giải quyết tranh chấp để bảo vệ quyền và lợi ích hợp pháp: Tư vấn và tham gia giải quyết tranh chấp liên quan đến hoạt động  sản xuất kinh doanh của Công ty, tham gia quá trình thu hồi công nợ và xử lý nợ xấu của các Dự án;
- Làm việc với cơ quan nhà nước: Soạn thảo hồ sơ đăng ký kinh doanh của đơn vị; nộp hồ sơ và nhận kết quả đăng ký kinh doanh của Công ty; Làm việc với cơ quan nhà nước có thẩm quyền khi có yêu cầu hoặc theo tình hình giải quyết công việc trong hoạt động của các đơn vị.
- Công tác kiểm tra nội bộ: Kiểm tra và xử lý các vụ việc liên quan đến việc chấp hành pháp luật; chấp hành các quy chế, quy định trong công ty. Đảm bảo việc tuân thủ pháp luật, cũng như những nội quy, quy định nội bộ liên quan đến pháp lý;
- Thực hiện các công việc khác theo yêu cầu của BGĐ.
3. Đào tạo, nâng cao kỹ năng, nghiệp vụ nhân sự của Phòng Pháp chế.

Legal Executive

legal-executive

- Tham gia phát triển, xây dựng, rà soát và hoàn thiện các chương trình, chiến lược về phát triển tổ chức và văn hóa doanh nghiệp của VMO.
- Là đầu mối xây dựng, quản lý và tổ chức triển khai các hoạt động, chương trình phát triển tổ chức, vinh danh khen thưởng, văn hóa doanh nghiệp nhằm tăng cường văn hóa doanh nghiệp của VMO.
- Xây dựng, kết nối và duy trì các mối quan hệ, quy trình phối hợp giữa các bên để đạt được mục đích công việc
- Lập kế hoạch, quản lý, điều phối và/hoặc tham gia các chương trình, dự án được giao trong phạm vi lĩnh vực chuyên môn về phát triển tổ chức và văn hóa doanh nghiệp
- Thực hiện các hoạt động huấn luyện/ đào tạo cán bộ nhân viên về các kiến thức/ năng lực liên quan tới phát triển tổ chức và văn hóa doanh nghiêp.
- Xây dựng, triển khai và phát triển các kênh truyền thông của công ty 
- Sáng tạo, sản xuất Content trên các kênh truyền thông
- Có khả năng nắm bắt nhanh các xu hướng mới trong cách viết content
- Phối hợp cùng phòng Truyền thông thực hiện các hoạt động truyền thông khác  theo phân công.

Internal Communication Executive

internal-communication-executive

- Tham gia dự án của thị trường Mỹ 
- Phát triển tính năng cho Mobile app.
- Thực hiện tất cả các công đoạn: Code, Test Review, Release
- Phân tích dữ liệu người dùng để liên tục cải tiến và tối ưu hoá các tính năng của app…

Mobile Native Leader

mobile-native-leader

- Phân tích các yêu cầu về sản phẩm
- Phát triển,bảo trì các sản phẩm/ứng dụng đang chạy trên nền tảng iOS
- Duy trì chất lượng code, tổ chức và tự động hóa
- Xây dựng các ứng dụng đa luồng
- Nghiên cứu tài liệu, phân tích yêu cầu và phát triển dự án trên môi trường iOS
- Báo cáo công việc đến cấp quản lý
- Nếu có kinh nghiệm lead team sẽ đảm nhiệm vị trí TeamLeader trở lên.

IOS Developer

ios-developer

- Các dự án Product/Outsourcing của các tệp Khách hàng toàn cầu với domain đa dạng
- Thiết kế UI/UX các sản phẩm web, app,…
- Review, coaching team member
- Nghiên cứu tìm hiểu, cập nhật xu hướng thiết kế mới nhằm mục đích nâng cao chất lượng và hiệu quả các sản phẩm thiết kế
- Thực hiện các nghiên cứu người dùng, phân tích tâm lý để xác định nhóm đối tượng mục tiêu và phương pháp thiết kế tương tác hiệu quả nhất;
- Phối hợp chặt chẽ với các bộ phận liên quan: kinh doanh, sản phẩm và thiết kế..để lựa chọn phương án thiết kế UX xuyên suốt dự án sản phẩm/dịch vụ;
- Trao đổi ý tưởng thiết kế bằng cách xây dựng các sản phẩm UX nổi bật như: storyboards, sitemaps, wireframes, flowcharts, prototypes…;
- Thiết kế UI/UX Website, Wapp và App theo tiêu chí : sáng tạo, thân thiện với người dùng, tạo sự khác biệt;
- Tạo trang và tính năng mockups;
- Chuyển đổi từ PSD mockups hoặc bất kỳ định dạng đồ họa nào khác về định dạng HTML tiêu chuẩn/CSS;

UI/UX Designer

ui-ux-designer

- Tham gia các dự án product/outsource cho khách hàng Nhật Bản: lĩnh vực tài chính, y tế, ecommerce…
- Kỹ sư cầu nối cho đội phát triển với khách hàng Nhật
- Làm việc trực tiếp với khách hàng người nhật
- Tham gia quản lý tiến độ dự án, báo cáo công việc định kỳ với khách hàng và quản lý dự án  
- Loại hình dự án: Các hệ thống web-application (Java, NodeJS, ReactJS, PHP, HTML, CSS,..)

Kỹ Sư Cầu Nối (BrSE)

ky-su-cau-noi-brse

- Hỗ trợ Manager trong việc lên kế hoạch, thực thi và theo dõi tiến độ công việc
- Giao tiếp với các bên liên quan để hoàn thành công việc được giao 
- Làm báo cáo công việc hàng tuần

Assistant Director

Assistant Director

INFORMATION