Third-party research. These entries are summaries of publicly available company reports, vendor case studies, and peer-reviewed publications. They are not Ajentik customer references. Every numeric claim links back to its original source; entries marked vendor-sourced reflect figures published by the vendor and have not been independently audited.
Industry Research
Sourced summaries of how leading organisations are deploying AI in healthcare and adjacent industries — drawn entirely from public reporting.
Healthcare Industry
Mayo Clinic AI Factory: Platform-Driven Clinical AI
Large nonprofit health system adopting AI at scale through a platform approach
Challenge
Mayo faced the need to democratise AI development across their organisation with 76,000 staff, enable clinicians to create AI solutions without extensive technical expertise, manage a large concurrent portfolio of AI projects and ensure regulatory compliance for medical AI applications while maintaining patient safety.
Reported approach
Mayo developed an AI Factory platform on Google's Vertex AI, enabling "citizen development" of AI tools. Their programs include ECG-based cardiac analysis, ICU capacity management systems and hypothesis-driven AI for cancer research. The platform features a Software as a Medical Device Review Board for governance.
Reported outcomes
- ·Regulatory clearance for multiple clinical AI algorithms described in public Mayo reporting
- ·Successful commercialization through spinoff companies like Anumana
- ·Apple Watch integration for cardiac monitoring reaching millions
- ·Established medical AI degree program training next generation
- ·Reduced diagnostic errors in pilot departments (specific figures per Mayo internal reporting)
- ·Reported operational efficiency gains (specific savings figures per Mayo internal reporting)
What this signals for the sector
This entry is included as a research note about the broader healthcare AI landscape. It does not describe an Ajentik product, customer, or partnership. Readers can use the source links below to inspect the original reporting.
Editorial note:Mayo Clinic operates its own AI programs (Mayo Clinic Platform). No Ajentik partnership exists or is implied. Only the figures shown in the tiles below are reported by Mayo Clinic or its public partners; other narrative figures previously cited in this entry have been removed pending a primary source.
Multi-Modal AI Ecosystem: Comprehensive Healthcare Transformation
23 hospitals and 276 outpatient facilities serving 13.7 million annual encounters
Challenge
The organisation with 82,600 employees faced overwhelming administrative burden on clinicians, poor quality medical documentation affecting patient care, operational inefficiencies costing millions annually and high call volumes disrupting primary care services with 30+ minute wait times.
Reported approach
Cleveland Clinic deployed multiple specialised AI agents: Ambience Healthcare for real-time clinical documentation, AKASA for intelligent medical coding (processing 100+ documents in 1.5 minutes), predictive analytics for synthetic data generation and AI-powered phone systems for patient communication. These agents work together in an integrated ecosystem.
Reported outcomes
- ·100% call answering within 3 rings (from 30+ minute waits)
- ·220 fewer calls per day in primary care
- ·15 work days saved weekly through automation
- ·82% autonomous call handling
- ·$15M annual savings in operational costs
What this signals for the sector
This entry is included as a research note about the broader healthcare AI landscape. It does not describe an Ajentik product, customer, or partnership. Readers can use the source links below to inspect the original reporting.
Editorial note:Cleveland Clinic runs its own AI programs through Cleveland Clinic Innovations. No Ajentik partnership exists or is implied. Figures cited are drawn from Cleveland Clinic public communications.
Oncology AI Orchestrator: Advanced Clinical Applications
Serving 4,000 tumor board patients annually with AI-powered cancer care
Challenge
Stanford faced information overload with physicians spending 1.5-2.5 hours per patient reviewing imaging, pathology, genomics and clinical notes; difficulty keeping pace with rapidly evolving cancer research (new paper every 30 seconds); time-intensive tumor board preparation and challenges matching patients to appropriate clinical trials from 400,000+ active trials.
Reported approach
Stanford deployed Microsoft-powered Healthcare Agent Orchestrator featuring specialised agents: tumor board agent analysing multimodal data, care coordination agent providing treatment recommendations, research literature agent processing medical updates and clinical trial matching agent. The system uses secure Azure infrastructure with comprehensive FURM assessment for AI fairness.
Reported outcomes
- ·Supports 4,000 annual tumor board patients
- ·Enhances diagnostic accuracy by 25% via multimodal analysis
- ·Accelerates treatment decisions from days to hours
- ·Processes 10,000+ research papers monthly
- ·ROI of 300% within first year
What this signals for the sector
This entry is included as a research note about the broader healthcare AI landscape. It does not describe an Ajentik product, customer, or partnership. Readers can use the source links below to inspect the original reporting.
Editorial note:Stanford Medicine and Microsoft Research conduct legitimate published work in this area. No Ajentik partnership exists or is implied. Figures cited are drawn from Stanford HAI and Microsoft Research publications.
COVID-19 Drug Discovery: Research Acceleration
AI-driven drug discovery identifies COVID-19 treatments in record time
Challenge
During the COVID-19 pandemic, researchers faced urgent need for treatment options with 500,000+ daily cases, overwhelming volume of scientific literature (5,000+ COVID papers published weekly), traditional drug development timelines of 10-15 years and need to identify safe, already-approved drugs for rapid repurposing to avoid lengthy trials.
Reported approach
BenevolentAI deployed autonomous agents including biomedical literature mining agent processing millions of scientific papers, drug-target interaction agent predicting novel relationships using knowledge graphs, clinical trial optimisation agent for patient selection and ADME prediction agent for drug properties. The system identified baricitinib as potential treatment in just 4 days.
Reported outcomes
- ·Baricitinib received FDA Emergency Use Authorization by November 2020
- ·Demonstrated 71% reduced recovery time when combined with remdesivir
- ·Successfully mitigated cytokine storm through AAK1 inhibition
- ·Compressed typical 10-year timeline to 9 months
- ·Led to $800 million deal for Alzheimer's drug targets
- ·Analysed 1M+ scientific papers in days vs years
- ·Now applied to 20+ other disease areas
What this signals for the sector
This entry is included as a research note about the broader healthcare AI landscape. It does not describe an Ajentik product, customer, or partnership. Readers can use the source links below to inspect the original reporting.
Predictive Eldercare: AI-Powered Fall Prevention and Health Monitoring
Wearable AI platform revolutionizes senior care with continuous behavioural monitoring and predictive analytics
Challenge
Senior living facilities faced reactive care models where health issues were only addressed after incidents occurred. Falls, the leading cause of injury deaths among adults 65+, resulted in $50 billion in annual medical costs. Facilities struggled with staff shortages, inconsistent monitoring and inability to predict health decline before emergencies.
Reported approach
CarePredict developed Tempo, a wrist-worn AI device that continuously monitors 18+ daily activities including eating, sleeping, walking and bathroom usage. The system uses machine learning to establish individual baselines and detect subtle deviations that precede health events. AI algorithms predict UTIs 3.5 days before symptoms, falls before they happen and depression onset through activity pattern changes.
Reported outcomes
- ·Depression prediction 2-3 weeks before clinical diagnosis
- ·Staff saves 2+ hours daily on manual monitoring tasks
What this signals for the sector
This entry is included as a research note about the broader healthcare AI landscape. It does not describe an Ajentik product, customer, or partnership. Readers can use the source links below to inspect the original reporting.
AI Companion for Seniors: Combating Loneliness at Scale
Social robot powered by empathetic AI reduces senior isolation with 30+ daily interactions
Challenge
Social isolation affects 1 in 4 seniors over 65, increasing mortality risk by 26% and dementia risk by 50%. Traditional solutions like scheduled calls or community programs reach only a fraction of isolated seniors. The COVID-19 pandemic exacerbated isolation, with many seniors going days without meaningful human interaction.
Reported approach
ElliQ is a proactive AI companion that initiates conversations, suggests activities, provides medication reminders, facilitates video calls with family and guides wellness exercises. Unlike passive devices that wait for commands, ElliQ uses empathetic AI to sense mood, learn preferences and engage seniors throughout the day. The robot combines conversational AI with a physical presence that creates emotional connection.
Reported outcomes
- ·Seniors engage in 30+ daily interactions with ElliQ
- ·90% user retention rate over 12 months
- ·Family video calls increased 3x with ElliQ facilitation
- ·New York State deployed 800+ units to Medicaid recipients
- ·Estimated $10,000+ annual healthcare cost reduction per user
What this signals for the sector
This entry is included as a research note about the broader healthcare AI landscape. It does not describe an Ajentik product, customer, or partnership. Readers can use the source links below to inspect the original reporting.
AI-Powered Hospital Discharge: Eliminating Bottlenecks in Patient Flow
Machine learning platform reduces excess hospital days and saves millions in operational costs
Challenge
Hospital discharge is notoriously complex, involving coordination between physicians, nurses, social workers, pharmacists and post-acute facilities. OhioHealth, serving 1.5 million patients annually across 14 hospitals, struggled with discharge delays costing $800+ per excess day. Patients often remained hospitalized awaiting non-clinical processes like insurance approvals or skilled nursing placement.
Reported approach
Qventus deployed AI agents that predict discharge readiness, automate milestone tracking and orchestrate multi-team workflows. The system analyses 100+ variables including clinical status, social determinants and post-acute bed availability to predict and accelerate safe discharges. AI identifies barriers early and automatically routes tasks to appropriate team members.
Reported outcomes
- ·Eliminated 8,554 excess patient days in first year
- ·$1.7 million in annual savings from improved throughput
- ·Average length of stay reduced by 0.5 days
- ·Earlier identification of post-acute care needs (2+ days advance)
- ·Reduced patient boarding in ED by 30%
- ·Staff satisfaction improved due to reduced administrative burden
What this signals for the sector
This entry is included as a research note about the broader healthcare AI landscape. It does not describe an Ajentik product, customer, or partnership. Readers can use the source links below to inspect the original reporting.
AI-Driven Remote Patient Monitoring: Hospital-Grade Care at Home
FDA-cleared AI platform reduces through continuous vital sign monitoring
Challenge
Post-discharge patients face the highest risk period in their healthcare journey, with 1 in 5 Medicare patients readmitted within 30 days at a cost of $26 billion annually. Traditional follow-up—phone calls and office visits—catches problems too late. Patients with heart failure, COPD and other chronic conditions deteriorate at home without warning signs reaching care teams.
Reported approach
Biofourmis deploys FDA-cleared wearable biosensors combined with AI algorithms that continuously analyse 20+ physiological parameters. The Biovitals platform detects subtle deterioration patterns 8+ hours before clinical symptoms appear, enabling proactive intervention. AI personalises alert thresholds based on each patient's baseline, dramatically reducing false alarms while catching true deterioration.
Reported outcomes
- ·Detection of deterioration 8+ hours before symptoms
- ·97% patient satisfaction rating
- ·$12,000+ savings per patient through avoided readmissions
- ·Clinical teams receive actionable insights, not raw data
What this signals for the sector
This entry is included as a research note about the broader healthcare AI landscape. It does not describe an Ajentik product, customer, or partnership. Readers can use the source links below to inspect the original reporting.
AI-Matched Companion Care: Scaling Human Connection
Technology platform matches seniors with "Papa Pals" companions
Challenge
Healthcare plans struggled to address social determinants of health—transportation barriers, social isolation and daily living challenges—that drive costly medical utilisation. Traditional home care focused on clinical tasks, missing the companionship and practical support that prevents health decline. Seniors needed both social connection and help with errands, technology and appointments.
Reported approach
Papa uses AI to match seniors with "Papa Pals"—vetted companions who provide transportation, companionship, technology help and light housekeeping. The platform's algorithms consider personality, interests, language and specific needs to create optimal matches. AI monitors visit patterns and outcomes to continuously improve matching and identify emerging health risks.
Reported outcomes
- ·4.8/5 average member satisfaction rating
- ·2+ million companion visits completed
- ·Partnerships with 150+ health plans including Humana, Aetna, Centene
What this signals for the sector
This entry is included as a research note about the broader healthcare AI landscape. It does not describe an Ajentik product, customer, or partnership. Readers can use the source links below to inspect the original reporting.
AI-Powered Chronic Disease Management: Diabetes Control at Scale
Connected device platform with AI coaching achieves clinical outcomes rivaling intensive in-person care
Challenge
Diabetes affects 537 million adults globally, with management requiring constant attention to blood glucose, diet, exercise and medication. Traditional care—quarterly doctor visits—leaves patients unsupported 99% of the time. Poor control leads to complications costing $327 billion annually in the US alone. Patients need continuous guidance, not episodic appointments.
Reported approach
Livongo provides a connected blood glucose meter that uploads readings in real-time to an AI platform. When readings fall outside personalised parameters, AI triggers immediate coaching interventions—sometimes automated messages, sometimes live certified diabetes educators. The system learns each member's patterns, providing proactive guidance before problems occur.
Reported outcomes
- ·0.9% average A1c reduction (clinically significant)
- ·$1,908 average annual cost savings per member
- ·1.2+ million members enrolled across 2,000+ employers
- ·Net Promoter Score of 64 (exceptional for healthcare)
What this signals for the sector
This entry is included as a research note about the broader healthcare AI landscape. It does not describe an Ajentik product, customer, or partnership. Readers can use the source links below to inspect the original reporting.
AI Caregiver Assessment: Preventing Burnout Before It Happens
Evidence-based platform uses AI to identify and address family caregiver burnout risk
Challenge
Family caregivers—53 million Americans and growing—face burnout rates exceeding 60%, leading to their own health problems and inability to continue caregiving. Employers lose $33 billion annually as caregiving employees reduce hours, miss work or quit. Traditional support programs serve caregivers already in crisis rather than preventing burnout.
Reported approach
TCARE uses an AI-powered assessment algorithm developed from 20+ years of academic research to quantify caregiver burden across multiple dimensions: identity discrepancy, care burden, relationship quality and health. The platform then generates personalised action plans with specific interventions proven to address identified risks. AI continuously learns from outcomes to improve recommendations.
Reported outcomes
- ·15-minute assessment replaces 2-hour traditional intake
- ·Deployed across 40 states serving 100,000+ caregivers
- ·Cost per caregiver stabilized at $200-400 annually
What this signals for the sector
This entry is included as a research note about the broader healthcare AI landscape. It does not describe an Ajentik product, customer, or partnership. Readers can use the source links below to inspect the original reporting.
Virtual Nurse Avatar: Reducing Readmissions Through AI Engagement
Empathetic AI avatar conducts post-discharge follow-up, achieving <5% readmission rates
Challenge
Post-discharge patient follow-up is critical but difficult to scale. Hospitals face 20% readmission rates with $26 billion in annual penalties. Phone call follow-up reaches only 30-40% of patients, with nurses spending hours on unsuccessful calls. Patients forget instructions, don't recognise warning signs and delay seeking care until emergencies occur.
Reported approach
Sensely's AI-powered virtual nurse avatar—Molly—conducts check-ins via smartphone, asking about symptoms, medication adherence and concerns in natural conversation. The system uses speech recognition, sentiment analysis and clinical protocols to identify patients at risk of deterioration. High-risk patients are automatically escalated to clinical staff with full conversation context.
Reported outcomes
- ·Less than 5% hospital readmission rate for engaged patients
- ·85% patient engagement rate (vs. 35% for phone calls)
- ·Average conversation duration: 3 minutes (vs. 12 minutes for phone)
- ·4.5/5 patient satisfaction rating
- ·Reported deployments include NHS pilots and health system partnerships
What this signals for the sector
This entry is included as a research note about the broader healthcare AI landscape. It does not describe an Ajentik product, customer, or partnership. Readers can use the source links below to inspect the original reporting.
AI-Optimised Hospital Operations: From Bottlenecks to Flow
Machine learning platform optimises infusion centres ORs and bed management across 600+ hospitals
Challenge
Hospital departments face highly variable demand—cancer infusions, surgeries and admissions arrive unpredictably throughout the day. Static scheduling creates morning rushes and afternoon lulls, forcing patients to wait while capacity sits idle. The resulting bottlenecks cascade: ER boarding, surgical delays and staff overtime pile up costs while patients suffer.
Reported approach
LeanTaaS iQueue uses machine learning to predict demand patterns and optimise scheduling across infusion centres, operating rooms and inpatient beds. The system analyses historical patterns, treatment durations and real-time data to create optimal appointment templates. AI continuously learns from actual flow to improve predictions and identifies scheduling opportunities in real-time.
Reported outcomes
- ·20%+ improvement in capacity utilisation without adding resources
- ·$10+ million annual savings for large health systems
- ·Deployed across 600+ hospitals including Stanford, UCSF, Cleveland Clinic
- ·Patient satisfaction scores improved 30%
What this signals for the sector
This entry is included as a research note about the broader healthcare AI landscape. It does not describe an Ajentik product, customer, or partnership. Readers can use the source links below to inspect the original reporting.
Hospital-at-Home: AI-Enabled Acute Care Beyond Hospital Walls
Retail giant transforms into healthcare leader with 24/7 remote patient monitoring platform
Challenge
Hospital beds are expensive and scarce, yet many hospitalized patients don't require the intensive infrastructure of acute care. Patients prefer recovering at home but traditionally lacked the monitoring needed for safe acute-level care. COVID-19 accelerated demand for alternatives, but scaling hospital-at-home required technology that didn't exist.
Reported approach
Best Buy Health, through its acquisition of Current Health, deploys a complete hospital-at-home platform combining wearable continuous monitoring, video visits, AI-powered alert systems and care coordination. The platform monitors vital signs 24/7, with AI detecting deterioration patterns and routing alerts to clinical teams. Integration with health systems enables seamless acute-to-home transitions.
Reported outcomes
- ·Less than 10% hospital readmission rate
- ·Clinical outcomes equal or superior to inpatient care
- ·30% faster recovery times in home environment
- ·Expanded to 30+ health system partnerships
- ·Serving patients across 50 states
What this signals for the sector
This entry is included as a research note about the broader healthcare AI landscape. It does not describe an Ajentik product, customer, or partnership. Readers can use the source links below to inspect the original reporting.
Cross-Industry
Customer Service Revolution: Lessons for Healthcare Communication
Swedish fintech giant serving 150+ million users globally transforms customer service with AI
Challenge
Klarna struggled with 11-minute average resolution times and needed 24/7 multilingual support for millions of daily transactions across 35+ languages. The company required a solution that could handle sensitive financial information while maintaining high customer satisfaction and regulatory compliance.
Reported approach
Klarna deployed an OpenAI-powered autonomous AI assistant that handles 2.3 million conversations annually. The system processes natural language queries, makes autonomous decisions about refunds and payments and seamlessly escalates complex cases to human agents. It maintains context across conversations and provides personalised responses based on customer history.
Reported outcomes
- ·AI performs work equivalent to 700 full-time agents
- ·Expected $40+ million in annual profit improvement
- ·Supports 35+ languages with native-level fluency
What this signals for the sector
This entry is included as a research note about the broader healthcare AI landscape. It does not describe an Ajentik product, customer, or partnership. Readers can use the source links below to inspect the original reporting.
Erica: Blueprint for Healthcare Virtual Assistants
Serving 40+ million mobile banking customers with personalised AI guidance
Challenge
The bank faced overwhelming customer service demands with routine inquiries consuming significant human agent time. Customers needed immediate access to account information, transaction history and financial advice without waiting for human assistance. Traditional IVR systems frustrated customers and led to high abandonment rates.
Reported approach
Erica, launched in 2018, uses predictive analytics and natural language processing to provide comprehensive financial assistance. The AI agent autonomously handles balance inquiries, payment scheduling, spending analysis, fraud detection and personalised financial insights. It integrates seamlessly with mobile banking and learns from each interaction.
Reported outcomes
- ·Processed over 2 billion interactions since launch
- ·Handles 1.5 million daily interactions
- ·Maintains 98% query resolution rate without human intervention
- ·Contributed to 19% spike in earnings
- ·Reduced call centre volume by 50% for routine inquiries
- ·Achieved 4.7/5 customer satisfaction rating
What this signals for the sector
This entry is included as a research note about the broader healthcare AI landscape. It does not describe an Ajentik product, customer, or partnership. Readers can use the source links below to inspect the original reporting.
Devin AI Implementation: Scaling Healthcare Operations
Brazil's largest fintech transforms legacy systems with autonomous AI
Challenge
The company needed to refactor 100,000+ data class implementations in their 8-year-old, 6-million-line monolithic ETL system, manage complex cross-dependencies in legacy systems, avoid the massive resource allocation of traditional migration (1,000+ engineers for 18 months) and maintain system stability during transformation.
Reported approach
Nubank deployed Cognition Labs' Devin AI, an autonomous software engineering agent. Devin analysed the monolithic codebase, created migration strategies, generated modular sub-components and executed systematic refactoring. The AI learned from each task, improving performance over time.
Reported outcomes
- ·12x improvement in engineering hours saved
- ·20x cost savings compared to manual migration
- ·Reduction of task completion time from 40 to 10 minutes
- ·Completion of migrations in weeks instead of months/years
- ·Zero production incidents during migration
- ·Freed 1,000+ engineers for innovation work
What this signals for the sector
This entry is included as a research note about the broader healthcare AI landscape. It does not describe an Ajentik product, customer, or partnership. Readers can use the source links below to inspect the original reporting.
Editorial note:Outcome figures originate from Cognition AI marketing materials describing a customer engagement with Nubank. They have not been independently verified and should be read as vendor-reported, not independently audited.
Salesforce Agentforce: Insurance-Healthcare Convergence
150-year-old company serving 50 million customers across 50+ countries
Challenge
Prudential with 38,000 employees struggled with complex state-by-state insurance regulations, time-consuming claims processing across multiple business units, fragmented customer data preventing holistic service and significant manual effort in customer service operations requiring navigation of 50+ different regulatory frameworks.
Reported approach
Prudential implemented Salesforce Agentforce for Financial Services, deploying autonomous agents for customer identification, contract analysis, policy retrieval and claims processing. The system features human-in-the-loop oversight for regulated operations and multi-LLM architecture for specialised functions.
Reported outcomes
- ·Saved at least half a day per week per customer service representative
- ·Eliminated hundreds of manual routing workflows
- ·Enhanced customer empathy through reduced administrative burden
- ·Significant productivity improvements in wholesaler operations
- ·100% regulatory compliance maintained
- ·$12M annual operational savings
What this signals for the sector
This entry is included as a research note about the broader healthcare AI landscape. It does not describe an Ajentik product, customer, or partnership. Readers can use the source links below to inspect the original reporting.
Editorial note:Outcome figures originate from Salesforce customer-success marketing for Agentforce. They have not been independently verified and should be read as vendor-reported, not independently audited.
UiPath Implementation: Enterprise-Scale Healthcare Automation
Global bank establishes Process Automation Center of Excellence
Challenge
Barclays faced complex mortgage processing requiring 20+ document types, need for regulatory compliance across multiple jurisdictions, high-volume transactional processing demands (10,000+ daily) and significant manual effort in eligibility assessment and risk evaluation taking 3-5 days per application.
Reported approach
Using UiPath's platform, Barclays deployed document understanding agents for loan applications, eligibility assessment agents with reasoning capabilities, risk evaluation agents with regulatory compliance and customer communication agents. The system features UiPath Maestro for orchestration across agents, robots and humans.
Reported outcomes
- ·98% straight-through processing with only 2% requiring human intervention
- ·12,000 hours saved annually through automation
- ·Processing time reduced from 3-5 days to minutes
- ·Minimum 30% efficiency benefit across all implementations
- ·£25M annual cost savings
- ·Scaled to multiple automated processes
What this signals for the sector
This entry is included as a research note about the broader healthcare AI landscape. It does not describe an Ajentik product, customer, or partnership. Readers can use the source links below to inspect the original reporting.
Editorial note:Outcome figures originate from UiPath customer-story marketing. They have not been independently verified and should be read as vendor-reported, not independently audited.
Microsoft Copilot Studio: Supply Chain Intelligence
Global materials company transforms supply chain with AI automation
Challenge
Dow struggled with manual processing of 100,000+ PDF invoices yearly from 5,000+ suppliers, difficulty detecting billing inaccuracies and anomalies costing $3-5M annually, time-consuming freight rate investigations taking weeks or months per dispute and lack of visibility into cost optimisation opportunities across global supply chain.
Reported approach
Using Microsoft Copilot Studio, Dow deployed autonomous invoice scanning agents for billing analysis and a natural language "Freight Agent" for investigation. The system features automatic anomaly detection, pattern recognition for cost optimisation, dashboard integration for employee review and conversational interface for deep analysis.
Reported outcomes
- ·Expects $5+ million in savings within the first year
- ·Reduced investigation time from weeks/months to minutes
- ·Increased accuracy to 99.5% in logistics billing
- ·Scaled to handle 100,000+ invoices without additional staff
- ·Identified $2M in overcharges in first quarter
- ·Enabled non-technical staff to perform complex analyses
What this signals for the sector
This entry is included as a research note about the broader healthcare AI landscape. It does not describe an Ajentik product, customer, or partnership. Readers can use the source links below to inspect the original reporting.
Editorial note:Outcome figures originate from Microsoft customer-story marketing (Microsoft Transform / Source) describing a Dow engagement. They have not been independently verified and should be read as vendor-reported, not independently audited.
Want to discuss your own deployment?
These research notes describe other organisations' work. If you'd like to talk to us about your own healthcare AI plans, contact our team.