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Augmented Intelligences
Return to all trendsWhat is it?
AI’s true promise lies in augmenting human cognitive tasks—learning, reasoning, problem-solving, and creativity—rather than merely automating routine jobs. While current AI applications often focus on eliminating low-value tasks, such as scheduling or note-taking, this narrow view overlooks AI’s potential to enhance human intelligence and creativity.
AI’s role should not be confined to routine tasks. History has shown that automation does not always reduce workload. Instead, AI can enhance human capabilities, making clinicians more insightful and creative, and accelerating the journey from novice to expert.
It can fundamentally transform healthcare by:
- Predicting and preventing health issues before they arise.
- Enhancing diagnostics with unprecedented speed and accuracy.
- Prioritizing healthcare delivery and resource management across systems.
- Continuously tracking and optimizing patient care and treatment plans.
AI has the potential to deliver on the “social contract of data” by maximizing the value of collected health data. It can provide real-time analyses, empowering decision-makers and integrating human and machine thinking for more effective healthcare.
In essence, AI should be at the core, not the periphery, of tomorrow’s healthcare systems, driving profound improvements in patient care, diagnostics, and healthcare delivery.
Opportunities for impact
The potential impact of AI across the core functions of the health system and its operations are extensive, touching almost every major element of the system.
Here are some illustrative applications and possibilities for AI across some of the key functions of the health system, using the system impact framework discussed in the report on page 16.
Patient care & wellbeing
Health need prediction and prevention
Disease prediction
AI models can predict disease outbreaks and progression, enabling proactive care and preventive measures. This helps in early intervention, redesng th nincidence of severe disease, and improves overall population health.
AI models can predict disease outbreaks and progression, enabling proactive care and preventive measures. This helps in early intervention, redesng th nincidence of severe disease, and improves overall population health.
Risk assessment
AI can assess patient risks, suggesting preventive actions to reduce the likelihood of developing serious conditions. This enhances patient safety, prevents complications, and lowers healthcare costs by avoiding emergency situations.
AI can assess patient risks, suggesting preventive actions to reduce the likelihood of developing serious conditions. This enhances patient safety, prevents complications, and lowers healthcare costs by avoiding emergency situations.
Epidemiological analysis
AI can analyse epidemiological data to identify trends and potential health threats, aiding public health planning and interventions. This supports timely public health responses, improves resource allocation, and enhances community health outcomes.
AI can analyse epidemiological data to identify trends and potential health threats, aiding public health planning and interventions. This supports timely public health responses, improves resource allocation, and enhances community health outcomes.
Patient education
AI can analyse patient data and provide personalised educational content to patients on self-care and device usage through interactive apps and virtual assistants, improving their understanding and engagement in their own care. This empowers patients, enhances health literacy, improves chronic disease management and supports better health outcomes through informed decision-making.
AI can analyse patient data and provide personalised educational content to patients on self-care and device usage through interactive apps and virtual assistants, improving their understanding and engagement in their own care. This empowers patients, enhances health literacy, improves chronic disease management and supports better health outcomes through informed decision-making.
Triage and diagnosis
Medical imaging analysis
AI algorithms can analyse medical images quickly and accurately, assisting radiologists and other clinical professionals in diagnosing conditions more efficiently and accurately. This reduces the workload on clinicians, decreases human error, and speeds up diagnosis time for patients.
AI algorithms can analyse medical images quickly and accurately, assisting radiologists and other clinical professionals in diagnosing conditions more efficiently and accurately. This reduces the workload on clinicians, decreases human error, and speeds up diagnosis time for patients.
Pathological examination
AI can analyse laboratory results to detect patterns and anomalies, helping lab technicians and clinicians diagnose diseases faster. This improves diagnostic accuracy, reduces turnaround time for lab results, and enhances patient care.
AI can analyse laboratory results to detect patterns and anomalies, helping lab technicians and clinicians diagnose diseases faster. This improves diagnostic accuracy, reduces turnaround time for lab results, and enhances patient care.
Diagnostic decision making
AI can support clinicians by providing diagnostic suggestions and identifying potential issues based on patient data. This improves diagnostic accuracy, reduces diagnostic errors, and enhances clinical decision-making.
AI can support clinicians by providing diagnostic suggestions and identifying potential issues based on patient data. This improves diagnostic accuracy, reduces diagnostic errors, and enhances clinical decision-making.
Treatment and patient care delivery
Treatment planning
AI can offer personalised, evidence-based treatment plans by analysing patient data and suggesting tailored therapies, improving treatment outcomes. This ensures more accurate and effective treatments, enhances patient satisfaction, and optimises care management.
Medication management
AI can recommend optimal medication dosages and schedules, reducing adverse drug reactions and improving therapeutic outcomes. This minimises medication errors, enhances patient safety, and improves treatment efficacy.
Patient monitoring
AI processes data from wearables and other monitoring devices, providing real-time insights and alerts to healthcare providers for timely interventions. This improves patient monitoring, enables early detection of issues, and enhances chronic disease management.
Patient support
AI-powered virtual assistants and chatbots offer 24/7 support, answering patient queries and providing guidance. This improves patient satisfaction, ensures adherence to treatment plans, and reduces the burden on healthcare providers.
System Infrastructure
Operational, administrative and resource management
Resource allocation
AI can optimise resource allocation by predicting patient admission rates and suggesting efficient staffing and bed management strategies. This improves hospital efficiency, reduces waiting times, and ensures better patient care through optimal resource utilisation.
AI can optimise resource allocation by predicting patient admission rates and suggesting efficient staffing and bed management strategies. This improves hospital efficiency, reduces waiting times, and ensures better patient care through optimal resource utilisation.
Appointment scheduling
AI automates scheduling, optimising patient flow and reducing administrative burdens. This enhances patient satisfaction, reduces no-shows, and improves the overall efficiency of healthcare delivery.
AI automates scheduling, optimising patient flow and reducing administrative burdens. This enhances patient satisfaction, reduces no-shows, and improves the overall efficiency of healthcare delivery.
Supply chain management
AI can forecast demand for medical supplies, maintaining optimal inventory levels and reducing waste. This ensures the timely availability of necessary supplies, reduces costs, and improves the overall efficiency of the healthcare supply chain.
AI can forecast demand for medical supplies, maintaining optimal inventory levels and reducing waste. This ensures the timely availability of necessary supplies, reduces costs, and improves the overall efficiency of the healthcare supply chain.
Information flow and communications
Privacy and security
AI enhances data security by monitoring for breaches and ensuring compliance with data protection regulations. This protects patient privacy, builds trust in the healthcare system, and ensures compliance with legal requirements.
AI enhances data security by monitoring for breaches and ensuring compliance with data protection regulations. This protects patient privacy, builds trust in the healthcare system, and ensures compliance with legal requirements.
Health records management
AI can automate EHR management, ensuring records are up-to-date and reducing manual entry errors. This improves data accuracy, enhances clinical workflows, and ensures clinicians have access to comprehensive patient information for better care.
AI can automate EHR management, ensuring records are up-to-date and reducing manual entry errors. This improves data accuracy, enhances clinical workflows, and ensures clinicians have access to comprehensive patient information for better care.
Data analysis
AI can identify trends and patterns in healthcare data, providing insights for improving patient care and operational efficiency. This enables data-driven decision-making, enhances health outcomes, and supports effective healthcare policies.
Emergency response
AI can enhance emergency response by analysing real-time data and offering decision support during critical situations. This improves response times, enhances patient outcomes in emergencies, and supports emergency medical personnel.
Skill and capability development
Professional development
AI can deliver personalised training modules and continuous education, helping healthcare professionals stay current with medical advancements. This enhances professional development, improves clinical skills, and ensures high standards of patient care.
AI can deliver personalised training modules and continuous education, helping healthcare professionals stay current with medical advancements. This enhances professional development, improves clinical skills, and ensures high standards of patient care.
Medical education
AI-driven simulators and virtual reality environments provide immersive learning experiences for medical students. This improves hands-on learning, accelerates skill acquisition, and prepares students for real-world clinical
Skill acquisition
AI-based training programs offer interactive and adaptive learning experiences, helping healthcare workers acquire new skills efficiently. This improves staff competencies, enhances patient care, and supports continuous professional development.
Who's doing it
Predictive intelligence for mosquito-borne diseases
Who: Blue Dot
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What: BlueDot employs predictive intelligence to forecast global climatic suitability for Aedes albopictus and Aedes aegypti mosquitoes under various climate change scenarios projected for the next decade. This allows health services to plan for any forecast increases in the incidence of mosquito-borne illnesses. BlueDot's methodology utilises a gradient-boosted regression tree model, integrating data on precipitation, surface temperature, and elevation. Historical mosquito occurrence data informs the model, which predicts suitability at a detailed resolution of 5km by 5km. The model accounts for three climate change pathways: SSP 1-2.6 (best-case scenario), SSP 2-4.5 (most-likely scenario), and SSP 5-8.5 (worst-case scenario) from the 6th Coupled Model Intercomparison Project.AI-powered breast cancer screening
Who: ScreenPoint Medical
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What: ScreenPoint Medical has created Transpara®, an advanced AI solution for breast cancer screening. Developed over a decade, Transpara® analyses mammograms to assist radiologists by highlighting potential areas of concern, enhancing the accuracy and efficiency of breast cancer detection. It identifies abnormalities and prioritizes cases by cancer likelihood, allowing radiologists to focus on the most critical cases. This AI system integrates seamlessly into existing workflows, improving diagnostic confidence and reducing healthcare professionals' workloads. Transpara® significantly advances breast cancer screening by providing earlier and more reliable detection, thereby optimizing healthcare resources. Links: ScreenPoint MedicalAI-powered medication safety
Who: MedAware
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What: MedAware has developed an AI-driven platform to enhance medication safety by identifying and preventing prescription errors. Leveraging extensive data from electronic health records (EHRs), MedAware's system utilizes machine learning algorithms to detect anomalies and potential adverse drug events (ADEs). The technology analyses prescription patterns, comparing them to historical data to flag deviations that could indicate errors. By integrating seamlessly into existing healthcare workflows, MedAware's solution supports healthcare providers in making safer, more informed prescribing decisions, thereby reducing the risk of medication errors and improving patient outcomes. The platform also addresses challenges like alert fatigue by refining the accuracy of its notifications, ensuring that healthcare professionals receive only the most pertinent alerts. This approach not only enhances patient safety but also optimizes the efficiency of healthcare operations.AI-powered healthcare operations automation
Who: Qventus
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What: Qventus offers an AI-driven platform designed to optimize hospital operations by predicting bottlenecks, recommending solutions, and automating processes through seamless EHR integration. The platform improves surgical scheduling, discharge planning, and resource utilization, enhancing efficiency and reducing length of stay. By combining real-time data and machine learning, Qventus helps healthcare providers create capacity, reduce manual work, and increase revenue. Trusted by top healthcare institutions, Qventus delivers significant ROI and improves patient care through intelligent automation.AI-powered security with Cisco Hypershield
Who: Cisco, in collaboration with NVIDIA and Isovalent
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What: Introduced in 2024, Cisco Hypershield is a groundbreaking security architecture designed for the AI era. Built from the ground up with AI-native technology, it redefines how data centres and cloud environments are secured. Hypershield provides autonomous segmentation, distributed exploit protection, and self-qualifying upgrades. It leverages the power of AI to automatically segment networks, identify and shield vulnerabilities before they can be exploited, and deploy upgrades without downtime. This AI-driven approach significantly enhances security while reducing the complexity and cost traditionally associated with manual processes.
Go toMedtronic Touch Surgery™
Who: Medtronic
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What: Touch Surgery™ is an AI-powered ecosystem that enhances surgical procedures through digital solutions. It offers tools for video capture, performance insights, live streaming, simulations, and connectivity. These features enable surgeons to turn complex data into actionable insights, track progress, and improve surgical efficiency. The platform integrates next-generation computing and visualization technology, supporting surgeons before, during, and after surgery.Actions 2024-2025
The potential for AI technologies to revolutionise nearly every major function of the health system is staggering. And the focus in technology and healthcare circles in developing applications has been laudable. That said, these technologies – though prominent and visible – require a good amount of further development before they are ready for at-scale deployment, as does the technology infrastructure in the health system on which they will rely.
As such, we recommend that actors from across the health and technology ecosystems disproportionately focus in 2024-25 on setting the foundations, activating and putting in place building blocks across several domains of the system to scope, position for, and experiment in AI deployments in key strategic areas.
As such, we recommend that actors from across the health and technology ecosystems disproportionately focus in 2024-25 on setting the foundations, activating and putting in place building blocks across several domains of the system to scope, position for, and experiment in AI deployments in key strategic areas.
Healthcare providers
Foundations
- Establish partnerships to drive AI innovation and implementation.
- Define specific goals, desired impacts, and assess AI’s appropriateness for specified challenges.
- Consider and resource human oversight and supplementary decisions to mitigate risks of AI application.
- Prioritise AI initiatives that enhance patient safety and health outcomes.
- Build and maintain robust data pipelines, warehouses and data management systems to ensure high-quality, structured health and other data for AI training and operation.
- Implement advanced cybersecurity protocols and conduct regular audits to protect sensitive health data.
- Standardise data formats and collaborate to enhance data interoperability and streamline AI integration.
Activate
- Invest in technology professionals with the skills to distinguish between AI tools that are fit for purpose and those that are mere hype.
- Develop health specific training in AI management for technology professionals.
- Start with narrow AI applications to health settings (e.g., imaging analysis, sepsis detection, operational domains).
- Foster collaboration between technology companies and healthcare providers to develop AI tools tailored to specific clinical and operational needs.
- Implement strategies to identify and mitigate biases in AI models and ensure transparency.
- Create platforms for sharing best practices and lessons learned from AI implementations
- Work with tech developers to provide clinical insights and advocate for necessary resources and support.
- Actively explore use of existing AI tools in diagnostics, treatment planning, and patient monitoring.
Technologists
Foundations
- Establish partnerships to drive AI innovation and implementation.
- Define specific goals, desired impacts, and assess AI’s appropriateness for specified challenges.
- Consider and resource human oversight and supplementary decisions to mitigate risks of AI application.
- Prioritise AI initiatives that enhance patient safety and health outcomes.
- Build and maintain robust data pipelines, warehouses and data management systems to ensure high-quality, structured health and other data for AI training and operation.
- Implement advanced cybersecurity protocols and conduct regular audits to protect sensitive health data.
- Standardise data formats and collaborate to enhance data interoperability and streamline AI integration.
Activate
- Invest in technology professionals with the skills to distinguish between AI tools that are fit for purpose and those that are mere hype.
- Develop health specific training in AI management for technology professionals.
- Start with narrow AI applications to health settings (e.g., imaging analysis, sepsis detection, operational domains).
- Foster collaboration between technology companies and healthcare providers to develop AI tools tailored to specific clinical and operational needs.
- Implement strategies to identify and mitigate biases in AI models and ensure transparency.
- Create platforms for sharing best practices and lessons learned from AI implementations
- Work with tech developers to provide clinical insights and advocate for necessary resources and support.
- Actively explore use of existing AI tools in diagnostics, treatment planning, and patient monitoring.
Government & Policy Makers
Foundations
- Strengthen regulations around data privacy and security to protect patient information.
- Develop and enforce AI use standards in healthcare to ensure safety, efficacy, and ethical use.
- Provide funding and incentives for AI research and development in healthcare.
- Establish partnerships to drive AI innovation and implementation.
- Define specific goals, desired impacts, and assess AI’s appropriateness for specified challenges.
- Consider and resource human oversight and supplementary decisions to mitigate risks of AI application.
- Prioritise AI initiatives that enhance patient safety and health outcomes.
Activate
- Start with narrow AI applications to health settings (e.g., imaging analysis, sepsis detection, operational domains).
- Foster collaboration between technology companies and healthcare providers to develop AI tools tailored to specific clinical and operational needs.
- Implement strategies to identify and mitigate biases in AI models and ensure transparency.
- Create platforms for sharing best practices and lessons learned from AI implementations
- Work with tech developers to provide clinical insights and advocate for necessary resources and support.
- Actively explore use of existing AI tools in diagnostics, treatment planning, and patient monitoring.
Researchers & Universities
Foundations
- Establish partnerships to drive AI innovation and implementation.
- Define specific goals, desired impacts, and assess AI’s appropriateness for specified challenges.
- Consider and resource human oversight and supplementary decisions to mitigate risks of AI application.
- Prioritise AI initiatives that enhance patient safety and health outcomes.
Activate
- Invest in technology professionals with the skills to distinguish between AI tools that are fit for purpose and those that are mere hype.
- Develop health specific training in AI management for technology professionals.
- Start with narrow AI applications to health settings (e.g., imaging analysis, sepsis detection, operational domains).
- Foster collaboration between technology companies and healthcare providers to develop AI tools tailored to specific clinical and operational needs.
- Implement strategies to identify and mitigate biases in AI models and ensure transparency.
- Create platforms for sharing best practices and lessons learned from AI implementations
- Work with tech developers to provide clinical insights and advocate for necessary resources and support.
- Actively explore use of existing AI tools in diagnostics, treatment planning, and patient monitoring.
Simulation and Simulacra
As the boundary between the physical and digital worlds fades, new technologies are transforming how we interact with reality, particularly in healthcare. Extended Reality (XR), digital twins, and 3D printing are at the forefront of this change, allowing us to experiment, innovate, and enhance care without the constraints of the physical world.
Remote Patient Care
Remote Patient Care (RPC) leverages modern communication technologies and sensors to deliver healthcare services when the patient and provider are not in the same physical space. While the concept isn’t new, recent technological advancements have made RPC more effective, allowing patients to receive hospital-level care from the comfort of their homes.
Adaptability and Dynamism
Adaptability in health refers to the creation of flexible systems designed to anticipate and respond to future challenges. Unlike specific technologies applied to healthcare, adaptability is a system-wide capability made possible by technology, allowing healthcare systems to adjust seamlessly to changing demands and stressors.
Harnessing Biotechnology Breakthroughs
Throughout history, healthcare has advanced through groundbreaking scientific discoveries, such as antibiotics, DNA structure, and vaccines. These innovations have dramatically improved health outcomes and set the stage for modern advancements in biotech and deep science.