Context

How AI and automation entered health

Digital health developed through electronic records, laboratory systems, imaging, telemedicine, and connected monitoring devices. As larger collections of health data became available, AI methods could identify patterns and support specific tasks. Automation expanded these capabilities by moving information, organizing routine work, and triggering predefined steps.

AI does not function as a single technology. It can include machine learning models, language-processing systems, computer vision, predictive models, and newer generative AI systems. WHO describes AI as algorithms integrated into systems that can learn from data and perform automated tasks without explicit programming for every step.

What the workflow looks like

A typical AI and automation workflow can involve several stages:

• Data collection: Information may come from electronic records, medical images, laboratory systems, connected devices, or public-health databases.
• Data preparation: Information is checked, structured, labeled, or converted into formats that software can process.
• AI analysis: A model examines patterns or generates an output based on its training and the information supplied.
• Automation: A workflow system can route results, create a draft summary, flag an item for review, or update a record.
• Human review: A qualified person checks relevant outputs before decisions that require professional judgment.

AI output is not automatically a clinical conclusion. Data quality, model design, context, and human review can affect the result.

Importance

Problems it can address

Health organizations handle large amounts of information and many repetitive processes. Staff may need to review records, organize reports, prepare documentation, monitor populations, or coordinate information across different systems. Automation can reduce repetitive manual steps, while AI can assist with information analysis.

For patients and the public, potential uses include easier access to health information, digital record organization, appointment-related workflows, remote monitoring, and tools that help clinicians examine certain types of data. WHO identifies applications ranging from diagnosis and clinical care to disease surveillance, public-health work, research, and health-system management.

Benefits and limitations

Potential benefits depend on the use case and implementation. AI can process large datasets, help identify patterns, support documentation, and assist with routine analysis. Automation can make workflows more structured and reduce repeated data entry.

There are also limitations. AI systems can produce incorrect or incomplete outputs, reflect weaknesses in training data, or behave differently with populations or conditions that were not adequately represented during development. Privacy, cybersecurity, transparency, accountability, accessibility, and human oversight remain central considerations.

Recent Updates

Generative AI and multimodal systems

From 2024 through 2026, attention has increasingly moved toward generative AI and large multimodal models. These systems can work with more than one type of input, such as text, images, or other data formats. WHO published guidance on ethics and governance for large multimodal models in 2025, noting their potential applications in healthcare, scientific research, public health, and drug development while emphasizing the need for appropriate governance.

Generative AI has also been explored for health information and communication. In 2024, WHO introduced S.A.R.A.H., a generative-AI digital health promoter prototype designed to provide health information through interactive conversations. The example illustrates how conversational systems can support information access while also showing why accuracy and appropriate boundaries matter.

Digital health infrastructure

Another continuing trend is the connection of AI with digital health infrastructure. In India, the Ayushman Bharat Digital Mission supports digital health records, health identifiers, interoperability, and consent management. Government material also describes AI work involving screening, follow-up, disease surveillance, and clinical decision support.

The broader direction is toward integrated workflows rather than isolated AI tools. This means future systems may combine records, imaging, laboratory information, workflow automation, and analytical models while maintaining controls for access and review.

Governance and responsible use

During this period, international health organizations have placed increasing emphasis on responsible AI. WHO has highlighted safety, equity, governance, data quality, privacy, and accountability as important parts of AI adoption. The World Health Assembly also extended the Global Strategy on Digital Health through 2027 and initiated work toward a subsequent strategy for 2028–2033.

Technical progress is therefore occurring alongside work on standards, oversight, workforce skills, and public trust.

Laws or Policies

India’s data protection framework

For India, AI systems that process personal information need to be considered alongside the Digital Personal Data Protection Act, 2023 and the Digital Personal Data Protection Rules, 2025. The Rules were notified in November 2025 and establish an implementation framework for personal-data protection, including requirements concerning notices, consent, and responsibilities for organizations handling personal data.

Health information can be particularly sensitive, so organizations using AI should consider data minimization, access controls, security measures, appropriate consent processes, retention practices, and accountability. The exact legal obligations can depend on the organization, processing activity, and applicable provisions.

Digital health policies

The Ayushman Bharat Digital Mission provides an important policy framework for India's digital health ecosystem. Its personal health record approach allows individuals to view health information and manage consent for sharing records.

Healthcare AI may also interact with existing clinical, medical-device, privacy, cybersecurity, and professional requirements. The applicable rules depend on what an AI system does and how it is used. A tool that organizes information may be treated differently from software that performs a regulated medical function.

Organizations should assess intended use, data involved, risk level, validation evidence, and applicable requirements before deploying AI in a health setting. General information about these rules is not legal or medical advice.

Tools and Resources

Digital health records

Digital health record platforms can help individuals organize laboratory reports, prescriptions, discharge information, and other records. In India, the ABDM ecosystem includes the ABHA platform and personal health record functions that support viewing and consent management.

AI documentation and analysis tools

AI-enabled documentation tools can assist with transcription, information extraction, summarization, and classification. Their usefulness depends on the data source, accuracy controls, integration with existing systems, and review procedures.

Public health and research resources

Government health portals, WHO resources, medical literature databases, and public datasets can help readers understand how AI is being evaluated. These resources help readers compare evidence, learn terminology, and follow policy developments.

Workflow planning templates

A simple workflow template can document:

• Purpose of the AI task
• Data sources and data permissions
• Input and output formats
• Automated steps
• Human review points
• Error handling
• Security controls
• Performance monitoring
• Record retention
• Responsible personnel

Documentation helps clarify where automation begins, where AI is involved, and where human judgment remains necessary.

FAQs

What is AI and automation in health?

AI and automation in health combines artificial intelligence with software workflows to analyze information, generate outputs, or perform repeatable tasks. It can support clinical, administrative, research, and public-health activities, depending on the system's design and approved use.

How is AI used in healthcare?

AI can assist with medical image analysis, information extraction, documentation, pattern recognition, public-health monitoring, research, and patient-information tools. The appropriate use depends on validation, data quality, human oversight, and applicable rules.

What are the benefits of AI automation in healthcare?

Potential benefits include faster information processing, reduced repetitive work, more structured workflows, and support for analysis. Results vary by application, and AI outputs can require professional verification.

Is AI in healthcare safe?

Safety depends on the specific system, its data, validation, monitoring, cybersecurity, and how people use its outputs. AI should not automatically replace professional judgment, particularly for decisions involving diagnosis or treatment.

What are the future developments in AI and automation in health?

Future developments may include multimodal AI, more connected digital records, automated administrative workflows, clinical decision-support tools, remote monitoring, and stronger governance frameworks. The direction of development will depend on evidence, regulation, technical progress, and public expectations.

Conclusion

AI & Automation in Health brings together artificial intelligence, digital records, connected technologies, and automated workflows. Its applications range from information management and documentation to image analysis, public-health monitoring, and research support. Recent developments have increased attention on generative AI, multimodal systems, interoperability, privacy, and responsible governance. The role of human review remains important because AI outputs depend on data, system design, context, and appropriate oversight.