AI Training for Healthcare & Life Sciences

How can healthcare and life sciences teams use AI in their daily work while protecting sensitive data?

Excentra AI Academy’s AI training courses for healthcare and life sciences professionals focuses on practical, non-clinical work. A hospital team might use AI to forecast demand for appointments, while a research team might use it to organise published evidence. In both settings, staff need to check the information behind an output and decide when it is reliable enough to use.

The training is designed for hospitals, health authorities, health insurers and life sciences organisations. Examples draw on service planning, quality reporting, internal policy searches and research documentation. They show how teams can limit access to sensitive information, verify outputs against approved sources and assign responsibility for review.

Browse the courses below Request in-house training

In short

AI training for healthcare organisations helps staff choose suitable tasks, handle patient and research data appropriately and check AI outputs before acting on them. For example, a hospital planning team can use aggregated appointment data to estimate demand, while a life sciences team can use AI to draft a literature summary and verify each finding against the original publication. Training connects these tasks to data access, source verification and human review.

Where AI is used in Healthcare & Life Sciences

01

Information governance and privacy

Check whether an AI use requires identifiable patient or research data. Limit access to authorised staff and keep records of how sensitive information is used.

02

Knowledge systems and documentation

Help staff find current administrative policies, procedures and approved templates. Show the source document so users can verify the answer and check that the version is current.

03

Operational analytics and planning

Forecast appointment volumes or service demand using historical data. Compare forecasts with actual activity before changing staffing or appointment capacity.

04

Quality and safety analytics

Group themes in complaints, incident reports and service feedback so quality teams can identify patterns and review the underlying reports.

05

Research and life sciences support

Organise literature searches, summarise published evidence and prepare draft study or regulatory documents for specialist review. Keep the sources traceable.

06

Human-AI teamwork

Assign who checks drafts and forecasts, who resolves exceptions and who signs off before outputs are used in a report or operational decision.

Artificial Intelligence (AI) in Asset Lifecycle Management

Optimizing Asset Strategy, Reliability, and Value Through Intelligent Technologies

5 days · Classroom or live online · In-house available

View course →

Artificial Intelligence (AI) in Human Resource Management (HRM)

Evaluating AI Applications for Better People Decisions, Employee Experience and Responsible HR Practice

5 days · Classroom or live online · In-house available

View course →

Start here in any sector

Thirty Excentra courses apply whatever the industry. These four are where most organisations begin.

Browse all courses by subject →

Generative AI

Turn Ideas and Information into Valuable Work with Generative AI

View course →

AI Leadership, Strategy & Transformation

Set Direction, Prioritise Investment and Lead Organisational AI Adoption

View course →

AI Governance, Risk & Responsible AI

Establish Accountability, Assess Risk and Strengthen Oversight of AI Use

View course →

AI for Business Functions

Connect AI Applications with the Responsibilities of Your Profession

View course →

Who attends

Executives, medical directors and board members: Executives, medical directors and board members who oversee AI investment and accountability for non-clinical use.
Information governance, privacy and compliance: Information governance, privacy and compliance professionals who review how AI tools access and handle sensitive data.
Operations, planning and quality teams: Operations, planning and quality teams who manage appointment capacity, service performance, incident reporting and improvement initiatives.
Research, regulatory and life sciences teams: Research, regulatory and life sciences teams who organise scientific evidence, support study operations and prepare regulatory documentation.

Standards the courses draw on

These learning references put practical AI use in the context of health-sector ethics, data protection and organisational oversight.

  • WHO guidance on ethics and governance of AI for health. Principles for protecting autonomy, promoting safety and fairness, and assigning accountability when AI is used in health settings.
  • Data protection law, such as the GDPR. The GDPR provides an example of how data minimisation and additional protections for health data apply where the law is relevant.
  • ISO 13485 and medical device regulation. ISO 13485 sets out quality management requirements for medical devices. Medical device guidance provides context for how the intended purpose of software affects its regulatory classification.
  • ISO/IEC 42001 and the NIST AI Risk Management Framework. References for establishing an AI management system and assessing and managing risks associated with AI use.

In-house training for healthcare & life sciences teams

In-house training for healthcare teams: Excentra AI Academy can tailor AI training to your organisation’s workflows, policies and data access rules. A hospital might focus on appointment planning or quality reporting, while a life sciences team might work on literature review or regulatory documentation. Exercises can use synthetic data and approved sample documents.

Request an in-house proposal

What AI governance training do healthcare organisations need?

Healthcare organisations need AI governance training that helps staff assess a proposed use, identify the data it requires, set access rules and decide who checks the results. For example, before approving a hospital policy assistant, an information governance team can define which documents it can access, who owns those documents and how staff should report an incorrect or outdated answer. 

How can hospitals use AI in non-clinical operations?

Hospitals can use AI to forecast service demand, support appointment scheduling and identify recurring issues in patient feedback. For example, a planning team can compare an appointment demand forecast with recent attendance and available capacity before adjusting clinic schedules.

Related Articles

Seven Everyday Tasks You Can Hand to an AI Assistant (And Three You Should Not)

Read article →

What is an AI agent, and how is it different from a chatbot?

Read article →

AI Process Automation: 5 Questions to Ask Before You Start

Read article →

Frequently asked questions

Excentra’s healthcare training focuses on non-clinical applications. It covers tasks such as forecasting service demand, reviewing administrative documents and organising evidence for research and regulatory teams. Examples show how staff can check outputs and decide when a task needs specialist review.

First check whether the task requires identifiable data and whether the tool is approved for that type of information. Where the GDPR applies, processing health data generally requires a lawful basis and an applicable condition for handling special category data. Teams should also check who can access the data and what is retained in prompts, outputs or logs. 

An AI assistant can retrieve relevant passages from approved, current documents and show staff where the information came from. Access should follow existing permissions. If the source is missing, outdated or ambiguous, the assistant should direct the user to the policy owner for clarification.

Life sciences teams can use AI to organise literature searches, extract details for an evidence table and draft summaries for regulatory writers. Researchers and regulatory specialists should check the original references, verify extracted details and review drafts before the information is used in formal documentation. 

No. Exercises can use synthetic records, public research material or approved anonymised examples. For instance, a hospital can practise reviewing an appointment demand forecast using a synthetic dataset that reflects its scheduling workflow.

Find the AI training your healthcare & life sciences team needs

Browse all industries Find a course Request in-house training