Training course · Google Gemini · Pharma & Life Sciences

Google Gemini for Pharma and Life Sciences

Make productive use of Google Gemini and Gemini in Workspace: Docs, Sheets, Gmail, Meet, custom Gems and NotebookLM for your sources.

Duration
1 day (7 hrs)
Format
Online or on-site
Language
German or English
Participants
open dates or exclusive for your team
Certificate
Certificate of attendance

Overview

“Google Gemini for Pharma and Life Sciences” is designed for roles such as Regulatory affairs professionals, Quality assurance and Qualified Persons and Clinical operations and medical writers and shows step by step how to use Google Gemini productively, safely and transparently in Pharma & Life Sciences – from the fundamentals to advanced workflows.

For organisations running on Google Workspace, Gemini is the natural AI assistant: it is available as a standalone app and is also built directly into Gmail, Docs, Sheets, Slides, Drive and Meet. In this practical day you learn to combine both access points deliberately. You summarise email threads and draft replies, create document drafts based on existing files in Drive, structure and analyse spreadsheets in Sheets and have Meet take meeting notes automatically – always with a focus on your own day-to-day workflows.

You also build your own Gems: specialised assistants with fixed instructions and knowledge files that standardise recurring tasks across the team. With Deep Research you produce well-founded research reports, and you use NotebookLM as a source-grounded workspace that answers only from your own documents and cites the relevant passages. A dedicated module covers data protection and administration in Workspace environments. You leave with tested prompts, a working Gem and a clear picture of which Google tool suits which task.

Pharmaceutical, biotech and medtech companies operate in one of the most heavily regulated environments of all. Documentation, validation and traceability shape daily work from research and clinical trials to manufacturing. Generative AI offers significant potential: AI assistants review scientific literature, help draft study protocols, regulatory documents and SOPs, summarise deviation reports, support pharmacovigilance case processing and prepare medical information for healthcare professionals. Automation supports change control, CAPA tracking and regulatory intelligence. At the same time, as soon as AI touches GxP-relevant processes, validation, data integrity according to ALCOA+ and clear accountability are mandatory. Adoption succeeds when companies clearly separate non-regulated support from GxP use and involve quality assurance early.

The course is delivered live – either online in a virtual classroom or on-site in our training rooms in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart and Kaiserslautern. We can also run it exclusively for your team.

Who is this course for?

  • Regulatory affairs professionals
  • Quality assurance and Qualified Persons
  • Clinical operations and medical writers
  • Pharmacovigilance and drug safety teams
  • Medical affairs and R&D scientists

Typical challenges we address

  • Producing extensive regulatory and scientific documents to a high standard
  • Processing deviations, CAPAs and change controls on time
  • Systematically evaluating growing volumes of literature and regulation
  • Using AI tools in a GxP-compliant, validatable way

What you will be able to do

  • You use Gemini in Gmail, Docs, Sheets, Slides and Meet for your recurring tasks.
  • You write prompts that bring existing Drive files and emails into the context.
  • You build a custom Gem and share it with your team.
  • You work source-grounded in NotebookLM and obtain referenced answers from your own documents.
  • Use AI for literature review, medical writing and regulatory intelligence.
  • Distinguish non-regulated AI use from GxP-relevant applications and understand validation requirements.
  • Safeguard data integrity, confidentiality and patient data protection when using AI.

Course outline

Module 1 Gemini fundamentals and effective prompts
  • The Gemini app and Gemini in Workspace: access points and use cases
  • Prompts with task, context, format and references to Drive files
  • Web search, Deep Research and checking sources
  • Limitations, hallucinations and critical evaluation of output
Module 2 Gmail, Docs and Slides
  • Summarising email threads and drafting context-aware replies
  • Drafting, rewriting and tailoring documents in Docs
  • Pulling in and combining content from Drive files
  • Creating presentation content and images in Slides
Module 3 Sheets and Meet
  • Structuring tables, generating formulas and cleaning data
  • Analyses, summaries and charts in Sheets
  • Letting Meet take meeting notes automatically
  • Deriving action items and decisions from meeting notes
Module 4 Gems and NotebookLM
  • Creating custom Gems with instructions and knowledge files
  • Testing, improving and sharing Gems with the team
  • NotebookLM as a source-grounded workspace for your own documents
  • Generating summaries, study aids and audio overviews from sources
Module 5 Data protection, administration and rollout
  • Privacy commitments for Workspace customers versus personal accounts
  • Drive sharing settings and how they affect Gemini results
  • Handling personal and confidential data in line with GDPR
  • Usage policies and quality assurance within the team
Module 6 Practice lab: Pharma & Life Sciences
  • Literature review and evidence
  • Medical writing support
  • SOPs and training materials
  • Deviations and CAPA
  • Regulatory intelligence
  • Pharmacovigilance support
Module 7 Data protection, quality & transfer to daily work
  • When AI systems are used in GxP-relevant processes, computerised system validation under EU GMP Annex 11 or 21 CFR Part 11 and data integrity according to ALCOA+ must be ensured. Clinical trial and pharmacovigilance data contain health data under Article 9 GDPR and may only be processed in pseudonymised form in approved environments.
  • Ground rules for safe AI use in your team
  • Checking results: facts, sources, tests and the four-eyes principle
  • Personal action plan for the next 30 days
  • Open questions, outlook and resources for further learning

Practical scenarios from your field

Literature review and evidence

An AI assistant summarises scientific publications, extracts study design, endpoints and results into tables and supports systematic reviews, with experts checking every data point against the original source.

Medical writing support

AI creates outlines and first drafts of study protocols, clinical study reports, investigator brochures or modules of the regulatory dossier based on approved templates and source data.

SOPs and training materials

Process descriptions and regulations are turned into draft SOPs, training slides and comprehension questions, which are then reviewed and approved through the validated document management system.

Deviations and CAPA

AI summarises deviation reports, structures root cause analyses, finds similar past cases and suggests CAPAs, which quality assurance evaluates and decides on.

Regulatory intelligence

Automations collect new guidelines and publications from the EMA, national authorities or the FDA; AI summarises changes and maps them to affected products and processes for an impact assessment.

Pharmacovigilance support

AI helps pre-structure case reports from unstructured text, extract relevant information on product, event and patient and detect duplicates, while medical assessment remains with qualified staff.

Prerequisites

Confident everyday use of Google Workspace (Gmail, Drive, Docs, Sheets); no prior AI knowledge is required. You work in the pharmaceutical, biotech or medtech industry and know the GxP basics of your area.

Technical requirements

A laptop with an up-to-date browser and a Google Workspace account with Gemini features enabled; alternatively a Google account for the Gemini app and NotebookLM.

Data protection & compliance

When AI systems are used in GxP-relevant processes, computerised system validation under EU GMP Annex 11 or 21 CFR Part 11 and data integrity according to ALCOA+ must be ensured. Clinical trial and pharmacovigilance data contain health data under Article 9 GDPR and may only be processed in pseudonymised form in approved environments.

Suggested dates

All dates are suggestions – your preferred date is always possible.

  • Thu, 26 November 2026 Online Request
  • Fri, 4 December 2026 Augsburg Request
  • Fri, 18 December 2026 Online Request
  • Tue, 12 January 2027 Kaiserslautern Request
  • Mon, 18 January 2027 Online Request
  • Fri, 29 January 2027 Bremen Request

Frequently asked questions

What is the difference between the Gemini app and Gemini in Workspace?

The Gemini app is a standalone chat assistant for research, writing and analysis. Gemini in Workspace works directly inside Gmail, Docs, Sheets, Slides and Meet and can draw on your emails and Drive files. In practice they complement each other: the app for open questions, the integrated version for work on specific documents.

Why would I use NotebookLM in addition to Gemini?

NotebookLM restricts its answers to the sources you upload – policies, studies or project files, for example – and points to the relevant passages. This reduces hallucinations and works well for getting up to speed on a topic, building internal knowledge collections or preparing training material. Gemini is broader in scope and can also draw on the web.

Is my Workspace data used to train Gemini?

For Google Workspace customers, Google commits that content from Gmail, Drive and other services is not used to train models outside your domain without permission. Different rules apply to personal accounts. Either way, GDPR and your internal policies remain decisive – in the course we review the relevant settings together.

Do AI tools need to be validated in pharma?

It depends on the use. For activities without GxP relevance, such as internal communication or literature overviews, general IT and data protection rules apply. If results feed into GxP-relevant decisions or records, a risk-based validation approach is required. We show you how to classify use cases.

How do we ensure data integrity?

AI output must be attributable, traceable and verifiable. That means documented inputs and prompts, clearly assigned reviewers, versioning and release through your validated systems. In the course we discuss how ALCOA+ principles apply to AI-supported work steps. Clear retention rules complete the picture.

How much does “Google Gemini for Pharma and Life Sciences” cost?

The course costs a flat €949 plus VAT per participant – whether online or on-site. This includes 1 training day(s) with a live trainer, course materials, a lab environment and a certificate of attendance.

Can I suggest my own date?

Yes. Our suggested dates are just a guide. In the request form you can always describe your preferred date or period in free text – we check availability and confirm within one working day.

Is the course online or on-site?

Both are possible: live online in a virtual classroom or on-site in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart or Kaiserslautern. We can also run the course exclusively for your team.

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