Drug safety
Adverse events in patient texts
Online posts, patient feedback and notes from healthcare professionals are scanned for suspected adverse events, with drug names and active substances recognised in several languages.
Industries · Pharma
AI for pharmaceutical companies in drug safety, medical affairs and operations
theBlue.ai builds custom AI systems that read medical texts, analyse images and sensor data and answer questions from your own documents. They flag suspected adverse events, draft answers to medical information requests, detect anomalies in production equipment and pull orders out of emails. Each system runs inside your existing infrastructure, on-premise, in the cloud or hybrid.
AI in pharma: drug safety and medical information requests
AI in pharma searches large volumes of text: patient and healthcare professional reports, scientific literature and incoming enquiries. It spots signals of adverse events, classifies requests and summarises the relevant passages.
In pharmacovigilance and medical affairs this supports the triage of incoming reports, the preparation of answers and literature screening. Results go to the safety database, the CRM or the internal knowledge system.
The gain: nothing sits unread in the inbox, reports are pre-sorted sooner and answers are drafted faster. Assessment, reporting and sign-off stay with the responsible experts.
Project example · Global pharmaceutical company
Drug safety signals were buried in millions of pieces of text
Patient feedback, reports from healthcare professionals, social media posts and regulatory documents came in across multiple languages. The client’s teams had to review and classify this information manually, a process that could no longer keep pace with the growing volume of data. An NLP pipeline now does this reading and classifying, and the teams work with its results.
Millions
Of medical texts read for drug safety signals
Automated
Adverse drug events found in unstructured online sources
Real time
Sentiment around a drug before and during its market launch
Some of the organisations we have built for
What AI takes over
Nine processes AI takes over in pharma
Pharmaceutical companies review texts, images and data at a volume no team covers in full. AI takes over in drug safety, medical affairs, research, manufacturing, the supply chain and around a launch.
Medical affairs
Medical information requests
Questions from healthcare professionals and patients get a draft answer from your approved product documentation, with the source, for your medical information team to check and send.
Medical affairs
Scientific literature screening
New publications are screened for mentions of your products and possible safety signals, and the relevant articles are summarised for review.
Clinical research
Image analysis in MRI and CT
Anatomical structures are outlined automatically and findings that are hard to see are highlighted for the specialists to assess.
Manufacturing
Anomalies in production and cold storage
Sensor data from equipment, clean rooms and cold storage is monitored for anomalies and failing sensors, and maintenance follows the actual condition of each machine.
Supply chain
Orders and planning figures
Orders from pharmacies and wholesalers are pulled out of emails into your ERP, and planners ask for stock and planning figures in plain language, by text or voice.
Congresses
Assistants at medical congresses
An AI avatar or voice assistant answers visitor questions about sessions, schedule changes and the exhibition floor in real time, in several languages.
Launch
Sentiment around a drug launch
Launch teams see how patients, caregivers and healthcare professionals talk about a drug on social media, before and during its market launch.
Knowledge
Answers from SOPs and internal documents
Staff ask in plain language and get an answer from standard operating procedures, work instructions and internal guidelines, with a link to the passage it came from.
How it works
From a forum post to a structured safety signal
Source: patient forum, public post
Language: English Drug mention
Language: English Drug mention
Started Product A 20 mg last week, prescribed by Dr. Keller in Zurich.
Since Tuesday I get a bad headache every afternoon and feel really dizzy. Anyone else?
Safety signal · draft
Product
Product A, 20 mg✓
Personal data
Physician and place removed✓
Suspected events
Headache, dizziness✓
Onset
About one week after start, check
Sent to the safety team · 1 field to confirm
Illustrative example with invented data, based on the pharmacovigilance case study.
Built for pharma
What AI in a pharmaceutical company has to handle
Sensitive health data, many different data sources and decisions that stay with qualified people.
Personal data out first
Where patient or physician data is involved, de-identification is the first stage of the pipeline, before any analysis starts.
Runs where your data is
On-premise, in the cloud or hybrid, connected to the systems you already use, such as SAP or your own applications.
Real data, as it arrives
Colloquial posts, pharmaceutical nomenclature in several languages, inconsistent document formats and noisy sensor streams.
Your team decides
The system surfaces and prepares. Assessment and every decision stay with your safety, medical, quality and operations teams.
Case studies
Pharmacovigilance, medical imaging and sensor data
Our pharmaceutical project, and two projects from related fields that show our work with medical images and sensor data.
5 languages
DE, EN, FR, IT, ES
GDPR
Full de-identification
Pharmaceutical · under NDA
Millions of medical texts read for drug safety signals
Teams read patient feedback, medical notes and social media in five languages looking for drug safety signals. Language models now detect medical entities, de-identify the text and flag adverse events.
Read the case study
90%
Sensitivity
70%
Specificity
Healthcare · Ev. Krankenhaus Alsterdorf
Finding epilepsy lesions that manual MRI reading misses
Neurologists scanned brain MRIs visually for epilepsy-causing lesions, which takes expert knowledge and can miss subtle cases. Our models detect them with higher sensitivity than the eye, and the results are published in the peer-reviewed journal Epilepsy Research.
Read the case study
RAG
Over their own files
Seconds
With source references
MedTech · apoQlar
A retrieval assistant that answers from hundreds of product documents
Support staff searched through hundreds of product documents to answer customer and compliance questions. They now ask in plain language and get an answer with the source it came from.
Read the case studyHow to start
Start with one process and the data behind it
A safety inbox, a literature feed or a production line: the work your team does by hand today, and the data it runs on. We check early whether the data carries the use case and where it may be processed.
Describe your process
Tell us where the manual work sits and which data it involves. We come back within one business day with an initial assessment and a proposal for a 30-minute scoping call.
Describe your processStart with the process analysis
Workflows mapped, your data sources and requirements checked, and an architecture proposed with scope, timeline and cost. A standalone engagement with no commitment to proceed.
See the AI Discovery WorkshopFAQ
Questions about AI in pharma
In pharma, AI takes over work that safety, medical and operations teams otherwise do by hand: patient texts and online posts in drug safety, medical information requests and scientific literature in medical affairs, MRI and CT images in research, sensor data in manufacturing and cold storage, orders and planning figures in the supply chain, and SOPs and internal documents across the company. The AI system prepares the result, and qualified people decide.
Yes. For a global Swiss pharmaceutical company theBlue.ai built a pipeline that analyses opinions and posts published online and identifies adverse events linked to specific drugs, in German, English, French, Italian and Spanish. Preprocessing handles the way people actually write online: abbreviations, colloquial language, medical slang and inconsistent formatting.
Yes. theBlue.ai built models that outline anatomical structures in MRI and CT scans in seconds, a 3D neural network that finds epilepsy lesions on MRI, and machine learning pipelines that detect anomalies and failing sensors in the operational data of more than 100 devices with 50 or more sensors each.
De-identification is the first stage of every pipeline that handles patient or physician data. Automated anonymisation strips personal data from medical notes and records before any analysis starts, and the system can run on-premise, in the cloud or hybrid.
Yes. theBlue.ai builds systems into the infrastructure you already run, such as SAP, data warehouses, document management or your own applications. A planning assistant built for a car manufacturer, for example, retrieves figures from on-premise SAP systems by text or voice.
With one process, such as screening one source of patient texts for adverse events, and the data behind it. The process analysis has a fixed price from €3k and ends with an architecture proposal that states scope, timeline and cost, including where the data may be processed. The build is priced in milestones, and first working components typically arrive six to eight weeks in.
Tell us which process is costing you the most
Describe the process and we’ll come back within one business day with an initial assessment and a proposal for a 30-minute scoping call.
- Phase 1 is a standalone engagement, with no commitment to proceed further.
- Senior engineers from day one. No account managers, no handoffs.
- We integrate, never replace. Your systems stay exactly as they are.