Durch KI Einsatz in der Wirkstoffforschung und klinischen Studien,
Produktivität steigern, 'time to market' beschleunigen und Kosten senken.
September 01 - 03, 2025 | Palace Hotel Berlin, Germany
2. Konferenztag, 03. September 2025
9:10 am - 9:20 am
Eröffnung durch die Vorsitzende Jessica Cordes, Interim Head Of Clinical Operations, Clinical Excellence
9:20 am - 9:50 am
GenAI Meets RWD: Building a Multi-Agent GenAI Platform for RWD/RWE & Insight
Abhishek Choudhary -
Principal Data Engineer,
Bayer
-Bayer’s modular, multi-agent GenAI platform for extracting insights from Real-World Data (RWD) and Real-World Evidence (RWE).
-How specialized agents handle data curation, harmonization, and evidence generation across complex healthcare datasets.
-Governance, scalability, and compliance considerations for deploying GenAI in regulated pharma environments.
Abhishek Choudhary
Principal Data EngineerBayer
9:50 am - 10:20 am
Die Rolle von KI bei der Generierung von Evidence
Sebastian Kloss -
Global Head RWE,
Menarini Group
-Wie KI die Evidenzgenerierung transformiert und die Geschwindigkeit und Genauigkeit der Datenanalyse für klinische und reale Umgebungen verbessert.
-Spezifische KI-Tools und -Methoden, die verwendet werden, um komplexe Datensätze zu synthetisieren und in umsetzbare Erkenntnisse zu extrapolieren.
-Erkunden Sie die zukünftige Landschaft der KI in regulatorischen und Compliance-Rahmenbedingungen und konzentrieren Sie sich dabei auf ihr Potenzial zur
Rationalisierung von Einreichungen und zur Verbesserung von Entscheidungsprozessen.
Sebastian Kloss
Global Head RWEMenarini Group
10:20 am - 10:50 am
Kaffeepause mit Networking Möglichkeit
10:50 am - 11:20 am
Panel: Data foundations for the continuous development and application of AI
Abhishek Choudhary -
Principal Data Engineer,
Bayer
Dimitri Metzger - Digital, Data & IT Business Partner for Generative AI, Medical & Development Uni, Merck
-Data quality and integrity: Establish rigorous processes for data collection, review, and cleansing. High-quality data is essential for training reliable AI models.
-Data integration and management: Working with various data types, including chemical data, biological data, clinical outcomes, patient records, and realworld evidence. Integrate these data sources and establish effective data management systems and practices.
-Human-machine interaction: Preparing your organization for the integrated use of AI through data and AI competency programs, continuing education, and change management.
-Legal and ethical considerations: Understanding AI law. Ethical implications of AI, including bias in AI models and the impact of AI decisions in clinical settings.
Abhishek Choudhary
Principal Data EngineerBayer
Dimitri Metzger
Digital, Data & IT Business Partner for Generative AI, Medical & Development UniMerck
11:20 am - 11:50 am
Integration von KI in Organisationen der Pharmaindustrie
Joachim Hagel -
Medical AI Engineer,
InfectoPharm Arzneimittel Und Consilium
-Erfolgsfaktoren und Hindernisse bei der Einführung von KI in pharmazeutischen Organisationen
-Rollenverteilung zwischen Fachexperten, Data Scientists und IT – wie Zusammenarbeit gelingt
-Strategien zur Skalierung von KI-Anwendungen unter Berücksichtigung regulatorischer und organisatorischer Rahmenbedingungen
Joachim Hagel
Medical AI EngineerInfectoPharm Arzneimittel Und Consilium
11:50 am - 12:20 pm
KI im Dienste der Qualität
-Warum KI für das Qualitätsmanagement einsetzen?
-Regulatorische Grenzwerte
-Überbrückung der Kluft zwischen Technologie- und Fachexperten
-Qualität der Zukunft
12:20 pm - 1:50 pm
Mittagspause mit Networking-Möglichkeiten
1:50 pm - 2:20 pm
Target discovery using GenAI, knowledge graphs and literature
Nikola Milosevic -
Science Fellow,
Bayer AG
-We have developed systems based on GenAI to rank and explain targets
-The system is based on literature, clinical trial documents, and knowledge graph
-Current state-of-the-art LLMs are able to analyze multi-modal data and provide comprehensive evaluation of the targets
-Pipeline can produce in few hours comprehensive summary of possible targets for a disease
Nikola Milosevic
Science FellowBayer AG
2:20 pm - 2:50 pm
Before GenAI, Get Your Data Right: Why FAIR Data Matters
-The GenAI Promise: How AI is expected to transform clinical trials—faster decision-making, better predictions, and improved efficiency.
-The Risk of Bad Data: Without structured, high-quality, and governed data, Gen AI models risk hallucinations, bias, and unreliable outputs.
-FAIR Data as a Prerequisite: Findable, Accessible, Interoperable, and Reusable (FAIR) data principles lay the foundation for effective and trustworthy Gen AI. FAIR
and Gen AI are not competing forces; they enhance each other. FAIR data enables AI to generate meaningful, actionable insights.
-The Roadmap Forward: Steps pharma organizations can take to ensure data governance and AI readiness for clinical trials.
2:50 pm - 3:20 pm
AI in Proteomics: Transforming Clinical Research through Multi-Omics Data Integration• Recent advanced in biomarker discovery using multi-omics data and AI
Ornella Cominetti -
Senior Omics Data Specialist,
Nestlé
-Important role of biobanks and recent large proteomic studies/cohorts
-AI versus standard statistical models in the context of omics data analysis
-Examples in metabolic health, cancer, brain health, and infectious diseases
Ornella Cominetti
Senior Omics Data SpecialistNestlé
3:20 pm - 3:50 pm
Maschinelles Lernen in der frühen Arzneimittelforschung – Wie Bayer Deep Learning nutzt, um Arzneimittelkandidaten gleichzeitig im phänotypischen, chemischen und transzipomischen Raum zu erforschen
-Wie Bayer Cell-Painting-Assays im großen Maßstab erstellt
-Wie man maschinelles Lernen nutzt, um Mikroskopiebilder für phänotypische Profile einzubetten
-ML-Modelle zur Einbettung chemischer Strukturen
-Analyse multimodaler Daten
