24th January 2026
Kick off and ideation sprint
The MBZUAI x Johnson&Johnson Hackathon 2026 brings together innovators, researchers, and builders to tackle real-world healthcare challenges using cutting-edge AI technologies. Participants will work on high impact problem statements inspired by pharmaceutical research, laboratory automation, and personalized medicine.
Internship opportunities at Johnson&Johnson for winning team members based in the UAECo-development and venture support provided by 10FT Ventures StudioGlobal visibility at Abu Dhabi Global HealthCare WeekDirect exposure to industry experts and healthcare leadersCoaching by global and regional entrepreneurship
NXT health challenge 1
Scientific graphs and charts, such as survival curves and radar plots, are pervasive in medical and healthcare literature. They are great for showing complex health information.
Your challenge: to build an AI tool that can “read”and interpret different types of graphs and then transform the data into other formats – for example, turning a chart into a table, a new type of graph, or a short data summary. Imagine a tool that helps scientists, businesses, or doctors easily switch between visuals or instantly understand a chart from a presentation.
Goal: Use generative AI to make graphs and data more understandable, flexible, and useful across different contexts.
Participate
NXT health challenge 2
Ambient laboratory vision with multimodal AI: Modern pharmaceutical labs rely on mobile robots to transport samples between workstations.
Your challenge: explore whether multimodal LLMs can interpret lab images to identify issues (e.g., misplaced samples, process errors) and adapt to new use cases without retraining.
Goal: Evaluate the feasibility, accuracy, and adaptability of using generative or multimodal AI for general-purpose visual monitoring in automated labs.
Participate
NXT health challenge 3
Pharma R&D generates massive, siloed datasets – genomic, proteomic, clinical, and real-world health data – yet integrating these to design optimal treatment plans remains complex.
Your challenge: to develop a multimodal or generative AI solution that can: Ingest multi-omic and clinical data (e.g., genomic variants, lab values, lifestyle factors) Predict optimal treatment options (e.g., drug type, dosage, or companion diagnostic)
Goal: Demonstrate how AI can accelerate personalized medicine, helping stakeholders make data-driven, transparent therapy decisions that improve outcomes and safety.
Participate