As Lead AI Engineer at Dermascan, I design and develop AI algorithms for dermatological diagnostic assistance and medical workflow optimization in skin cancer screening centers.

• Main Objective: Improve patient triage quality and orientation in physical centers through AI-powered diagnostic assistance algorithms.
• Key Contributions:
  • Developed diagnostic assistance algorithms from dermoscopic images to improve patient triage and orientation.
  • Built automated medical data extraction pipelines from tele-expertise reports (PDF → database).
  • Designed AI tools to reduce manual tasks and accelerate medical record processing.
  • Structured and enriched medical data to improve internal datasets quality for AI models and clinical analyses.
  • Led technology watch in AI & healthcare to identify and integrate innovative approaches in medical vision and LLMs applied to healthcare.
  • Served as scientific interface between technical teams, partner dermatologists and LIS laboratory (Aix-Marseille University) to ensure clinical and scientific validity of developed solutions.
  • Contributed to regulatory compliance of AI solutions (GDPR, AI Act, medical device software certification requirements).
  • Internal scientific outreach to acculturate non-technical teams to AI, medical data and regulatory issues.
• Technologies: Python, FastAPI, Computer Vision, LLMs, Hugging Face, AWS, Vercel, Medical Data Engineering, GDPR, AI Act

As a Senior Data Scientist at DMS Logistics, I lead the design and deployment of AI-driven solutions to optimize container flow management for key logistics players — including shipowners, terminals, and freight forwarders.

• Main Objective: Enhance operational efficiency, automate logistics processes, and anticipate container flow through intelligent forecasting and optimization models.
• Key Contributions:
  • Developed a GenAI-powered insight engine using Retrieval-Augmented Generation (RAG) to enable clients to query and analyze CO₂ KPIs via natural language.
  • Built a document intelligence pipeline combining OCR and LLMs to automate data extraction from scanned documents.
  • Designed and deployed time series forecasting modules to predict traffic, capacity needs, and terminal appointments.
  • Engineered scalable APIs and microservices using FastAPI, Docker, Kubernetes, and Airflow.
• Methods & Technologies:
  • Exploratory data analysis on large, heterogeneous logistics datasets.
  • Development of machine learning models for prediction, classification, and optimization.
  • Data pipeline design and integration across multiple internal and external sources.
  • Cloud deployment and MLOps using Scaleway Cloud, Prometheus, and CI/CD workflows.

Diamelex is a research project funded by the French National Research Agency (ANR) for an amount of 513,950 euros in collaboration with the start-up Anapix medical, the Marseille Cancer Research Center (CRCM) and the Computer Science and Systems Laboratory (LIS). The project concerns the development of a computer aided diagnosis system for melanoma based on deep learning techniques. Arthur Cartel Foahom Gouabou actively participated in this project as a researcher and its research activities in this project led to the publication of five scientific papers in international and national journals and conferences.

This project concerns the design of a driving assistance system for a passenger vehicle. It is the end-of-study project carried out by Arthur Cartel Foahom Gouabou to obtain the diploma of engineer in mechatronics of the National Advanced School of Engineering of Douala.