AI is transforming the practice of medicine. It’s helping doctors diagnose patients more accurately, make predictions about patients’ future health, and recommend better treatments.


Medicine is one of the fastest-growing and important application areas, with unique challenges like handling missing data.

The future of AI in healthcare

We believe that AI has an important role to play in the healthcare offerings of the future. In the form of machine learning, it is the primary capability behind the development of precision medicine, widely agreed to be a sorely needed advance in care.

The greatest challenge to AI in healthcare domains is not whether the technologies will be capable enough to be useful, but rather ensuring their adoption in daily clinical practice. It also seems increasingly clear that AI systems will not replace human clinicians on a large scale, but rather will augment their efforts to care for patients.

Algorithm development

  • Artificial neural networks (ANN)
  • Convolutional neural networks (CNN)
  • Recurrent neural networks (RNN)
  • Reinforcement learning (RL)
  • Support Vector Machines (SVM)
  • Principle component analysis (PCA)
  • Random decision tree forest
  • Kalman filtering

Data gathering

  • Assessing data quality (multidisciplinary)
  • Designing data gathering process
  • Pre-processing & cleaning data
  • Data augmentation
  • Feature engineering (multidisciplinary)

Design strategies

  • Design for improved user experience
  • Learn cheap fail fast by building rapid prototypes and demonstrators
  • 360° project view: inverting the learning curve

Algorithm categories

  • Regressions, classifications, predictions
  • Image recognition
  • Predictive maintenance
  • Anomaly detection
  • Natural language processing or understanding (NLP/NLU)

Software skills

  • Tensorflow, Keras, Scikit-learn …
  • Model serving (google cloud, amazon web services, local serving)
  • python, matlab, java, C++ …

AI vision creation services

  • AI readiness assessment
  • Road map development
  • Structured idea generation & selection
  • Use case & interaction analysis

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