AI Innovations in Healthcare: From Detection to Treatment Course in Istanbul

Artificial Intelligence in Healthcare Diagnosis and Treatment

Artificial intelligence is transforming healthcare by improving disease detection, supporting clinical decision-making, enhancing patient monitoring, and streamlining medical operations. As healthcare organisations increasingly adopt AI-powered technologies, professionals need the knowledge and practical skills to evaluate their potential, understand their limitations, and apply them responsibly to improve patient care.

The AI Innovations in Healthcare: From Detection to Treatment course provides participants with a practical understanding of how artificial intelligence, machine learning, and deep learning are applied across healthcare settings. The programme explores AI-assisted diagnostics, predictive analytics, medical imaging, patient management, and healthcare data analysis, with an emphasis on the responsible use of technology in clinical environments.

Through Python-based workshops, practical exercises, and healthcare case studies, participants learn how to analyse medical datasets, develop predictive models, and explore deep learning applications using TensorFlow and Keras. The course also examines patient data privacy, algorithmic bias, clinical validation, regulatory considerations, and AI implementation strategies, enabling participants to assess and develop AI solutions that support safer, more efficient healthcare delivery.

You'll Learn How To

• Understand the role of artificial intelligence in healthcare diagnosis, treatment, and management.
• Identify AI applications in medical imaging, patient monitoring, and clinical decision support.
• Use Python and relevant libraries to analyse healthcare datasets.
• Conduct exploratory data analysis to identify patterns in medical information.
• Prepare healthcare data for machine learning and predictive modelling.
• Develop machine learning models for disease risk prediction and early detection.
• Evaluate model performance using appropriate healthcare metrics.
• Understand deep learning techniques for medical imaging and genetic data analysis.
• Build and train basic Convolutional Neural Network (CNN) models using TensorFlow and Keras.
• Assess emerging AI applications in robotic surgery and patient management.
• Address patient privacy, algorithmic bias, ethical concerns, and regulatory requirements.
• Plan the implementation, validation, and monitoring of AI solutions in healthcare settings.