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Artificial Intelligence in - Healthcare
(Diagnosis & Predictive Analytics)

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Objectives

This course focuses on leveraging AI to improve diagnostic accuracy, automate radiology workflows, and enable predictive patient care using historical and real-time data. It highlights practical integration of machine learning, NLP, and predictive modeling into clinical settings, emphasizing explainability, regulatory compliance, and ethical use. By combining advanced analytics with clinical decision support systems,



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Course Outcome

Apply AI techniques to enhance diagnostic accuracy and automate analysis of medical images.
Develop predictive models that assess patient risks and forecast disease progression using health data.
Automate regulatory compliance processes with natural language processing applications.
Utilize advanced medical imaging tools and frameworks to build and validate diagnostic AI models.
Ensure AI healthcare solutions comply with regulatory standards and ethical requirements.
Collaborate effectively with clinical and technical teams to design AI healthcare products.
Leverage AI to improve early detection of diseases, reduce diagnostic errors, and support personalized patient care.
Monitor and interpret AI model outputs to maintain clinical trust and facilitate adoption in healthcare settings.

Why Mazenet?


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    Expert Faculty

    Our Faculty comprises of 300+ SMEs with many years of experience. All our trainers possess a minimum of 8+ years of experience.

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    Proven Track Record

    We have served over 200+ global corporate clients, consistently maintaining a 99% success rate in meeting training objectives for 300+ technologies with quick turnaround time.

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    Blended Learning

    We provide course content over any platform that our clients prefer. You can choose an exclusive platform or a combination of ILT, VILT, and DLP.

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    Learning Paths

    The learning paths are very defined with clear benchmarks. Quantitative assessments at regular intervals measure the success of the learning program.

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    Case Study

    We have amassed over 10,000 case studies to support training delivery. Candidates will be trained to work on any real-time business vertical immediately after the training.

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    24*7 Global Availability

    We are equipped to conduct training on any day, date or time. We have delivered training pan India, Singapore, North America, Hong Kong, Egypt and Australia.

Delivery Highlights

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    Customized Training Modules

    Training programs are highly flexible with module customizations to suit the requirements of the business units.

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    Certification

    The training can be supplemented with appropriate certifications that are recognized across the industry.

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    Multi-language Support

    Course content can be delivered in English, Spanish, Japanese, Korean or any other language upon request.

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    Personalized Training Reports

    Candidates are assessed individually at regular intervals and are provided unique learning suggestions to suit their learning calibre.

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    Industry-Oriented Training

    Industry-oriented training, completing which, candidates can be immediately deployed for billable projects.

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    Diverse Training Platforms

    Choose from Instructor-Led Training, Virtual Instructor-Led Training, Digital Learning Platform and Blended Training platforms

Contact Us
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info@mazenetsolution.com +91 9500929297
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Course Preview

  • Review AI’s impact on clinical and diagnostic medicine.
  • Discuss challenges and ethical concerns unique to healthcare.
  • Overview AI algorithms for medical imaging and diagnostics.
  • Understand regulatory compliance like FDA, HIPAA, MDR.

  • Apply pattern recognition on medical datasets.
  • Use AI for cancer detection, diabetic retinopathy, and skin lesion classification.
  • Explore clinical decision support systems (CDSS).
  • Leverage NLP for patient notes and EMRs.

  • Use deep learning techniques on X-rays, MRI, CT, and PET scans.
  • Understand CNN applications in medical imaging.
  • Tools: NVIDIA Clara, MONAI, Aidoc, Azure Medical Imaging.
  • Hands-on: train a CNN for chest X-ray classification.

  • Stratify patient risk based on historical data.
  • Forecast readmission and disease onset using time-series models.
  • Build early warning systems using vitals and wearable data.
  • Tools: scikit-learn, XGBoost, TensorFlow Health.
  • Hands-on: build predictive models for patient outcomes like sepsis risk.

  • Emphasize explainability’s role in medical AI.
  • Use LIME and SHAP for healthcare applications.
  • Develop trust with clinicians through validation.
  • Incorporate human-in-the-loop approaches in decision support.

  • Integrate AI into hospital systems (HIS, PACS, EHR).
  • Monitor models in real-time.
  • Address data bias, equity, privacy, and informed consent.