AI in Healthcare and Medical Diagnostics Training Course

Module 1: Foundations of AI in the Healthcare Ecosystem

• Evolution of medical technology: From expert systems to deep learning
• Identifying high-impact use cases in primary care and specialized medicine
• Introduction to the Healthcare AI stack: Python, PyTorch, and Medical APIs
• Understanding the lifecycle of a medical AI project from research to bedside
• Key challenges in MedAI: Data scarcity, label noise, and clinical validation
• Practical session: Mapping a clinical workflow and identifying high-value AI integration points

Module 2: Medical Data Engineering and Privacy Standards

• Handling diverse medical data: DICOM, HL7, FHIR, and unstructured notes
• Data de-identification and anonymization techniques for patient privacy
• Managing imbalanced datasets in rare disease diagnostics
• Feature engineering for clinical data: Lab results, vitals, and demographic trends
• Data quality auditing and handling missing values in longitudinal health records
• Practical session: Cleaning and de-identifying a clinical dataset for model development

Module 3: Machine Learning for Clinical Risk Prediction

• Implementing Logistic Regression and Random Forests for patient readmission risk
• Survival analysis and time-to-event modeling for chronic disease progression
• Evaluating model performance: Why Sensitivity and Specificity matter over Accuracy
• Cost-sensitive learning: Balancing False Positives vs. False Negatives in triage
• Real-time monitoring: Architectures for early warning systems in Intensive Care Units
• Practical session: Building a predictive model to identify patients at high risk of sepsis

Module 4: Computer Vision in Medical Imaging and Radiology

• Introduction to Convolutional Neural Networks (CNNs) for X-ray and CT analysis
• Image segmentation techniques for tumor volume estimation and organ mapping
• Transfer learning for medical imaging: Utilizing pre-trained models on small datasets
• Multi-modal imaging: Combining PET, CT, and MRI data for comprehensive diagnostics
• Real-time detection of anomalies in ultrasound and endoscopic video feeds
• Practical session: Training a CNN to detect pneumonia in a dataset of chest X-rays

Module 5: Natural Language Processing for Electronic Health Records

• Mining clinical notes and discharge summaries using text analytics
• Named Entity Recognition (NER) for extracting medications, dosages, and diagnoses
• Sentiment analysis and behavioral health monitoring from patient communications
• Using Transformers (BioBERT/ClinicalBERT) for medical document classification
• Automated summary generation for complex patient medical histories
• Practical session: Building an NLP pipeline to extract ICD-10 codes from clinical narratives

Module 6: AI in Genomics and Personalized Medicine

• Introduction to bioinformatics: Analyzing DNA and RNA sequencing data with ML
• Identifying genetic markers for hereditary diseases and cancer susceptibility
• Pharmacogenomics: Predicting drug response based on a patient’s genetic profile
• Deep learning for protein folding and molecular structure prediction
• Precision oncology: Selecting the optimal treatment path using multi-omic data
• Practical session: Using a classification model to identify disease-causing genetic variants

Module 7: AI-Driven Drug Discovery and Clinical Trials

• Accelerating the R&D cycle: Virtual screening of molecular compounds
• Predicting drug-target interactions and potential side effects with AI
• Optimizing clinical trial design: Patient recruitment and site selection
• Synthetic control arms: Using historical data to reduce the need for placebos
• Generative models for de novo molecular design
• Practical session: Implementing a graph neural network to predict drug-target binding affinity

Module 8: Robotics and AI in Surgical Assistance

• Fundamentals of robot-assisted surgery: Perception, planning, and control
• Computer vision for real-time surgical tool tracking and navigation
• AI-driven preoperative planning and intraoperative decision support
• Haptic feedback and motion scaling: Enhancing surgeon precision
• Analyzing surgical video data for skill assessment and training
• Practical session: Simulating an AI-based surgical path planning algorithm

Module 9: Explainable AI (XAI) and Clinical Interpretability

• The "Black Box" problem in medicine: Why clinicians need to know "Why"
• Implementing SHAP and LIME to explain diagnostic flags to physicians
• Visualizing neural network attention maps in radiology reports
• Global vs. Local interpretability: Understanding the logic of clinical models
• Documenting AI logic for Model Risk Management and clinical auditing
• Practical session: Generating an interpretability report for a model-assisted cancer diagnosis

Module 10: Regulatory Compliance, Ethics, and Future Trends

• Navigating FDA/EMA regulations for Software as a Medical Device (SaMD)
• Addressing algorithmic bias and ensuring health equity across diverse populations
• Ethical considerations of AI: Patient autonomy, consent, and accountability
• The impact of Generative AI and LLMs on patient-doctor interactions
• Future trends: Edge AI for wearable health tech and digital twin patients
• Practical session: Designing an AI governance framework for a hospital-wide deployment

Requirements:

• Participants should be reasonably proficient in English.
• Applicants must live up to Armstrong Global Institute admission criteria.

Terms and Conditions

1. Discounts: Organizations sponsoring Four Participants will have the 5th attend Free

2. What is catered for by the Course Fees: Fees cater for all requirements for the training – Learning materials, Lunches, Teas, Snacks and Certification. All participants will additionally cater for their travel and accommodation expenses, visa application, insurance, and other personal expenses.

3. Certificate Awarded: Participants are awarded Certificates of Participation at the end of the training.

4. The program content shown here is for guidance purposes only. Our continuous course improvement process may lead to changes in topics and course structure.

5. Approval of Course: Our Programs are NITA Approved. Participating organizations can therefore claim reimbursement on fees paid in accordance with NITA Rules.

Booking for Training

Simply send an email to the Training Officer on training@armstrongglobalinstitute.com and we will send you a registration form. We advise you to book early to avoid missing a seat to this training.

Or call us on +254720272325 / +254725012095 / +254724452588

Payment Options

We provide 3 payment options, choose one for your convenience, and kindly make payments at least 5 days before the Training start date to reserve your seat:

1. Groups of 5 People and Above – Cheque Payments to: Armstrong Global Training & Development Center Limited should be paid in advance, 5 days to the training.

2. Invoice: We can send a bill directly to you or your company.

3. Deposit directly into Bank Account (Account details provided upon request)

Cancellation Policy

1. Payment for all courses includes a registration fee, which is non-refundable, and equals 15% of the total sum of the course fee.

2. Participants may cancel attendance 14 days or more prior to the training commencement date.

3. No refunds will be made 14 days or less before the training commencement date. However, participants who are unable to attend may opt to attend a similar training course at a later date or send a substitute participant provided the participation criteria have been met.

Tailor Made Courses

This training course can also be customized for your institution upon request for a minimum of 5 participants. You can have it conducted at our Training Centre or at a convenient location. For further inquiries, please contact us on Tel: +254720272325 / +254725012095 / +254724452588 or Email training@armstrongglobalinstitute.com

Accommodation and Airport Transfer

Accommodation and Airport Transfer is arranged upon request and at extra cost. For reservations contact the Training Officer on Email: training@armstrongglobalinstitute.com or on Tel: +254720272325 / +254725012095 / +254724452588