AI in Health Care Industry
- 160 Hours of training
- 450+ Hiring Partners
- Classroom & Live Online Training Available
- Hands-on training with assignments and capstone projects
- Next batch starts 10 Nov 2026 · Cohort Annapurna
DedicatedPlacement Assistance
About the AI in Health Care Industry course
Explores how AI is applied across healthcare — diagnostics support, operational efficiency, and patient data analysis — with close attention to the regulatory and ethical considerations unique to the industry. You'll examine real use cases spanning clinical decision support, hospital operations, and administrative automation, while learning how concepts like data privacy, bias, and explainability apply specifically to healthcare settings. The program is built for professionals who want to understand where AI genuinely helps in clinical and operational workflows, and where caution and human oversight remain essential.
Learn From The Real Forward Deployed Engineers And AI Experts
Guest lectures and mentorship from Forward Deployed Engineers working at leading companies.
Companies Hiring In This Field
Companies that recruit for roles in this field.
Who Should Do The AI in Health Care Industry?
Who this program is built for.
- Bachelor's or Master's in Life Sciences, Pharmacy, Medicine, or Nursing01
- Biotechnology, Microbiology, Biochemistry, or Chemistry graduates02
- B.Pharm, M.Pharm, Pharm.D, MBBS, BDS, or MDS graduates03
- 04B.Sc Nursing, or any other recognized degree in life science-related field
- 05B.Tech/B.E (CSE or related), MCA, B.Sc, or BCA graduates
AI in Health Care Industry Learning Path
Learn → Build → Deploy → Solve Real Problems → Get Placed
Tech Stack
Technologies & Tools
Placement Path
From your first class to your first offer letter, here is how we take you there.
Career Opportunities with AI in Health Care Industry
Tap or hover a card to see what the role looks like day to day. Pay ranges are indicative market figures, not a guarantee of salary or placement.
Health Informatics SpecialistIndicative market pay: ₹5 – 8 LPA
Health Informatics Specialist
A role you can aim for after AI in Health Care Industry. We help you prepare with projects, mock interviews and resume reviews.
Clinical Data AnalystIndicative market pay: ₹5 – 8 LPA
Clinical Data Analyst
A role you can aim for after AI in Health Care Industry. We help you prepare with projects, mock interviews and resume reviews.
Healthcare AI AnalystIndicative market pay: ₹6 – 9 LPA
Healthcare AI Analyst
A role you can aim for after AI in Health Care Industry. We help you prepare with projects, mock interviews and resume reviews.
AI Solutions AssociateIndicative market pay: ₹6 – 10 LPA
AI Solutions Associate
A role you can aim for after AI in Health Care Industry. We help you prepare with projects, mock interviews and resume reviews.
Healthcare Data ScientistIndicative market pay: ₹9 – 14 LPA
Healthcare Data Scientist
A role you can aim for after AI in Health Care Industry. We help you prepare with projects, mock interviews and resume reviews.
Clinical AI EngineerIndicative market pay: ₹10 – 16 LPA
Clinical AI Engineer
A role you can aim for after AI in Health Care Industry. We help you prepare with projects, mock interviews and resume reviews.
Everything That You Get by Enrolling in AI in Health Care Industry
We've built this program with one goal — to guide, support, and uplift you at every stage of your career journey. From the first class to your first placement and beyond, every component has been created with your growth, confidence, and success in mind.
EMI Options Available
Placement Assistance
Collaborative Learning through Teamwork
Course Completion Certificate
Expert Career Guidance
Flexible Learning
ISO 9001:2015 Certified Institute
Hands on Project
Corporate-Ready Curriculum
Industry Expert Sessions
Internship programs
Mock Interview
Placement Mentorship
Resume Preparation Guidance
Recognised worldwide
ISO
IAF
MSME
DPIIT
Accreditation and Recognition from Top Industry Bodies
Our programs and certifications are affiliated with, and recognised by, globally respected standards and skill-development bodies.
AI in Health Care Industry Curriculum
Comprehensive modules designed to build AI in Health Care Industry skills step by step.
Understand how AI is transforming healthcare and the rules that govern it.
Key Topics
- Overview: evolution, applications, market landscape
- Key technologies: ML, NLP, computer vision, robotics, generative AI
- Smart hospitals & digital health records
- Ethical & legal aspects: India DPDP Act 2023, ABDM, CDSCO guidance for medical AI, HIPAA & GDPR overview, patient consent, algorithmic bias
- Use cases: radiology automation, virtual nursing assistants, AI triage
- Domain focus: EHR/EMR, HL7 & FHIR, ICD-10, roles of clinicians, informaticists and data teams
Industry Relevance
Speak the language of cliniciansSpot compliant AI use casesUnderstand EHR data flowsAssess algorithmic biasLearn the Python and tooling used for healthcare AI.
Key Topics
- Dev setup: Jupyter, VS Code, PyCharm, Anaconda, Colab
- Python basics, data structures for medical data, OOP for reusable health-analysis modules
- NumPy for imaging arrays & sensor data; Pandas for hospital, pharmacy & EHR data
- Matplotlib/Seaborn: lab values, vital trends, seasonal illness patterns
- FastAPI / Flask / Streamlit for rapid healthcare AI demos
- Git & version control; AI-assisted coding (ChatGPT, Claude, Copilot)
- Datasets: synthetic hospital admissions CSV, open COVID-19 case time-series
Industry Relevance
Analyze hospital data in PythonPrototype clinical AI demosVersion-control health AI projectsPlot vitals and lab trendsPrepare, explore and visualize clinical data.
Key Topics
- Data sources: EHRs, insurance records, public datasets, health portals
- Cleaning: nulls, outliers in vitals/dosage, duplicates
- Feature engineering: symptoms, medication durations, lab ranges, dummy variables
- Visualization: dashboards for disease trends & mortality rates; EDA by age, condition & gender
- Maths & statistics: probability, CLT, standard deviation, normal distribution, linear algebra & calculus for ML
- Domain focus: oncology & endocrinology basics (tumor staging, HbA1c, CBC/BMP panels)
- Datasets: MIMIC-III/IV, WHO GHO, Pima Indians Diabetes, Diabetes 130-US Hospitals, Breast Cancer Wisconsin
Industry Relevance
Clean messy clinical recordsBuild disease-trend dashboardsEngineer lab-based featuresRead common clinical panelsPredict diagnoses, risk and outcomes from clinical data.
Key Topics
- Regression vs classification in diagnostics; linear regression for length-of-stay
- Logistic regression, Naive Bayes, decision trees, random forests, SVM, KNN
- Metrics in clinical context: accuracy, precision, recall, F1, AUC-ROC
- Cross-validation & tuning: GridSearchCV, RandomizedSearchCV
- XGBoost & gradient boosting for readmission prediction and risk scoring
- Recommendation systems: treatment suggestion, drug-interaction alerts, personalized care plans
- Datasets: UCI Heart Disease, Pima Diabetes, Stroke Prediction, MIMIC-III length-of-stay, Kaggle readmissions
Industry Relevance
Predict 30-day readmissionScore patient riskPick metrics that fit clinical stakesSupport clinical decisionsFind structure in patient, imaging and claims data.
Key Topics
- K-Means: patient segmentation by severity & demographics; hierarchical clustering
- DBSCAN: anomaly detection in vitals and ICU data
- PCA & t-SNE for genomic, clinical and wearable-sensor data
- Autoencoders: anomaly detection in ECG, EHR and labs
- Fraud & outlier detection in billing and insurance claims
- Association rule mining: co-morbidity and co-prescribing patterns
- Datasets: MIMIC, eICU, TCGA, PhysioNet, Kaggle insurance claims, UCI HAR
Industry Relevance
Group patients by profileDetect claim fraudFlag abnormal vitalsVisualize high-dimensional cohortsApply deep learning to medical images and signals.
Key Topics
- Neural networks: perceptrons, activation functions, backpropagation; ANN vs CNN vs RNN
- CNNs for chest X-ray, CT and retinal image classification; LeNet, AlexNet, VGG16, GoogLeNet, ResNet
- Transfer learning with ImageNet models (CheXpert, NIH ChestX-ray14)
- Object detection: YOLO v5/v8, SSD, Detectron2; segmentation with U-Net (BraTS)
- RNN/LSTM/GRU for vitals, glucose & ICU streams; ECG arrhythmia with CNN/LSTM
- Transformer basics; TensorFlow & PyTorch pipelines
- Hands-on: ANN for stroke prediction; CNN for diabetic retinopathy
- Domain focus: radiology (DICOM), ophthalmology, dermatology, cardiology signals
Industry Relevance
Classify medical imagesDetect lesions and tumorsPredict from ICU time-seriesUnderstand imaging standardsExtract insight from clinical text.
Key Topics
- Text preprocessing of clinical notes & patient histories
- Named Entity Recognition: conditions, medications, procedures
- Sentiment analysis of patient feedback and chatbot dialogues
- Medical text classification for reports & insurance claims
- Word embeddings: Word2Vec, GloVe, FastText; RNN/LSTM/GRU for clinical text
- BioBERT, ClinicalBERT; transformers & LLM basics
- Datasets: MIMIC notes, i2b2/n2c2, MedNLI, PubMed, Drug Review dataset
Industry Relevance
Extract entities from clinical notesSummarize research literatureAnalyze patient feedbackClassify reports and claimsDeploy and monitor healthcare models securely in the cloud.
Key Topics
- Deploy models as REST APIs with FastAPI/Flask; Docker & Kubernetes basics
- AWS: EC2, S3, IAM, SageMaker pipelines & model registry
- MLOps: pipelines, versioning, MLflow experiment tracking, CI/CD with GitHub Actions
- Auto-scaling, cost optimization, CloudWatch monitoring; Streamlit dashboards for clinics
- Security: encrypting medical data, API access, audit logging
- Compliance: DPDP Act 2023, ABDM health-data standards, HIPAA Security Rule, RBAC in clinical systems
- Datasets: models from Modules 4-7, Synthea synthetic patient records
Industry Relevance
Deploy models to hospital workflowsMonitor clinical model driftEncrypt and audit patient dataMeet HIPAA-style requirementsBuild safe LLM and agent solutions for clinical workflows.
Key Topics
- LLMs, transformers, prompt engineering; Med-Gemini, current GPT/Claude/Gemini models, open medical LLMs
- RAG: grounding clinical Q&A in verified literature and EHR data
- Fine-tuning vs prompting for clinical documentation, discharge summaries, patient education
- Agentic AI: autonomous agents, tool use, orchestration; triage, scheduling and lab-result agents
- Multi-agent systems: diagnostic-assist + scheduling + billing coordination
- Responsible GenAI: hallucination risk, human-in-the-loop, clinical safety guardrails, DPDP/HIPAA compliance
- Hands-on: RAG clinical Q&A assistant & agentic patient-intake workflow
- Datasets: MedQA, PubMedQA, MedMCQA, MIMIC notes, PMC Open Access, Synthea
Industry Relevance
Draft clinical documents with AIBuild grounded medical Q&AAutomate patient intakeKeep a human in the loopDesign, build and present an end-to-end healthcare AI solution.
Key Topics
- Phase 1: problem definition & data; choose a real-world use case (readmission, remote monitoring, diagnostic triage)
- Phase 2: full pipeline build & training, UI, ethics & DPDP/ABDM compliance documentation
- Phase 3: AWS deployment, CloudWatch monitoring, security audit
- Phase 4: portfolio curation & demo day with a clinical-impact narrative
- Deliverables: working solution, deployed API/Streamlit dashboard, documented GitHub repo, compliance docs, demo presentation
Industry Relevance
Deliver a deployable clinical AI toolDocument compliancePresent clinical impactBuild a healthcare AI portfolio
The L&L AI Solutions Placement Mentorship Program
At L&L AI Solutions, we treat every learner as a professional in progress. Our Placement Mentorship Program pairs you with dedicated mentors who guide, support, and push you through every stage of your journey — from your first module to your final interview — so you leave not just trained, but genuinely ready to build a career in AI in Health Care Industry.
Stage 01
Sharpen Your Fundamentals & Interview Confidence
You'll work closely with mentors who pressure-test what you know and flag exactly where to focus next. Through regular check-ins, simulated interviews, and guided practice, you'll build the technical clarity and communication confidence to hold your own in any interview — technical or HR.
Skill Gap Assessments
Confidence Coaching
Technical Interview Simulations
Weekly Progress Scorecards
HR Round Simulations
Concept Refresher Sessions
Communication Skill Coaching
Key Support
Stage 02
Map Your Career Path & Build Your Professional Profile
Get complete clarity on how placements actually work — the roles open to you, what employers expect, and where you're the strongest fit. With a mentor by your side, you'll rebuild your resume and LinkedIn profile around the roles you're targeting, so your profile reads as capable as you actually are.
Career Path Walkthroughs
What to Expect from Placements
Resume Rebuild Support
LinkedIn Profile Makeover
Role Eligibility Reviews
Personal Branding Guidance
Extra Upskilling Support
Key Support
Stage 03
Apply, Interview & Land the Role
This is where preparation turns into real opportunity. Our placement team matches your profile to live openings, preps you for each specific interview, coordinates directly with employers, and stays with you — feedback and all — until an offer is in hand.
Live Job Opportunities
Profile-to-Role Matching
Role Briefings
Application Support
1:1 Interview Prep
Interview Scheduling Help
Post-Offer Support
Key Support
AI in Health Care Industry — FAQs
Everything you need to know about the course and admissions.
Classes are held live online and at our campus: 5th Floor, Draper Startup House Building, Rajiv Gandhi Nagar, Gachibowli, Hyderabad 500032.
Applying AI to clinical and healthcare operational challenges.
Eligibility depends on your background. See the "Who Should Do The AI in Health Care Industry?" section on this page for the details.
The program runs for 6 Months.
The next cohort starts on 10 Nov 2026. Contact our admissions team to confirm seats and timings.
Yes. You receive a certificate of completion once you finish the program requirements.
Yes. We provide placement assistance, including mentorship, resume support and mock interviews. Placement assistance is not a guarantee of a job.
Yes. The course is built around hands-on practice and projects, ending with a capstone project you can add to your portfolio.
Yes. Mentors review your work and clear doubts throughout the program, not only during the capstone.
A laptop with a stable internet connection. Setup for the tools used is covered at the start of the course.
Our refund and cancellation terms are published on the Refund Policy page of this website. Please read them before you enrol, or ask our admissions team.
