Bachelor of Science in Applied AI in Healthcare (BSc.AIH)

Plan of Study Bachelor of Science in Applied AI in Healthcare (BSc. AIH)

Semester – 1

Course Code	Course Title	C H	L H	N L H	Pre-requisites	Department
ENG 101	English Language	3	3	–		General Education
BSE 101	Behavioural Sciences and Ethics	3	3	–		General Education
EMS 101	Emirati Studies	3	3	–		General Education
ITH 101	Information Technology for Healthcare Professionals	2	1	2		General Education
MAT 101	Mathematics	2	2	–		General Education
MET 102	Medical Terminology	1	1	–
Semester Total		14	13	2

C H – Credit Hours, L H – Lecture Hours, N L H – Non-Lecture Hours

 

Semester – 2

Course Code	Course Title	C H	L H	N L H	Pre-requisites	Department
BIS 101	Biostatistics and Research Methodology	3	2	2	MAT 101	General Education
AIH 102	Programming-I	3	2	2
AIH 103	Discrete Mathematics	3	3		MAT 01
AIH 104	Linear Algebra	3	3		MAT 01
AIH 105	Operating System and Networking	3	3
Semester Total		15	13	4

C H – Credit Hours, L H – Lecture Hours, N L H – Non-Lecture Hours

 

Semester – 3

Course Code	Course Title	C H	L H	N L H	Pre-requisites	Department
AIH 201	Software Engineering	3	2	2
AIH 202	Programming -II	3	2	2	AIH102
AIH 203	AI in Healthcare	3	2	2
AIH 204	Machine Learning	3	3	–
AIH 205	Electronic Health Record Management	3	2	2
Semester Total		15	11	8

 

Semester – 4

Course Code	Course Title	C H	L H	N L H	Pre-requisites	Department
AIH 206	Data Structure and Algorithm	3	2	2
AIH 207	Health System Sciences	3	3	0
AIH  208	Computational Methods in Health Care Systems	3	2	2	AIH 102, AIH 202
AIH 209	Fundamentals of Health Informatics	3	2	2
AIH 210	Medical Information Security & Privacy	3	3	0
AIH 211	Knowledge Representation for Health Care	3	3	0	MET102
Semester Total		18	15	6

C H – Credit Hours, L H – Lecture Hours, N L H – Non-Lecture Hours

 

Semester – 5

Course Code	Course Title	C H	L H	N L H	Pre-requisites	Department
AIH 301	Deep Learning	3	2	2	AIH 203, AIH 204
AIH 302	Modeling and Optimization of Healthcare Systems	3	3	0
AIH 303	Medical Image Computing	3	3	0	AIH 203, AIH 204
AIH 304	Natural Language Processing in Healthcare	3	3	0	AIH 206
AIH 305	Cloud Computing	3	3	0
AIH 306	Reinforcement Learning	3	3	0	AIH 208
Semester Total		18	17	2

 

Semester – 6

Course Code	Course Title	C H	L H	N L H	Pre-requisites	Department
EIS 101	Entrepreneurship, Innovation, and Sustainability	2	1	2
AIH 307	Generative AI for Healthcare	3	3	0	AIH301, AIH304
AIH 308	Digital Health	3	3	0
AIH 309	Ethics of AI in Healthcare	3	3	0
AIH 310	AI for Precision Medicine	3	3	0
AIH 311	Research Project in AI for Healthcare	3	0	6
Semester Total		17	13	8

C H – Credit Hours, L H – Lecture Hours, N L H – Non-Lecture Hours

 

Semester – 7

Course Code	Course Title	C H	L H	N L H	Pre-requisites	Department
AIH 401	Medical Robotics and Automation	3	3	0
HTA 402	Health Technology Assessment	2	2	0
AIH 402	Introduction to Quantum Computing	3	3	0
AIH 403	Project Management	3	3	0
AIH 404	Digital Twins and Simulations in Healthcare	3	3	0
AIH 406	Capstone Project in Healthcare AI	3	0	6
Semester Total		17	14	6

 

Semester 8

Course Code	Course Title	Description	Pre-requisite	CH
INT 501	Internship in Healthcare AI	Students must complete an internship training program in a healthcare organization where students identify a real-world AI challenge, design and implement a solution under supervision, and submit a comprehensive final report.	Successful completion of all Semester 1–7 core and elective courses and approval of the internship proposal by the Internship Coordinator.	9
Semester Total				9

C H – Credit Hours, L H – Lecture Hours, N L H – Non-Lecture Hours             
123 Credits
Maximum 18 credits in a semester

 

Course Description

Year 1: Semester 1

ENG 101 English Language

This course provides instruction in written academic English tailored to the field of Applied AI in Healthcare. It emphasizes the development of language and communication skills necessary for academic study and professional practice in healthcare and AI contexts. Students will engage with case studies drawn from healthcare and artificial intelligence to build transferable skills such as critical thinking, problem-solving, and effective communication. Through discussion-based and group activities, they will practice reflecting on real-world scenarios that combine medical and technological perspectives. The course also introduces modules on substantive and technical written communication, including organizing ideas into clear expository and argumentative essays. Students will gain familiarity with APA and Harvard citation styles and develop foundational skills in scientific and technical writing, preparing them for advanced coursework and research in Applied AI in Healthcare.

BSE 101 Behavioural Sciences and Ethics

The course is designed to provide an overview of the main topics in behavioral sciences and ethics, including the biological basis of behavior, mental processes, sensation and perception, learning, motivation, intelligence, human development, personality, socialization, social groups, changing trends, individual challenges, and universal ethical principles. The objective of the course is to help students understand and apply the knowledge, skills, and attitudes developed to communicate effectively. Students will learn and practice strong values, ethical conduct, and social responsibility, with a focus on personal, academic, and professional integrity while fostering collaboration in diverse team settings. They will also be trained to demonstrate sensitivity to cultural, psychosocial, and ethical issues.

EMS 101 Emirati Studies

This course provides an in-depth exploration of the most significant aspects of the United Arab Emirates, offering students insights into the features of Emirati society. It covers economic and social development, affirms the nation’s core values and heritage, and includes studies in history, geography, internal and external policies, social systems, human development, and demographics. The course emphasizes the role of Emirati citizens in development, with particular focus on women’s empowerment and their contributions to society. It also highlights the country’s commitment to sustainable energy, economic progress, and development indicators, as well as its global competitiveness. The course further examines future strategic development plans and the challenges they present, recognizing the UAE’s pioneering role on the international stage and its achievements in global development and competitiveness indicators. It provides a detailed analysis of the social aspects of Emirati society, focusing on its unique culture, community dynamics, and the interaction of multiethnicity and cultural diversity, supported by the values of tolerance and indigenous traditions. By the end of the course, students will have developed an understanding of multiculturalism and the ability to connect their knowledge to a global context.

ITH 101 Information Technology for Healthcare Professionals

This course provides the essential principles and knowledge of technology sciences for healthcare professionals, necessary for their daily practice in the field of digital health. It also introduces concepts that help develop practical skills in accessing and using information to deliver quality patient care, applying educational technology, and building electronic communication skills.

MAT 101 Mathematics

This course is designed to provide students with basic math skills useful for solving real-life business problems. The topics include arithmetic operations such as fractions, decimals, percentages and their conversions, ratios and proportions and their applications, linear and quadratic equations, graphing and evaluating functions, significant figures and rounding, matrices, exponents and logarithms, and fundamentals of statistics. The course provides a solid foundation to help students analyze and interpret techniques used in business studies.

MET 102 Medical Terminology

The Medical Terminology course introduces the professional language used by those directly and indirectly involved in the health sciences sector. It is designed to give students a clear understanding of medical terminology, from word origins to practical application. Students will study Latin and other common prefixes and suffixes used in medical terms. The course emphasizes the word-building system, focusing on how terms are formed from their origins. Through this, students will develop the skills to understand complex medical terms by analyzing their components. By the end of the course, students will be able to use and comprehend terminology commonly applied in the medical field.

 

Year 1: Semester 2

BIS 101 Biostatistics and Research Methodology

This course focuses on biostatistics and its application to health and medical problem-solving through an analytical approach. It introduces students to the fundamentals of statistics, including the design and analysis of clinical trials. By the end of the course, students will understand variables, data description, probability, and the principles of both descriptive and inferential statistics. They will gain the ability to apply data analysis techniques in health sciences and make informed decisions about selecting appropriate statistical methods based on the type of data and study design, thereby addressing research questions effectively. The course also serves as a foundation for research methodology modules and research projects.

AIH 102 Programming-I

This introductory course establishes a foundation in programming using Python. Students will learn the essential constructs required for developing structured programming solutions. Topics include basic syntax, control flow (conditional statements and loops), modular programming with functions and built-in modules, core data structures, robust file handling and error management, as well as an introduction to data analysis and visualization. The course is designed to build strong problem-solving skills and prepare students for advanced topics in artificial intelligence and machine learning.

AIH 103 Discrete Mathematics

This course introduces the fundamental principles and methodologies of discrete mathematics with a focus on healthcare applications. Students will explore the study of finite systems, which has become increasingly important with the integration of computer technologies in healthcare. The course first covers standard topics such as sets, relations, functions, and algorithms. It then highlights logic, counting, and probability. In addition, graphs, directed graphs, and binary trees are discussed. Finally, the course includes chapters on the properties of integers, ordered sets and lattices, and Boolean algebra.

AIH 104 Linear Algebra

This course introduces the fundamental principles and methodologies of Linear Algebra (LA), assuming no prior knowledge of the subject. It emphasizes developing a clear understanding of definitions, theorems, and proofs, with a focus on healthcare applications. Students will learn to formulate LA problems, progressing through vector techniques, matrix algebra, and linear equations, and will further explore advanced topics such as vector spaces, inner product spaces, and linear mappings. By integrating theoretical concepts with practical exercises, the course equips students with the analytical and technical skills needed to apply linear algebra strategies for decision-making and improving operational efficiency in healthcare settings.

AIH 105 Operating System and Networking

This course provides a fundamental understanding of operating systems and networking, covering key concepts such as process management, memory management, file systems, network protocols, and security. Students will study OS structures, scheduling algorithms, network architectures, and troubleshooting techniques. The course emphasizes the principles of modern operating systems and computer networks, equipping students with the knowledge to configure, analyze, and optimize system performance. Through lectures, hands-on exercises, and case studies, students will develop practical skills in operating system management and networking, essential for computing and software development.

 

Year 2: Semester 3

AIH 201 Software Engineering

This course provides an in-depth study of software engineering principles and their application in artificial intelligence solutions for healthcare. Students will examine software lifecycle models, systematic approaches to requirements analysis, and effective software design principles. The course also covers practical strategies for implementation, rigorous software testing methodologies, and the integration of agile and DevOps practices tailored to healthcare AI systems. Ethical considerations and security measures essential in healthcare environments form a core part of the learning, preparing students to develop innovative, compliant, and reliable AI software solutions.

AIH 202 Programming -II

This course builds upon foundational programming skills to delve into sophisticated techniques for analyzing complex healthcare datasets. Students will explore advanced Python constructs, data merging and cleaning strategies, and effective manipulation of large datasets through intricate indexing, slicing, aggregation, and grouping methods. The course further emphasizes efficient array operations, advanced data visualization techniques, and the development of predictive models coupled with robust preprocessing methods. Through a blend of hands-on projects, practical exercises, and real-world case studies, students will acquire the analytical and technical skills essential for extracting actionable insights from healthcare data and advancing AI-driven healthcare solutions.

AIH 203 AI in Healthcare

This course examines the intersection of Artificial Intelligence (AI) and healthcare, with a focus on the transformative role of AI in clinical practice. Students will study health IT systems, including data semantics, interoperability standards such as ICD-10, FHIR, and SNOMED CT, as well as clinical decision support tools. The course includes hands-on tutorials using real-world Electronic Health Record (EHR) data from MIMIC-III to develop skills in data search, natural language processing, machine learning, deep learning, and generative AI. Through applied projects, students will gain practical experience in AI applications for healthcare analytics, predictive modeling, and decision-making, preparing them for emerging roles in digital health innovation.

AIH 204 Machine Learning

This course introduces students to fundamental machine learning techniques with a focus on their applications in healthcare. It covers topics from foundational concepts and data representation to supervised and unsupervised learning methods and predictive modeling and ethical considerations. The course equips students with both theoretical insights and practical skills necessary for developing and evaluating machine learning models. Emphasis is placed on techniques such as linear and logistic regression, decision trees, ensemble methods, and neural networks, along with robust data preprocessing and model evaluation strategies, all within real-world healthcare contexts.

AIH 205 Electronic Health Record Management

This course introduces the fundamentals of Electronic Health Records (EHR), with emphasis on standards, workflows, data quality, privacy, security, and interoperability. Students will gain hands-on experience with core EHR modules, including registration, clinical documentation, order entry, and decision support, while also examining governance, usability, and regulatory requirements. The course prepares students to evaluate and design EHR systems that are secure, interoperable, AI-ready, and aligned with modern healthcare practices.

 

Year 2: Semester 4

AIH 206 Data Structure and Algorithm 

This course introduces the essential concepts of data structures and algorithms. Students will learn the basics of arrays, lists, linked lists, stacks, queues, trees, and hash tables, as well as fundamental sorting and searching algorithms. Emphasis is placed on understanding the key properties and efficiency of these structures and algorithms through theoretical analysis and practical implementation. The course is designed to build a solid foundation in algorithmic thinking and problem-solving.

AIH 207 Health System Sciences

This course offers a comprehensive introduction to Health Systems Science, a critical field that bridges the gap between clinical care and the systems within which healthcare operates. Students will explore the structures, processes, and dynamics of health systems, gaining insights into how healthcare is delivered and how professionals collaborate to improve patient outcomes. This course aims to develop foundational skills in teamwork and leadership within health contexts, preparing students to contribute effectively to health systems transformation.

AIH  208 Computational Methods in Health Care Systems

Computational Methods in Healthcare Systems introduces students to algorithms, simulation, optimization, and data-driven models for analyzing and improving healthcare delivery. The course provides essential tools for decision-making, resource allocation, and system modeling, supporting efficient healthcare management and evidence-based policy design through computational and quantitative approaches. Combining theory with practice, it integrates lectures, hands-on assignments, and case studies to prepare students to apply computational methods in real-world healthcare settings.

AIH 209 Fundamentals of Health Informatics

This course provides a clear and concise introduction to health informatics. It covers information systems and applications such as electronic health records, clinical decision support, telehealth, ePatients, social media tools, and system implementation. In addition, the course includes topics on data science and analytics, mobile health (mHealth), principles of project management, and contract negotiations.

AIH 210 Medical Information Security & Privacy 

This course equips students with the principles, regulations, and technical controls necessary to safeguard electronic health information. Topics span data classification, cryptography, identity and access management, network and cloud security, incident response, and privacy-preserving techniques. Emphasis is placed on applying global and regional health-care regulations and on designing AI-ready security frameworks that protect confidentiality, integrity, and availability of medical data while enabling interoperable clinical workflows.

AIH 211 Knowledge Representation for Health Care

This course introduces symbolic knowledge–representation (KR) techniques that allow healthcare data, clinical concepts, and expert rules to be expressed in machine‐interpretable form. Students learn logical foundations, ontology engineering, clinical terminologies, interoperability standards, and reasoning mechanisms that power decision-support and semantic integration solutions. Emphasis is placed on current biomedical ontologies, semantic-web standards, and best practices for assuring quality, governance, and ethical use of knowledge assets within health-information systems.

 

Year 3: Semester 5

AIH 301 Deep Learning

This course provides a structured introduction to Deep Learning, building on prior knowledge from AI in Healthcare and Machine Learning. It begins with core concepts such as neural networks, backpropagation, and optimization, then advances to architectures including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers. Students will develop both theoretical understanding and practical skills through lectures and hands-on sessions. Key topics include model training, regularization, evaluation metrics, and healthcare-focused applications of deep learning. Assignments and projects will give students experience in implementing models with leading deep learning frameworks and working on real-world datasets. The course emphasizes technical proficiency and critical thinking in applying deep learning to complex problems. By the end, students will be able to design, train, and evaluate deep learning models, preparing them for careers or research in AI and data science.

AIH 302 Modeling and Optimization of Healthcare Systems

This course equips students with quantitative modelling and optimization skills needed to analyse, design, and improve healthcare service systems. Learners study deterministic and stochastic techniques including linear, integer, multi-objective, and simulation-based optimisation to support resource allocation, capacity planning, and patient-flow decisions. Emphasis is placed on translating real-world operational challenges into mathematical models, selecting appropriate solvers or simulation tools, and interpreting results within ethical and regulatory constraints.

AIH 303 Medical Image Computing

This course introduces the principles and practical methods used to acquire, process, analyze, and interpret digital medical images. Students explore common imaging modalities, image-processing pipelines, classical computer-vision techniques, and deep-learning architectures tailored to radiology and pathology data. Emphasis is placed on segmentation, registration, radiomics, evaluation metrics, and integration with clinical information systems while addressing ethical and regulatory considerations for AI-enabled medical imaging solutions.

AIH 304 Natural Language Processing in Healthcare

This course introduces the principles and practice of Natural Language Processing (NLP) with a focus on clinical and biomedical text. Students learn linguistic preprocessing, representation learning, transformer-based architectures and downstream tasks such as named-entity recognition, concept normalization, text classification, summarization and question answering while addressing evaluation, bias, privacy and regulatory issues unique to healthcare. The course balances theory with hands-on labs that prepare students to design AI-ready NLP pipelines and integrate them into electronic-health-record (EHR) and decision-support workflows.

AIH 305 Cloud Computing

This course introduces students to the fundamental concepts, models, and technologies of Cloud Computing. It explores the evolution of computing paradigms and the architecture that supports modern cloud infrastructure. Students will learn about various cloud service models (IaaS, PaaS, SaaS) and deployment models (public, private, hybrid, and community clouds). The course covers essential topics such as virtualization, data centers, cloud storage, and resource provisioning. Key enabling technologies like distributed computing, service-oriented architecture, and containerization are also discussed. In addition, students will explore cloud security, compliance, and service level agreements, gaining an understanding of risks and best practices. The course emphasizes the application of cloud computing in real-world scenarios, including business, healthcare, and research environments. By the end of the course, students will be able to evaluate cloud platforms, understand pricing models, and assess the impact of cloud adoption on organizational strategy and IT infrastructure.

AIH 306 Reinforcement Learning

This course provides a comprehensive introduction to Reinforcement Learning (RL), a subfield of Machine Learning that studies how intelligent agents take actions in an environment to maximize cumulative rewards. Students will explore the theoretical foundations, core algorithms, and practical applications of RL. Topics include Markov Decision Processes (MDPs), Dynamic Programming, Monte Carlo methods, Temporal Difference learning, Policy Gradient methods, Deep RL, Multi-Agent RL, and real-world applications. Hands-on programming assignments and projects will give students practical experience in designing, implementing, and evaluating RL algorithms.

 

Year 3: Semester 6

EIS 101 Entrepreneurship, Innovation, and Sustainability

This course aims to introduce students to the innovation process, equip them with basic entrepreneurship skills, and build their understanding of sustainability in the modern professional world. It explains the main principles of the entrepreneurial process, links them with the concept of innovation, and emphasizes sustainable leadership for positive societal change. For future healthcare professionals, the course ensures the development of decision-making skills that balance professional and ethical responsibilities with financial and business considerations.

AIH 307 Generative AI for Healthcare

This course explores deep generative models variational autoencoders, generative adversarial networks, diffusion models, and large language models and their specialized use in healthcare. Students learn to synthesize realistic medical images, clinical text, and electronic health records for data augmentation, decision support, and privacy-preserving analytics. Emphasis is placed on probabilistic foundations, model evaluation metrics, ethical and regulatory constraints, and deployment within healthcare information systems.

AIH 308 Digital Health

This course provides an in-depth understanding of digital health technologies and their role in transforming modern healthcare systems. Students will explore the evolution, infrastructure, and governance of digital health, including telemedicine, mobile health (mHealth), wearable devices, electronic health records (EHR), digital therapeutics, and health information systems. The course emphasizes real-world applications, standards (e.g., HL7, FHIR), regulatory frameworks, privacy and security, and evaluation strategies for digital health interventions. Students will engage in case studies, tool demonstrations, and projects to build competence in leveraging digital tools for enhanced healthcare delivery.

AIH 309 Ethics of AI in Healthcare  

This course explores the ethical, legal, and social implications of artificial intelligence in healthcare. It examines issues such as data privacy, algorithmic bias, transparency, accountability, and patient autonomy. Students will analyze real-world case studies and regulatory frameworks to develop ethical decision-making skills in AI-driven healthcare environments.

AIH 310 AI for Precision Medicine

This course explores the application of artificial intelligence in precision medicine, an emerging approach to disease prevention and treatment that accounts for individual variability in genes, environment, and lifestyle. Students will learn to use genomic, phenotypic, and environmental data with AI tools such as machine learning, deep learning, and AI-assisted biomarker discovery. The course covers omics data analysis, patient stratification, treatment prediction models, and ethical considerations in precision health. Through real-world case studies and project-based learning, students will develop skills in designing AI-driven personalized healthcare solutions.

AIH 311 Research Project in AI for Healthcare

This course offers a rigorous, research-focused experience where students investigate advanced topics in Artificial Intelligence (AI) and its applications in healthcare. Students will identify a significant research problem, conduct a structured literature review, apply AI techniques to healthcare datasets, and generate publishable-quality findings. Emphasis is placed on research methodology, model development, evaluation, ethical considerations, and interdisciplinary collaboration. The course culminates in a formal research report and presentation modeled on academic conferences. Students gain exposure to real-world healthcare challenges, critical analysis, and academic writing, preparing them for graduate studies or research-intensive roles in digital health.

 

Year 4: Semester 7

AIH 401 Medical Robotics and Automation

This course introduces both foundational and advanced concepts in robotics and autonomous systems, with a focus on medical applications. Topics include robotic kinematics, sensors, actuators, control systems, autonomy, and human-robot interaction. Students will study surgical robots, assistive systems, rehabilitation devices, and mobile robots in clinical settings. The course emphasizes healthcare integration, ethical and legal considerations, and interprofessional collaboration. Through case studies, simulations, and team projects, students will evaluate and design robotic solutions for clinical environments. It equips learners to apply AI and automation to improve diagnosis, therapy, and hospital operations.

HTA 402 Health Technology Assessment

This course aims to study the different tools of economic evaluation of healthcare technology and programs to reduce costs and improve health-related quality of life. It also covers features of health technology assessment methods used in different countries. Strategies to perform a comprehensive evaluation to decide the merits and demerits of health technologies are also covered.

AIH 402 Introduction to Quantum Computing

The course begins with the principle of superposition, which contains essential features of quantum mechanics, energy quantization, and the concept of qubits. It then covers entanglement, the Bell inequality, applications of entanglement, and quantum gates. Typical circuits such as teleportation and superdense coding are introduced, along with tensor products, computational complexity, and three well-known algorithms: Deutsch, Grover, and Shor. Finally, the course describes different physical platforms for quantum computing, including quantum error correction.

AIH 403 Project Management

The course emphasizes the critical role of project management in achieving unique objectives within limited resources and strict time constraints. It explores the complexities involved in managing projects that require coordination across people, teams, and organizational structures. Through this course, students gain a comprehensive understanding of the project environment, the phases of the project life cycle, and the tools and techniques essential for effective planning, execution, and control. It equips learners with practical insights to navigate real-world challenges in project implementation and delivery.

AIH 404 Digital Twins and Simulations in Healthcare

This course introduces the foundational concepts, technologies, and applications of Digital Twins and simulation models in the healthcare domain. Students will explore how digital replicas of physical healthcare systems, including patients, medical equipment, and hospital operations, can enhance diagnostics, treatment personalization, predictive analytics, and resource optimization. Emphasis is placed on integrating Internet of Things (IoT), AI/ML, data analytics, and ethical considerations in the development and deployment of Digital Twins. The course combines theoretical insights with practical simulation projects, preparing students to leverage these emerging technologies to improve healthcare delivery and outcomes.

AIH 406 Capstone Project in Healthcare AI

This capstone project course focuses on the application of Artificial Intelligence (AI) in real-world digital healthcare solutions, secure digital healthcare systems, and the development of smart healthcare environments. The course integrates cutting-edge AI techniques, healthcare data security measures, and innovative smart healthcare technologies to create solutions that address current challenges in healthcare delivery, data privacy, and patient outcomes. Students will engage in project-based learning, applying theoretical knowledge to practical problems in healthcare, and will contribute to the advancement of the field by developing AI-driven applications or frameworks for healthcare systems.

 

Year 4: Semester 8

INT 501 Internship in Healthcare AI

The course offers students hands-on experience in applying artificial intelligence solutions to real-world healthcare problems. Through supervised placements in hospitals, research labs, or health-tech companies, students engage in projects involving data analytics, predictive modeling, and clinical decision support. The internship enhances their technical, ethical, and interdisciplinary competencies, preparing them for impactful roles at the intersection of AI and healthcare.

1. The applicant must have completed a minimum of 12 years of school education.
2. The applicant must have passed any one of the following English Language Proficiency Tests with a minimum score as follows:
   • For those who completed English as their medium of instruction in high school
     • Minimum 80% (UAE education system or its equivalent) in Grade 11/12
   • For those who completed high school in their national language:
     • TOEFL CBT 173 – iBT 61
     • 5.0 in IELTS for Academic
3. Applicants from UAE educational systems (all tracks) must have secured a minimum aggregate score equivalent to 60% in Grade 12.
4. Applicants from any other non-UAE educational system must submit an Equivalency Certificate of their High School certificate from the Ministry of Education, UAE. Failing to submit the equivalency certificate, the student would be on conditional admission and is required to fulfill the requirements specified in the General Admission Requirements.
5. Applicants from any other non-UAE educational systems not listed above must have secured a minimum aggregate score equivalent to UAE 60% as per the International Grade Conversion Table published by World Education Services (WES).
6. The applicant must have passed Math in any of the following grades: 11^th or 12^th. If not, they must appear for an admission exam or may register for the non-credit remedial course offered by the University.
7. All applicants shall be evaluated for cognitive and non-cognitive traits demonstrating their aptitude for the chosen area of study by the Admissions Committee of the College.
8. On successful completion of the above, the applicant and parent meet the admissions committee. The decision of the Admission Committee shall be final and binding.

Special needs applications

GMU is committed to admitting students who need special attention and management. Applications are open for students who disclose their condition on the special determination form and the applications are reviewed by the admission committee and admitted as per the policy for different programs.

Required documents:

• Applicant Passport (Ethbara for UAE nationals)
• Emirates ID
• Family book for UAE nationals
• 10^th grade and 12^th or O level and AS/A level High school certificate
• Equivalency certificate for international curriculum students
• Valid English proficiency certificate (IELTS or TOEFL)
• Good conduct certificate
• Health Insurance document for UAE residents
• Scanned passport-size photograph with white background

All originals shall be scanned and returned to the applicant.

Competencies developed by the program include, but are not limited to:

Knowledge and understanding

• Explain core concepts in artificial intelligence, machine learning, and data science for healthcare.
• Describe healthcare systems, clinical workflows, and common medical data sources such as EHR, imaging, and sensor data.
• Recognize health informatics standards and terminologies, including ICD-10 and SNOMED CT, and their use in practice.

Technical and analytical skills

• Write clear, maintainable code to acquire, clean, and analyze healthcare datasets.
• Build, train, and evaluate machine learning models, including deep learning, natural language processing, and computer vision for clinical tasks.
• Apply modeling, optimization, and simulation to improve healthcare operations and decision making.
• Use interoperable data methods and APIs, including HL7 and FHIR, to integrate systems and applications.

Health informatics and systems integration

• Manage electronic health records data securely, with attention to data quality, provenance, and interoperability.
• Design end-to-end AI pipelines that fit clinical workflows and information systems in hospitals and clinics.
• Deploy and monitor AI solutions using appropriate software tools and cloud platforms.

Ethics, legal, and privacy

• Apply ethical principles, legal requirements, and regulatory guidance for responsible AI in healthcare.
• Implement security and privacy practices for health data, including risk awareness and safe handling of sensitive information.
• Assess model fairness, safety, and reliability, and document limitations and safeguards.

Research, innovation, and problem-solving

• Formulate clear problem statements from real clinical or management needs and select suitable AI methods.
• Conduct applied research through a supervised project and a capstone, analyze results, and draw valid conclusions.
• Translate findings into practical recommendations and prototype solutions that address stakeholder requirements.

Professional communication and teamwork

• Communicate technical concepts, methods, and results in clear language to clinical and technical audiences.
• Collaborate in interdisciplinary teams, plan and manage tasks, and meet agreed timelines and quality standards.
• Reflect on professional practice, act with integrity, and plan for continuous learning and development.

Work readiness

• Demonstrate workplace skills through a structured 15-week internship, including professional conduct, documentation, and reporting.
• Build a portfolio that shows competence in AI for healthcare, aligned with Level 6 expectations of the QF Emirates framework.

Graduates of this program can pursue diverse and rewarding careers at the intersection of healthcare and technology. Graduates may find employment opportunities in hospitals, healthcare systems, technology companies, research institutions, consulting firms, and government agencies. Potential roles include:

• Health Informatics Specialist: Managing and analyzing healthcare data to improve clinical workflows and patient outcomes.
• AI Solutions Architect for Healthcare: Designing and implementing AI-driven solutions tailored to healthcare challenges.
• Data Scientist in Healthcare: Applying data science techniques to solve healthcare problems, from predictive analytics to population health management.
• Clinical Data Analyst: Analyzing clinical data to support evidence-based decision-making and operational efficiency.
• Digital Health Consultant: Advising healthcare organizations on the adoption and optimization of digital technologies, including AI.
• Healthcare AI Researcher: Conducting research on AI applications in healthcare, including diagnostics, treatment optimization, and patient monitoring.
• Health IT Project Manager: Leading the development and implementation of health IT projects, ensuring alignment with organizational goals.
• Ethics and Compliance Officer in Health Informatics: Overseeing ethical considerations and regulatory compliance in AI and health informatics initiatives.
• Healthcare Quality Improvement Analyst: Utilizing data-driven insights and AI tools to enhance the quality and safety of healthcare delivery.
• Entrepreneur in Digital Health: Developing innovative AI and health informatics solutions to address unmet needs in the healthcare industry.