AI for Healthcare Applications—MBA

Theme: AI Integration

Summary of Initiative

The College of Business (COB) at Al Ain University integrates AI-driven experiential learning into its MBA program’s healthcare management focus area through courses such as AI for Healthcare Applications, in which students use the KNIME analytics platform to apply machine learning algorithms to real healthcare data. This innovative approach enhances analytical thinking, decision-making, and practical AI competencies.

Call to Action for Initiative

The AI for Healthcare Applications course, developed for the COB’s MBA focus area in healthcare management, was inspired by the UAE’s national priorities and the rapid digital transformation reshaping the healthcare sector. His Highness Sheikh Mohammed bin Rashid Al Maktoum, UAE vice president, prime minister, and ruler of Dubai, in the “UAE National Strategy for AI 2031,” states, “We want the UAE to become the world’s most prepared country for Artificial Intelligence.” This national vision underscores the country’s ambition to position itself as a global hub for AI innovation, research, and development.

The UAE identifies AI as a key driver of innovation and competitiveness across all sectors, including healthcare. The government’s strategy aims to enhance the nation’s global leadership through partnerships with leading international technology firms and by integrating AI into education, business, and public services. Within this context, the healthcare industry is undergoing a profound transformation. AI is increasingly being adopted both globally and locally in diagnosis, treatment planning, hospital management, insurance operations, and administrative processes.

The COB recognizes the importance of AI across various business domains, including the healthcare industry. AI not only supports physicians through robotics and predictive systems for patient care, diagnosis, and treatment but also enhances the efficiency of healthcare administrators and decision-makers.

AI-driven predictive and machine learning models can optimize hospital operations, streamline patient admissions, and improve the use of medical and financial resources. These functions are often managed by professionals with backgrounds in business or healthcare management who must be capable of using AI tools and interpreting AI-generated outcomes to support evidence-based, data-driven decisions.

However, most MBA students come from non-technical backgrounds—including the physicians, nurses, healthcare administrators, and business professionals targeted for the program—and they initially perceived AI as complex, assuming it required programming expertise. The AI Applications in Healthcare course was designed to equip MBA students with the analytical, technological, and managerial skills required to make informed decisions in healthcare environments.

In summary, the initiative was inspired by the UAE government’s commitment to AI leadership, the accelerating digital transformation of healthcare, and the need to make AI education accessible to non-technical MBA students. The initiative bridges business and technology education, empowering future healthcare professionals and leaders to use AI effectively to enhance both patient care and healthcare organizational performance.

Description of Initiative

The AI for Healthcare Applications course at Al Ain University’s College of Business was designed to integrate AI into the MBA’s healthcare management focus area to enhance students’ analytical, technological, and managerial competencies.

The course aimed to achieve the following course learning outcomes (CLOs):

1. Examine the basic concepts and principles of healthcare systems and their interaction with AI.
2. Synthesize an understanding of national and international policy frameworks governing the use of AI in healthcare.
3. Analyze and evaluate datasets and computational procedures to answer clinical and managerial questions.
4. Apply AI technologies to develop practical AI applications in healthcare.
5. Evaluate the effectiveness of AI technologies in developing healthcare solutions.
6. Formulate written reports and oral presentations to communicate AI-based, data-driven, and personalized healthcare solutions effectively.

To achieve these outcomes, the course instructor, Mohanad Halaweh, implemented an innovative pedagogical approach that combined theoretical understanding with hands-on application using KNIME, an open-source analytics and machine learning platform that supports low-code/no-code development. KNIME allows students to visually build, test, and evaluate machine learning models without requiring programming knowledge.

The course began with foundational topics introducing AI concepts and subfields, the healthcare environment, and ethical frameworks and evaluation standards for AI in healthcare. The instructor then introduced the practical component, in which students participated in interactive lab sessions using KNIME to experience the entire machine learning process—data preparation, model building, testing, and evaluation. Step-by-step tutorials were provided to help students understand how algorithms function and how AI could be applied to healthcare problems.

Applications related to patient care, such as predicting diabetes or hospital admissions, were demonstrated through these tutorials. Students experimented with supervised and unsupervised learning techniques such as regression, classification, clustering, and association to analyze real-world healthcare data.

For example, one project used datasets from Kaggle, an online data science community, to build predictive models for obesity risk. Obesity is a major global health issue associated with heart disease, diabetes, and other chronic conditions. Students applied machine learning models to predict obesity based on various indicators, such as age, weight, and lifestyle factors. They learned to evaluate model accuracy, interpret outputs, and translate findings into actionable managerial recommendations. Students produced a final report and presentation that summarized key insights from their analysis, discussed the importance of specific features in predicting obesity, and provided recommendations for healthcare professionals and other stakeholders.

The course also emphasized ethical and responsible AI use, ensuring that graduates not only gained technological expertise but also developed a strong understanding of AI accountability and ethical implications in healthcare.

This innovative initiative bridged the gap between business education and AI practice, preparing MBA graduates to lead digital transformation in the healthcare sector through the use of machine learning, a core area of AI.

Impact of Initiative

The impact of this initiative can be observed in multiple dimensions, both within the course and beyond, demonstrating not only the successful achievement of the CLOs but also the initiative’s broader educational, professional, and scholarly influence. For assessment of the CLOs, the results clearly showed that students achieved the targeted learning outcomes (80 percent or above) with strong performance across both theoretical and applied components of the course (CLO1: 88 percent; CLO2: 94 percent; CLO3: 90 percent; CLO4: 88 percent; CLO5: 92 percent; CLO6: 92 percent).

Quantitative and qualitative feedback from students’ evaluations of the course were also positive. The overall student satisfaction rating was 96.66 percent. Student feedback further illustrates the educational impact of the course:

• “Learnt a lot from the AI ML model. Was an eye opener and a good experience.”
• “Wonderful teaching method with good examples.”
• “AI is the new trend worldwide and it was a very great opportunity to learn about AI and its applications in the healthcare sector.”
• “Make AI one of the fundamental courses in the program because it is the current trend now and everyone should know how to use it and benefit from it in the different sectors.”
• “I wanted to know more about AI and I wish that there are more subjects related to AI—an amazing dr number 1.”

After completing the course, one student shared the following sentiments:

It was a great experience to take the AI Application in Healthcare course with you. I learned so much from the course, and I liked using the KNIME software. If possible, I would like to ask for your advice as I would like to know more about AI applications in healthcare, especially with the KNIME software. How can I improve myself and learn more about this? How can I convince my future workplace to start applying AI? Thank you so much for your effort in this course. You made it easy for me to understand—and I believe my other classmates feel the same—since I come from a medical background and I am not well-educated in AI or even MIS, but your teaching made things simple and easy to understand, so I just wanted to thank you, Professor, for that.

This feedback reflects the initiative’s impact beyond academic achievement. Students not only developed technical and analytical competencies but also gained curiosity and motivation to continue exploring AI applications in their professional contexts.

The initiative also inspired Halaweh to contribute to the wider academic community through a paper titled “Teaching Tip: AI and Machine Learning for Business and Information Systems Education Using KNIME” (Journal of Information Systems Education, 2025).

In summary, the impact of this initiative is evident through strong CLO achievement, high student satisfaction, lifelong learning motivation, and scholarly dissemination—contributing to both educational advancement and the integration of AI in business education.

Additional Information

• “Teaching Tip: AI and Machine Learning for Business and Information Systems Education Using KNIME,” Journal of Information Systems Education, by Mohanad Halaweh