📌 Key facts
- Mission: Explore the integration of AI technologies in the medical industry to enhance productivity and improve healthcare outcomes for doctors and patients. By conducting comprehensive research and analysis, we aim to identify the pain points faced by doctors and uncover innovative solutions through the use of AI. Join us in our mission to shape the future of healthcare by leveraging AI for increased productivity and improved patient care.s.
- When: Start anytime. Applications are open!
- How to apply: Send us an e-mail (at the end of this page) with your CV, a grade report, a short motivation and your suggested approach (proposed plan and questions, potential data, and methods, possible outcomes with a tentative outline).
- 💡 Background
- 🦾Who We Are
- 🎯 Goals
- 🧠 Topics of Interest
- 🎓 Profile
- 📚 Further Reading
- 📄 Requirements to any Work
- 📬 How to Apply
The medical industry plays a critical role in providing quality healthcare to individuals and communities. However, doctors often face numerous challenges, such as heavy workloads, information overload, and time constraints, which can impact their productivity and patient care. To address these challenges, the integration of AI technologies holds great potential to streamline processes, enhance decision-making, and optimize patient outcomes. By understanding the current landscape and exploring the possibilities of AI in the medical industry, we can unlock innovation and transform the way healthcare is delivered.
🦾Who We Are
The Chair for Strategy and Organization is focused on research with impact. This means we do not want to repeat old ideas and base our research solely on the research people did 10 years ago. Instead, we currently research topics that will shape the future. Topics such as Agile Organizations and Digital Disruption, Blockchain Technology, Creativity and Innovation, Digital Transformation and Business Model Innovation, Diversity, Education: Education Technology and Performance Management, HRTech, Leadership, and Teams.. We are always early in noticing trends, technologies, strategies, and organizations that shape the future, which has its ups and downs.
This thesis will be co-supervised
- Identify the specific areas within the medical industry where AI can provide solutions to enhance doctors' productivity and efficiency.
- Investigate the effectiveness of AI technologies in medical research, diagnosis, treatment planning, and patient monitoring to improve healthcare outcomes.
- Assess the ethical and regulatory considerations surrounding the use of AI in the medical industry, including issues of privacy, bias, and accountability.
- Explore the impact of AI on doctors' roles and responsibilities, including the potential for job augmentation, workflow optimization, and skill development.
- Analyze the readiness of healthcare organizations and practitioners to adopt AI technologies and develop strategies for successful implementation.
- Examine the potential benefits and risks associated with the integration of AI in the medical industry, including cost-effectiveness, patient safety, and data security.
- Evaluate the impact of AI on patient-doctor relationships, communication, and the delivery of personalized healthcare services.
- Identify the necessary skills and competencies for doctors to effectively utilize AI technologies in their practice and ensure seamless collaboration with AI systems.
- Foster interdisciplinary collaboration between AI researchers, medical professionals, and policymakers to drive innovation and ensure responsible AI adoption in the medical industry.
- Contribute to the academic and practitioner knowledge on AI in healthcare through rigorous research, analysis, and thought leadership.
🧠 Topics of Interest
- AI has the potential to revolutionize the healthcare industry by alleviating doctors' burden of administrative tasks and streamlining their workflow. Here are some ways AI could help doctors with desk and admin work:
- Automated Documentation: AI-powered natural language processing (NLP) algorithms can analyze doctors' notes, patient histories, and medical records to automatically generate accurate and detailed documentation. This automation reduces the time and effort spent on manual data entry, allowing doctors to focus more on patient care.
- Voice Recognition and Transcription: AI-based speech recognition technology can transcribe doctors' verbal instructions, patient conversations, and medical dictations accurately and efficiently. This enables real-time documentation and minimizes the need for manual transcription, saving valuable time for doctors.
- Intelligent Scheduling and Appointment Management: AI algorithms can optimize doctors' schedules by analyzing patient data, availability, and appointment priorities. AI-powered systems can automatically schedule appointments, manage cancellations and rescheduling, and send reminders to patients, reducing administrative burdens on doctors' staff and improving overall efficiency.
- Clinical Decision Support: AI-driven clinical decision support systems can assist doctors in diagnosing and prescribing treatment plans. By analyzing vast amounts of patient data, medical literature, and clinical guidelines, AI algorithms can provide evidence-based recommendations and alerts for potential drug interactions or allergies. This support enhances doctors' decision-making processes and ensures accurate and timely care.
- Streamlined Billing and Coding: AI technologies can automate billing processes by accurately coding medical procedures, diagnoses, and insurance claims. AI algorithms can review medical charts, extract relevant information, and assign appropriate codes, minimizing errors and improving reimbursement efficiency for doctors and healthcare providers.
- Intelligent Virtual Assistants: AI-powered virtual assistants can assist doctors in managing administrative tasks such as scheduling, retrieving patient information, and answering frequently asked questions. These assistants use natural language processing and machine learning techniques to provide quick and accurate responses, saving doctors time and improving overall productivity.
By leveraging AI technologies, doctors can focus more on patient care, spend less time on administrative tasks, and improve their overall productivity and job satisfaction. It is important to note that while AI can enhance efficiency and accuracy, it should always complement and support doctors' expertise rather than replace their critical decision-making and human touch in healthcare delivery.
- Proficiency in conducting interviews
- Enrolled in a Bachelor’s or Master's program related to Business Administration/Management/Consulting
- Fluent in English (additional proficiency in German is a plus)
- Reliable, self-driven, and motivated
- Demonstrated interest in management consulting and innovation
- Passion for learning and conducting impactful research
📚 Further Reading
- Ayers, J., Poliak, A, & Drezde, M. (2023): Comparing Physician and Artificial Intelligence Chatbot Responses to Patient Questions Posted to a Public Social Media Forum, https://jamanetwork.com/journals/jamainternalmedicine/article-abstract/2804309
- Adams, M. E., Cohen, G. R., & Siwicki, B. (2021). Artificial Intelligence in Health Care: Anticipating Challenges to Ethics, Privacy, and Bias. The Commonwealth Fund.
- Topol, E. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44-56.
- Raghupathi, W., & Raghupathi, V. (2019). Big data analytics in healthcare: promise and potential. Health Information Science and Systems, 7(1), 3.
- Oh, S., & Kim, S. (2021). The role of artificial intelligence in medical diagnosis. Ewha Medical Journal, 44(1), 1-7. 
- Fröhlich, H., & Holzinger, A. (2019). Explainable AI for Explainable Forensics. Machine Learning and Knowledge
- Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347-1358.
- 1. Beam, A. L., & Kohane, I. S. (2018). Big data and machine learning in health care. JAMA, 319(13), 1317-1318.
- Char, D. S., Shah, N. H., Magnus, D., Hlatky, M. A., & Bernstein, A. (2018). Implementation of machine learning in health care—Reply. JAMA, 320(15), 1625-1626.
- Wang, F., Casalino, L. P., Khullar, D., & Deep Learning for Predicting Hospitalization Costs in Advance. JAMA Internal Medicine, 178(11), 1532-1539.
- Aggarwal, A., Sarda, A., & Gupta, V. (2021). Artificial intelligence in healthcare: Opportunities and challenges. Journal of Health Management, 23(1), 4-17.
- Ravi, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., & Lo, B. (2017). Deep learning for health informatics. IEEE Journal of Biomedical and Health Informatics, 21(1), 4-21.
📄 Requirements to any Work
We do not want your research to gather dust in some corner of the bookshelf but make it accessible to the world. Thus, we warmly encourage you to create some or all of the following:
- Infograph - visually represent some of your work
- Slide Deck - prepare a slide deck on your overall topic with relevant, interesting and impactful findings
- MVP- Develop a prototype with us for training medical doctors how to use AI for their benefit
📬 How to Apply
If you are interested, please contact Isabell Welpe and Bernd Storm by submitting your CV, a grade report, a short motivation and your suggested research approach.
We are looking forward to hearing from you!
👉 Bernd: email@example.com
👉 Isabell: firstname.lastname@example.org