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Human-Centered Artificial Intelligence (AI) and Machine Learning (ML) in Health

The University course will give the students a deeper basic knowledge based on current insights of the most important topics/aspects of Artificial Intelligence and Machine Learning as well as basic knowledge of medicine and biomedical research. The course is intended to serve as a basis for Artificial Intelligence for a more in-depth treatment of various theories and technologies to build an AI system in medicine and biomedical research and to implement new findings in practice.

Graduates are enabled to work at the interfaces between medicine and AI-technology in companies or research centres and develop new intelligent applications for medicine and/or biomedical research. Graduates of the University course Human-Centered Artificial Intelligence (AI) and Machine Learning (ML) in Health (Human-Centerd AI/ML in Health) can:

  • recognise an awareness for technology in the fields of Artificial Intelligence (AI) and Machine Learning (ML) in health care and act adequately upon it
  • develop people-orientated and institutional concepts in the area of human-centred AI in health care, with a great focus on Ethics, responsibility on the rights of privacy and security (human-in-control approach)
  • conceive a definition of application-oriented AI and ML in medicine and to define the application - develope concepts for a safe ethical application of AI and ML in medicine, while considering all norms and regulations - define medical problems and use AI or ML as its solution
  • use different medical or health-related parameters, which include progression of the disease, the effectiveness of the treatment, safety, the quality of life, the individual patient preferences for algorithms for decision making
  • understand analysis in medicine and choose a suitable model type for a specific medical or health-related matter
  • conceptualize and apply AI and ML-modeling using practical examples for medicine and health
  • develop a basic understanding of AI and ML in drug-design and recognize its practical significance
  • develop a basic understanding for the complementary usage of human and artificial intelligence in medicine and recognize its relevance in practice
  • acquire basics to read, understand and assess research reports/articles on AI/ML and on insights in medicine and diagnostics
  • encourage interdisciplinary teamwork and to find solutions for a constructive approach to conflicts
  • plan necessary structural and economical factors in order to participate in the designing processes

The use of technology for Artificial Intelligence (AI) in our society is widespread, mainly due to successes in Machine Learning (ML) (e.g. driverless cars, personal assistants, monitoring systems, robot manufacturing, machine translation, cyber security, web search, etc.). Such applications use technologies of Artificial Intelligence, to interpret information of a variety of sources and enable an intelligent and targeted behaviour. Thereby great importance is also attached to looking at new technologies by a “human in control” approach and transmit values such as an ethical and responsible use of new technologies and data security and data security issues. Also, in health care and research, AI and ML technologies have reached the working environment of treating physicians and researchers. That way it is hoped that in many specialist fields of medicine and research, with a focused use of technical possibilities, new and better treatment plans for individual patients can be adapted. Especially in this area, a “human in control” approach is important – new technologies are to support physicians and not to replace them. The final decision still lies with the medical expert. It is therefore even more important, that the expert has all the necessary competences in order to understand the data and its decision making and to assess the information correctly. For the development of these new technologies it is of similar importance, to have a better understanding of the data and findings of algorithms so that they can be construed better by technicians. In future the integration of AI and ML technologies in medicine, lies in a human-centred AI approach and interactive Machine Learning (“human-in-control”) with knowledge of ethical approaches in this area, data protection and data security. For graduates of the university course Human-Centered Artificial Intelligence (AI) and Machine Learning (ML) in Health (Human-Centerd AI/ML in Health), the following professional fields are of relevance:

  • Medical informatics – Application of informatics to process medical data and simulations of biological processes by applying AI and ML in various areas (diagnostic imaging systems, hospital information systems, medical knowledge systems, data analysis for DNA sequencing, various systems of AI, telemedicine, simulations of modern therapeutic methods … )
  • IT project management – to accompany IT projects from the planning stage to the implementation of software in medicine, medical research, diagnostic, genomics
  • Software architecture – for planning, developing and further developing medical software
  • Medicine –for consulting and product development of medical and diagnostic software incorporating AI and ML
  • Medical research - for consulting and product development of medical and diagnostic software incorporating AI and ML

The University course Human-Centered Artificial Intelligence (AI) and Machine Learning (ML) in Health (Human-Centerd AI/ML in Health) addresses the following:

  • people, who already have a Bachelor-degree in Medicine, Natural Science or technical studies and
  • already in this area working physicians, researchers, bioinformaticians and IT developers, with a minimum of two years of working experience

Degreee from

  • a subject-relevant Bachelor's study, or
  • a subject-relevant Diploma study, or
  • a subject-relevant Bachelor’s study of an advanced technical college (minimum 180 ECTS), or
  • a subject-relevant Diploma study of an advanced technical college (minimum 180 ECTS), or
  • a Diploma study of an equivalent programme at a recognized nationally or internationally accepted tertiary education institution (in accordance to the Austrian University Law §64 Abs 5 UG idgF) and a relevant two year work experience is essential.
  • The study management can confirm one of the qualifications defined in the points above. To meet this condition it is necessary to have gained the post-secondary school Diploma corresponding to the University entrance level of an Austrian University or a University of Applied Science (analogue § 64 UG as amended) and a minimum of a three year subject relevant work experience.

The course is held in English. Therefore, a good understanding of English is required.

The university course:

  • is a part-time postgraduate programme offered as a distance learning course,
  • is designed to be completed alongside full-time work,
  • is completed within two semesters of the study,
  • comprises 6 modules, and
  • includes a diploma of the Medical University of Graz.
  • AI fundamentals
  • ML fundamentals
  • Applied AI / ML for Bioinformatics and Biomedical Data Science
  • AI Ethics, Explainability and Causability
  • Medical Fundamentals
  • Biomedical Research and Drug Design

After the successful completion of all the credits provided for in the curriculum the graduate receives a diploma from the Medical University of Graz.

€ 5.950,--

Registration for the university course starting in winter semester 2021/22 will be possible until 30.06.2021.

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