| CODE | ARI5420 | ||||||||||||
| TITLE | Artificial Intelligence in Healthcare | ||||||||||||
| UM LEVEL | 05 - Postgraduate Modular Diploma or Degree Course | ||||||||||||
| MQF LEVEL | 7 | ||||||||||||
| ECTS CREDITS | 5 | ||||||||||||
| DEPARTMENT | Artificial Intelligence | ||||||||||||
| DESCRIPTION | This study-unit will introduce students to the application of Artificial Intelligence techniques in a healthcare-related setting. This study-unit will first focus on providing the motivation for how AI is useful in situations such as mining unstructured medical data, minimizing diagnostic errors, predicting patient outcomes from clinical data, providing early diagnosis, and suggesting treatments. This will be followed by a survey of various popular technologies and algorithms. These will include, so called, ‘classical’ AI techniques such as neural networks, as well as state-of-the-art Deep Learning algorithms. The study unit will also provide various case studies on how AI has been successfully used in medical applications, what their limitations are, and the hurdles in real-world applications of AI in healthcare. Study-unit Aims: The aim of this unit is to provide students with the knowledge and skills to critically appraise the importance and extent of the application of AI in healthcare: In particular this will: - expose students to several survey content related to the current status of Artificial Intelligence in Healthcare; - introduce a number of case studies where Artificial Intelligence is used in healthcare environments; - familiarize students with the hurdles related to implementing Artificial Intelligence in real-life situations; - provide students with an overview of classical as well as state-of-the art technologies such as Artificial Neural Networks, Support Vector Machines, Natural Language Processing for dealing with unstructured data, regression, clustering, machine vision, and Deep Learning. Learning Outcomes: 1. Knowledge & Understanding By the end of the study-unit the student will be able to: - describe and appraise the areas in healthcare which can and currently do benefit from AI; - gain an in-depth understanding of which AI techniques are relevant in which healthcare related scenarios; - distinguish between the different types of AI paradigms (e.g. supervised vs. unsupervised learning, inductive vs. deductive reasoning); - gain an in-depth understanding of the basics of how to use and apply various classical and state-of-the-art AI technologies to real-world situations; - evaluate the limitations and hurdles which exist in applying AI in medical situations. 2. Skills By the end of the study-unit the student will be able to: - employ the AI techniques covered in simple healthcare related scenarios; - compare the appropriateness of various AI techniques in various healthcare related settings. Main Text/s and any supplementary readings: - Artificial Intelligence: Modern Approach, Stuart J. Russell and Peter Norvig (4th Edition) ISBN-13: 978-0-13-461099-3 - Intelligence-based Medicine, Anthony Chang (1st Edition) ISBN: 9780128233375 |
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| STUDY-UNIT TYPE | Lecture and Independent Study | ||||||||||||
| METHOD OF ASSESSMENT |
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| LECTURER/S | Josef Bajada Kristian Guillaumier (Co-ord.) Konstantinos Makantasis |
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The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints. Units not attracting a sufficient number of registrations may be withdrawn without notice. It should be noted that all the information in the description above applies to study-units available during the academic year 2023/4. It may be subject to change in subsequent years. |
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