PTB is increasing its expertise in AI in medicine
Job openings for ten junior scientists in the field of artificial intelligence in medicine
Artificial intelligence (AI) methods are enjoying much success – even in the medical sector – as they promise to be of great benefit for patients and offer an enormous potential for the economy. However, AI is also creating new challenges. As a National Metrology Institute, the Physikalisch-Technische Bundesanstalt (PTB) is Germany’s highest authority with a legal mandate when it comes to evaluating measurement procedures, and it is now further intensifying its commitment in the field of AI. For this purpose, PTB is recruiting ten junior scientists who are to develop new procedures for the standardized evaluation of AI methods. The thematic focus will be on explainability, robustness and uncertainty of AI systems in the fields of imaging procedures as well as on radiation and laboratory medicine. Before applying for one of the positions, every candidate should select three projects from a list of 13 possible projects. The candidate will then apply for these three projects. The closing date will be March 8, 2021.
Due to rapidly advancing digital transformation, methods of artificial intelligence are increasingly finding their way into all areas of medicine and are also presenting PTB with new challenges. The complexity of correlations, the progress made in measurement technology, the great benefit for patients and the enormous potential for the economy will rapidly increase AI applications in the health sector. AI techniques could be the solution to many challenges: For example, imaging procedures generate large volumes of data which are unmanageable for physicians. According to the German Cancer Research Center (Deutsches Krebsforschungszentrum, DKFZ) in Heidelberg, a total of 675 exabytes of image information were generated by radiologists in 2019, which corresponds to about 13.5 trillion tomographic images – but only about 7 % of the data has been used in medicine. At the same time, the number of registered practice-based radiologists decreased by 2.2 % in Germany from 2018 to 2019, whereas the total number of practice-based physicians increased. This is where AI can be of help in the future: Tasks for which physicians would require years of experience can be processed by artificial intelligence within a few seconds. However, there is to date no generally recognized anchor of trust in quality infrastructure.
PTB is intensively dealing with the subject of AI within the scope of its legal mandate, given, for example, by the German Medical Devices Act. PTB is furthermore developing modern methods for determining the image quality of X-ray images, e.g. in mammography. The aim is to use objective parameters for the image quality and dose to be able to develop an objective target value and optimize these radiological diagnoses – with a view to achieving the best possible image quality at low doses. In magnetic resonance tomography, robust methods for image reconstruction are being developed. Here, machine learning allows, for instance, the image reconstruction of functional MR images to be accelerated in such a way that examinations of moving tumors are also possible. Another example is the world’s largest freely accessible ECG database published by PTB, which provides machine-readable findings and assessments by cardiologists. The dataset can be used as a reference for methods on the market that have often been trained with proprietary data so far. In this way, greater transparency is achieved.
The research program “Metrology for AI in Medicine” (M4AIM) is interdisciplinary and covers AI applications in different fields of medicine. Depending on their special fields, the new staff members will be employed at one of PTB’s two sites, Braunschweig or Berlin, and will work both with experienced PTB colleagues and with experts from the academic AI research and from the cooperating university clinics and hospitals. However, even if the concrete tasks vary, the focus will be on the team as well as on the three major pillars of metrology for AI applications. The first pillar is comprehending the reasons for an AI result. The second pillar is about determining the uncertainty of AI methods, which is the professional and metrological approach to the accuracy of the corresponding measurement methods. The third pillar stands for the generalization and robustness of AI methods. Here, the aim is to develop parameters which can be standardized to evaluate robustness against noisy, unknown or harmful input data. This is the general perspective for the entire team.
With this general focus in mind, each applicant is asked to apply for three projects from a list that can be found on PTB’s website.
Further details about the job openings (including the project list) can be found on PTB’s website: https://www.ptb.de/cms/en/about-us-careers/careers/bms-stellen/bms-stelle.html?tx_jobmodul_pi1%5Bjob%5D=3690&tx_jobmodul_pi1%5BlistBackPid%5D=11489&cHash=f522b853665ad2a72318405787f14348
Read more on the website of the Steering Group for Medicine:
Hans Rabus, Senior Scientist 8.02 "Artificial Intelligence and Simulation in Medicine",
phone: +49 30 3481-7054, email: firstname.lastname@example.org