Session Item

Key points:
- Medical physicist needs to identify the clinical problems and is the key person to link the various technical disciplines (e.g. data scientists, IT) with clinical professionals (e.g. physicians, RTTs).
- Artificial Intelligence follows distinctive (engineering) principles: development, implementation and QA that medical physicists have been doing for the past decades.

Artificial Intelligence based solutions need to follow the same technology adoption in medical physics typically as other engineering principles in medicine. First a clinical unmet need or a problem is defined, next novel solutions are proposed based on the proposed requirements, followed by a phase validation or testing if the new solutions to validate they meet the requirements for use in clinical practice. Finally education of understanding and explaining the new technology to end users (e.g. clinicians, physicists, software engineers, RTTs etc) is important for successful implementation.

Medical physicists need to work together with their surrounding disciplines to safely and efficiently introduce AI implementation into the current care path of patients. This starts with an understanding of the technology presently at hand. Although many medical physicists have by training strong analytical skills for problem solving, detailed knowledge about current machine learning, deep learning or other AI techniques is not ubiquitous. As for other technological advances, medical physicists do not necessarily have to be developers themselves of these solutions but could fulfill a coordinating role in this.

However, playing such a central active role in this field would be attractive for some physicist to define the directions taken and secure the proper clinical implementation. Collaboration with industry and scientific computing research groups is therefor of utmost importance to have access to the latest technology and scientific insights. A simplistic approach could be to team up with data science/data engineers and incorporate the tools from them and apply/interpret them, as this needs to come from the field itself. The success depends on both the quality (and amount) of the data and the ability to rephrase the clinical questions towards the data science engineers.

This is the other aspect that artificial intelligence needs to deal with: access to clinical (patient) data. The hospital IT environment is the landscape is changing rapidly and medical physicists should play a role in this. Besides advice on the requirements for the IT frameworks,  the medical physicist together with the physician is responsible for interpreting and develop the appropriate models: e.g. prediction models or more advanced solutions (cf. example of Babylon). Technology for mining data is only a single aspect of this work, but also challenges are present in aligning the different stakeholders (IT, physician, legal/patient privacy etc)., to strive for collaboration and keeping the common (clinical) goal.

Medical physicist have a tradition to secure the implementation of new technology in clinical practice. The role of MP in this scenario and where MPs need to be involved in the right parts of the projects is currently under discussion as described above. Besides implementation, also development is needed where the industry needed to roll-out these new technologies wants to work with the innovators and early adopters, but these are not all of Medical Physics but only a subselection currently typically located in the larger academic hospitals. AI could be seen as just another medical device that needs training, commissioning and QA. Where the commissioning is not based on the classical dosimetry data but on patient data. Clinical physicists need training for these new developments and commissioning and QA programs need to be developed. The amount of training needed will depend on the specific implementations but as AI will be classified as a medical device the medical physicists should also approach these techniques as such with proper testing and validation (QA) programs in place.