Traditionally, the pipeline of a classical systematic review starts with the reviewer doing a keyword search to retrieve all potentially relevant references.

The reviewer can then start screening the abstracts and titles to assess the potential relevance to his/her particular research question.

For an experienced reviewer, it takes between 30 seconds to a couple of minutes to classify a single abstract, which easily results in hundreds of hours spent merely on abstract screening.

After all of the abstracts have been screened by the reviewer, the result is a subset of the initial search containing all potentially relevant references. The reviewer reads all of the full-text versions and writes his awesome paper.

Traditionally, the pipeline of a classical systematic review starts with the reviewer doing a keyword search to retrieve all potentially relevant references.

The reviewer can then start screening the abstracts and titles to assess the potential relevance to his/her particular research question.

For an experienced reviewer, it takes between 30 seconds to a couple of minutes to classify a single abstract, which easily results in hundreds of hours spent merely on abstract screening.

After all of the abstracts have been screened by the reviewer, the result is a subset of the initial search containing all potentially relevant references. The reviewer reads all of the full-text versions and writes his awesome paper.

Similarly, in the research cycle of a systematic review with machine-aided systematic reviewing, the reviewer also starts with a keyword search to retrieve all potentially relevant references, downloads these and imports them into a reference manager.

Then, the reviewer selects some relevant target papers. A machine learning model is trained on these papers to predict which reference to present next. Subsequently, the reviewer enters the active learning cycle.

Since the abstracts are presented from most to least relevant, the reviewer can stop reviewing after having seen all relevant abstracts. This form of active learning will save hundreds or hours time going through all references.

Since we embrace Open Science, all decisions made by the reviewer as well as all the technical information is stored in a log-file, which can (or should) be published alongside the paper.

Similarly, in the research cycle of a systematic review with machine-aided systematic reviewing, the reviewer also starts with a keyword search to retrieve all potentially relevant references, downloads these and imports them into a reference manager.

Then, the reviewer selects some relevant target papers. A machine learning model is trained on these papers to predict which reference to present next. Subsequently, the reviewer enters the active learning cycle.

Since the abstracts are presented from most to least relevant, the reviewer can stop reviewing after having seen all relevant abstracts. This form of active learning will save hundreds or hours time going through all references.

Since we embrace Open Science, all decisions made by the reviewer as well as all the technical information is stored in a log-file, which can (or should) be published alongside the paper.