Interaction with autonomous agents for medical decision support

The aim of the research is to explore the interaction between humans and computers by the design of contexts that create efficient ensembles of medical practitioners and learning machines.

The last decade's advancements in machine learning (ML) has led to a dramatic increase in AI capabilities and the viability of learning by example. However, despite impressive technical advances and many successful research projects, machine algorithms for medical diagnostics are to a very small extent used in healthcare today.

One challenge is that for ML algorithms with less than 100% sensitivity and specificity the clinical user needs effective means to assess the validity of results and incorporate this knowledge within the broader context of their diagnostic process.

The research explorations involve viewing this interaction as a process that unfolds over time enabling reciprocal and continuous learning as well as framing machine learning as material in the design process and investigating the limits, extent and characteristic of the design space that this new material affords.

Tool for training a machine learning system
Tool for training a machine learning system to classify tissues. The design emphasizes rapid refinement by near-realtime feedback and enables human-machine synergies by improving by continuously improving with use. Martin Lindvall
Diagnostic prototype for tumor area assessment
Diagnostic prototype for tumor area assessment. Predictions are presented in an input/output-symmetric way that enables direct manipulation of algorithmic predictions and the continous training of the underlying prediction model. Martin Lindvall
interative tool in assissting lymphnode adenocarcinomas
Iterative refinement of annotation strategy while developing a interative tool in assissting lymphnode adenocarcinomas. The medical domain expert uses the output of the preliminary model to assess whether their annotation is wrong or whether the prediction is wrong. Martin Lindvall

ResearchersShow/Hide content

External partners

Sectra AB, Region Östergötland, Region Gävleborg, University of Leeds

WASP research at MITShow/Hide content