Aliado
Designing Collaborative
AI for Surgeons

Aliado was part of a research project exploring how AI-Systems could help surgeons in cancer treatment, which is characterized by information overload. The challenge was to understand limitations of AI-Models in clinical usage and align our and stakeholders' thinking to find suitable solutions.
Futher Facts
Timeframe:
12 Weeks, 2020/21
Skills:
AI-UX, Shadowing, User Research, Co-Creation Workshop Facilitation
Team
Frederic Myers (Designer)
Sven Hornburg (Designer)
Johanna Brandenburg (PhD Student)
Michael Haselbeck-Köbler (PhD Student)
André Schulze (PhD Student)
Cooperation Partners:
UKHD, NCT
Awards:
UXDA 2021, aed neuland 2021
My Role
I researched AI-Technology, prepared the AI & Data Scientist Expert Interviews.
Conducted user research and shadowing together with my fellow students to gain insights.
I was developing the final concept for the surgeons side, worked on wireframes and the prototye.
THE Challenge
+47%
THE Outcome
↑ Watch the video to see how the solution looks like
01 - Structuring medical findings
to make them more accessible
Medical findings are hard to access, even when digitally stored taking up to two minutes to find. They are also not machine readable. The Aliado System scans digital documents using specific search algorithms and highlights important parameters for the doctor to investigate before meeting with the patient. He can annotate these to give the AI-system feedback.
↑ Analizing the findings and highlighting parameters
02 - Summarizing Information
to
support decision-making
After gathering data, surgeons have to prepare a case overview to serve as a base for the decision-making. The Aliado AI-System saves time by automatically generating a case overview out of the patient data. This improves decision-making and creates machine-readable data to train further ML Models.
03 - Surfacing Information to
augment surgeons experience
THE Process
01 - ResearcH
Getting up to speed and into the nitty-gritty of clinical processes
More
5
Days spent in hospital
16
Expert Interviews
02 - Synthesis
Validating and complementing insights, identifying surgeons' pains
More
Piecing together insights with medical students in 2 workshops
Investigating into problems we found in the first phase deeper
03 - Ideation
Brainstorming ideas on how AI could help surgeons during treatment
More
Multiple co-creation sessions with surgeons from various experience levels
Key wishes were a brief overview of relevant patient data & support on decisions
04 - Turning Point
Pivoting the concept towards structuring medical data to make it suitable for AI-Applications
More
We sat together with Data Scientists to get feedback on our ideas
The lack of suitable data made our concept unrealistic
05 - Design
Tailoring the solutions to surgeons needs through co-creation
More
3
Co-Creation Days
Refinining Information Architecture and views




Impact
Looking back two years later, the influence of this project on my thinking was immense. But there have been 2 Learnings that proved to be insightful: