hi i'm martin designing human-AI interaction for the clinical domain can be challenging
due to the safety critical nature of the work and the probabilistic nature of AI models.
Today I'll present an ai-powered visual search tool that can support pathologists in assessing cancer
specifically we aim to support finding cancer in colorectal lymph nodes these lymph nodes
are removed as part of surgically removing a cancer. In around 30% of cases the cancer could
have spread from the primary tumor to the lymph nodes and through them to the rest of the body
so if the cancer is seen in the lymph nodes the patients will receive chemotherapy that could
mean the difference between living or dying
the method we used when designing an ai assistant for this task was basically a user-centered design process
we did field work built a predictive model and we constantly evolved the prototype using feedback from pathologists during roughly
a eight month period. following this we performed a larger user study which i'll present later
in order to design a successful human ai interaction we needed a realistic ai model
we collected the training data and trained a pretty standard convolutional neural network
the output of the model is a probability for cancer in each pixel of the image
to get a value for sections we simply take the max value in each section
compared to the state of art we achieved a similar performance
but how good is that compared to pathologists from similar works we expect pathologists to be much
better than our model so while capable using this model without a human in the loop is not an option
so how can we build a user interface that provides value despite the imperfect model
let's first state our design goal. our goal was to improve quality and or speed of reviewing lymph nodes
in order to understand the way we did this let's first look at how a manual review is done
these lymph node images are actually gigapixel sized and navigated using a zoomable user interface
smaller cancers are only visible in high magnification
therefore concluding absence is the most time consuming as you need to search all of the
tissue before deciding that there is nothing in addition most lymph node sections are negative
so again how can we exploit the imperfect model to improve this search task? Let me describe the
Let me describe the overall strategy that we used.
So when you have a binary model you can decide whether you want
to be specific or sensitive we select the threshold in order to be 100% sensitive
this leaves a lot of work with rejecting false positives to the pathologist.
the benefit is that when all regions have been rejected you can conclude that the section is normal
without doing that systematic search
the challenges with that approach
is that the review of potential regions needs to be very fast to compete with manual review
and we need to be careful not to bias decisions additionally the user needs to trust the system
in order to not perform a systematic review after reviewing the suggested regions
so let's look at the user interface we designed and how we handle these specific challenges
let's start with an overview and then i'll go into the challenges
to the left is an overview of all the sections in a case with four suggested regions
per section it's always a fixed number and i'll go into why soon
to the right is a zoomable user interface. again showing the four regions in a low contrast color
in the top left the positive and negative findings are counted and this count is what
the pathologist needs to put in their final report after going through the entire case
for being fast we borrowed the rsvp technique and automated navigation
the only thing the user needs to do is indicate whether they see cancer or not and navigation follows
we know that ai advice can cause confirmation bias so this is why there are no labels or color coding
indicating whether the model believes the region is positive or not
additionally always showing a fixed number of regions is also to avoid bias
for each section we select the top candidate regions without applying any kind of threshold
for regions close to zero
this was because we didn't want to implicitly bias the user through the non-presence of regions
why four? well that was the number that yielded a hundred percent sensitivity on our test set
since we're using a hundred percent sensitive model the idea is that once you checked four
regions there is no need for systematic search however users might not trust us on this
and that is fine and we should design for that we did a couple of things first manual navigation
is always available and you can just ignore the regions if you want to. as an alternative you can
go through the case once and then increase the sensitivity to do four more regions per
negative or preliminary negative section finally while those alternative options are available
we designed the flow so if you just go with the flow you will always use the recommended strategy
for our evaluation we wanted to know the effects on quality and speed of
using our interface compared to the manual unassisted task. we had six pathologists
review sections with and without ai support in a within subject design using a washout period
after the experiment we conducted interviews in order to learn about the user experience
i'll be quite brief with results and you can find more details in the paper. in terms of quality the
conditions were roughly equal with a slightly higher sensitivity in the assisted condition
using our system was significantly faster than the manual condition. for the average case the speed
up corresponds to completing the task in six minutes instead of 17 minutes for one case
one pretty serious drawback was that users struggled with identifying what might be an indication that
the model had malfunctioned. they said that certain region selections were suspicious
but they were uncertain on whether they should act on this and revert to manual or not, was this a
normal thing or was this a malfunction? in practice this ambiguity would need to be resolved
finally, users established trust progressively many mentioned that they were initially skeptic
to the system but after going through negatives again and again with increased sensitivity they
never found anything and eventually stopped increasing the sensitivity
i believe that providing such a mechanism so users can balance the speed confidence trade-off
is an important idea that could be refined further
in conclusion i presented rapid assistive visual search
which is a human ai user interface that manages to increase the speed of reviewing lymph
nodes despite using an ai model that performs worse than pathologists
the design effort was
not guided by recipes of do's and don'ts but instead, thoughtful consideration of
pathologists' work, human factors and the strengths and limitations of our predictive model
the datasets used are available for extending our work
thank you