Case study.
Balancing accuracy and interpretability in AI metastasis journey in healthcare diagnostics.
AI model selection is crucial for achieving the delicate balance between accuracy and interpretability,
influencing the effectiveness and acceptance of AI systems.
In this case study, we explore the experiences of Medistat, a healthcare analytics company, as it
tackles the interplay between these two critical dimensions while developing an AI diagnostic tool.
Doctor Emily Carter, chief data scientist at Medistat, faces a pivotal decision her team is developing
an AI model to predict the likelihood of patients developing type two diabetes.
The goal is to provide early diagnosis and intervention, potentially improving patient outcomes.
The challenge is to choose between a highly accurate but opaque deep learning model and a more interpretable
but slightly less accurate logistic regression model.
As the team analyzes data from various electronic health records, they find that the deep learning
model achieves a 95% accuracy rate in predicting diabetes onset, while the logistic regression model
achieves an 88% accuracy rate.
Doctor Carter must decide which model to recommend for deployment.
This decision brings up the first critical question how important is the marginal increase in accuracy
compared to the need for interpretability in the health care context?
The team knows that health care professionals, including doctors and nurses, must understand how AI
models make predictions to trust and effectively use them.
This requirement leads to another question.
Can the complex deep learning model be simplified or made interpretable enough to meet regulatory and
ethical standards?
The regulatory environment in health care demands transparency, especially when decisions impact patient
care.
Doctor Carter ponders if tools like local interpretable, model agnostic explanations or Shapley additive
explanations could be used to provide sufficient transparency for the deep learning model.
While considering this, another aspect surfaces the potential biases within the model.
The team needs to ensure that the chosen model does not perpetuate biases inherent in the training data,
prompting the question what measures can be taken to identify and mitigate biases in the chosen AI model?
Doctor Carter recalls studies showing that models trained on biased data can lead to unfair treatment
of certain patient groups, affecting their trust, and the overall efficacy of the AI system.
To explore these concerns, the team deploys both models in a pilot phase across two hospitals.
They collect feedback from clinicians and patients, revealing that while the deep learning model's
predictions are highly accurate, clinicians are hesitant to act on them due to a lack of clear rationale
behind the predictions.
This feedback emphasizes the importance of transparency and leads to another key question how can user
feedback be effectively incorporated into the model selection process to ensure both accuracy and interpretability
are adequately balanced?
In parallel, the team also evaluates the performance of the logistic regression model in real world
scenarios.
Although less accurate, it provides clear insights into how different features such as age, BMI,
and blood sugar levels contribute to the prediction.
This clarity boosts clinician confidence, supporting patient trust and adherence to recommended interventions.
The pilot results suggest that despite the lower accuracy, the logistic regression model might be more
practical for real world application in this context.
To further analyze the tradeoffs, the team examines cases where the logistic regression model made
incorrect predictions.
They find that in many instances, the errors were due to the model's inability to capture complex interactions
between features.
This finding raises another important question can the accuracy of the logistic regression model be
improved by incorporating non-linear relationships without significantly compromising interpretability?
Doctor Carter considers generalized additive models, which extend logistic regression by allowing non-linear
relationships while maintaining interpretability.
By experimenting with Gams, the team achieves an accuracy rate of 92%, a notable improvement over
the logistic regression model.
The Gams also provide clearer insights into the influence of each feature, striking a better balance
between accuracy and interpretability.
With these findings, Doctor Carter prepares a detailed report for the board, highlighting the trade
offs and recommending the adoption of Gams for their AI diagnostic tool.
The board, comprising clinicians, data scientists and AI governance professionals, reviews the report.
They deliberate on another essential question what additional steps should be taken to ensure the selected
model remains transparent and robust over time?
The discussion centers around implementing continuous monitoring and updating of the model to ensure
it adapts to new data and maintains fairness.
They agreed to set up a dedicated team for ongoing evaluation, and to use model agnostic interpretability
tools to periodically reassess the model's decision making process.
This proactive approach ensures the model remains both accurate and interpretable, aligning with regulatory
standards and ethical considerations.
In conclusion, Metastatic Journey underscores the complex interplay between accuracy and interpretability
in AI model selection by piloting both the deep learning and logistic regression models.
They gather valuable insights that inform their decision to adopt generalized additive models.
This choice offers a balanced solution enhancing patient outcomes while fostering trust and compliance
among health care professionals.
The analysis reveals that prioritizing interpretability in high stakes domains like healthcare is crucial
for gaining user trust and meeting regulatory requirements.
Techniques such as lime and shape, combined with inherently interpretable models like Gams, provide
practical solutions to address these challenges.
Continuous monitoring and iterative improvement further ensure that the AI system remains transparent,
fair, and effective over time.
By reflecting on the thought provoking questions posed throughout the process, students can better
understand how to navigate the trade offs between accuracy and interpretability in AI model selection.
This case study illustrates the importance of considering context, regulatory requirements, and stakeholder
needs when developing AI systems, ultimately guiding professionals toward responsible and impactful
AI deployment.