arXiv:2605.22243v1 Announce Type: new Abstract: Predictive modelling is important for health data analysis and data-driven clinical decision-making. However, predictive studies are challenging to design optimally by hand when tens or even hundreds of features require selection, transformation, or interaction modelling. While complex machine learning models offer high performance, their "black-box" nature limits the clinical trust, transparency, and interpretability required for decision-making. We developed and evaluated an Exploratory AI Recommender that provides data-driven recommendations t
The proliferation of complex AI models in critical applications like healthcare necessitates methods to ensure transparency and trustworthiness, making explainable AI crucial at this stage of AI development.
This development addresses the core limitation of 'black-box' AI in sensitive domains, potentially unlocking broader adoption and regulatory acceptance for advanced analytics in clinical decision-making.
The design of high-dimensional predictive studies can now be augmented with AI-driven recommendations and explanations, moving beyond purely human-driven or opaque model selection processes.
- · Healthcare sector
- · Patients
- · AI ethics and governance companies
- · Data scientists in medical research
- · Providers of non-interpretable AI solutions
- · Manual data analysis methodologies
Increased trust and adoption of AI in clinical decision-making and health data analysis.
Development of new regulatory frameworks and industry standards specifically for explainable AI in healthcare.
Broader societal acceptance of AI in other high-stakes domains, driven by success stories in medicine.
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Read at arXiv cs.LG