# Learning from the experts: From expert systems to machine-learned diagnosis models

arXiv: https://arxiv.org/abs/1804.08033 · HTML: https://mlsanity.com/paper/1804.08033/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Apr 2018.

## Built on by (most cited first, 3 of 3)

- [MEDCOD: A Medically-Accurate, Emotive, Diverse, and Controllable Dialog System](https://mlsanity.com/paper/2111.09381/) (2021)
- [Rare Disease Differential Diagnosis with Large Language Models at Scale: From Abdominal Actinomycosis to Wilson's Disease](https://mlsanity.com/paper/2502.15069/) (2025)
- [Generative artificial intelligence in dentistry: Current approaches and future challenges](https://mlsanity.com/paper/2407.17532/) (2024)

## Abstract (arXiv)

Expert diagnostic support systems have been extensively studied. The practical applications of these systems in real-world scenarios have been somewhat limited due to well-understood shortcomings, such as lack of extensibility. More recently, machine-learned models for medical diagnosis have gained momentum, since they can learn and generalize patterns found in very large datasets like electronic health records. These models also have shortcomings - in particular, there is no easy way to incorporate prior knowledge from existing literature or experts. In this paper, we present a method to merge both approaches by using expert systems as generative models that create simulated data on which models can be learned. We demonstrate that such a learned model not only preserves the original properties of the expert systems but also addresses some of their limitations. Furthermore, we show how this approach can also be used as the starting point to combine expert knowledge with knowledge extracted from other data sources, such as electronic health records.