# On the practice of classification learning for clinical diagnosis and therapy advice in oncology

**Authors:** Flavio S Correa da Silva, Frederico P Costa, Antonio F Iemma  
**arXiv:** [1811.04854](https://arxiv.org/abs/1811.04854)  
**Date:** 2018-11-12  
**Summary page:** https://www.summarizepaper.com/en/paper/1811.04854v1/

## Key points

- Authors explore the relationship between AI and Medicine, specifically machine learning techniques
- They highlight critical points that hinder practical use of machine learning for clinical diagnosis and therapy advice
- Oncology is studied as it provides strong evidence for these critical points
- Objective is to develop successful systems aiding in clinical diagnosis and therapy advice

## Summary

In their work titled "On the Practice of Classification Learning for Clinical Diagnosis and Therapy Advice in Oncology," authors Flavio S Correa da Silva, Frederico P Costa, and Antonio F Iemma explore the relationship between Artificial Intelligence (AI) and Medicine, particularly focusing on machine learning techniques. They highlight some critical points that may hinder the practical use of machine learning for clinical diagnosis and therapy advice. To support their arguments, the authors emphasize the importance of studying oncology as it provides strong evidence for these critical points. By examining this existing relationship between AI and medicine with regards to machine learning applications, they aim to develop successful systems that can aid in clinical diagnosis and therapy advice. This expanded summary provides more context about the authors' objectives and their focus on oncology as a key area for their research.