Towards Responsible Medical Diagnostics Recommendation Systems

TL;DR

This paper discusses designing responsible medical diagnostic recommender systems that prioritize accountability, safety, and fairness to enhance healthcare data management and support medical staff.

Contribution

It presents a practical framework for developing responsible recommender systems in healthcare, emphasizing ethical design considerations.

Findings

Highlights importance of accountability, safety, and fairness in medical recommender systems.

Provides design criteria and discussion points for responsible AI in healthcare.

Illustrates with a practical example of system design.

Abstract

The early development and deployment of hospital and healthcare information systems have encouraged the ongoing digitization of processes in hospitals. Many of these processes, which previously required paperwork and telephone arrangements, are now integrated into IT solutions and require physicians and medical staff to interact with appropriate interfaces and tools. Although this shift to digital data management and process support has benefited patient care in many ways, it requires physicians to accurately capture all relevant information digitally for billing and documentation purposes, which takes a lot of time away from actual patient care work. However, systematic collection of healthcare data over a long period of time offers opportunities to improve this process and support medical staff by introducing recommender systems. Based on a practical working example, in this position paper, we will outline the design of a responsible recommender system in the medical context from a technical, application driven perspective and discuss potential design choices and criteria with a specific focus on accountability, safety, and fairness.