# The role and utility of artificial intelligence and machine learning for diagnostic prediction in general practice

## TL;DR

This paper explores how artificial intelligence and machine learning can improve diagnostic predictions in general practice, while addressing challenges like interpretability and data quality.

## Contribution

The paper provides a comparative analysis of traditional statistical methods and AI/ML approaches in diagnostic prediction and offers practical recommendations for their adoption in general practice.

## Key findings

- Machine learning can better handle complex datasets from electronic health records compared to traditional statistical methods.
- Key challenges for AI/ML adoption include interpretability, data quality, and clinical relevance.
- Collaborative development with GPs is essential for successful implementation of AI/ML tools in general practice.

## Abstract

Diagnostic prediction models are commonly used in general practice to support clinical decision-making. Traditionally, these models have been developed using statistical methods such as logistic regression. While these approaches have proven useful, they often produce average risk estimates that may not fully account for the complexity of individual patients. In recent years, the use of machine learning (ML), a subfield of artificial intelligence (AI), has grown in healthcare. We examine the similarities and differences between traditional statistical methods and AI/ML approaches for diagnostic prediction in general practice. Using examples from daily practice, we explore how ML techniques can add value, particularly in handling large, complex datasets such as those derived from electronic health records. We also discuss key challenges that hinder the adoption of AI/ML in general practice, including interpretability, data quality, external validation, clinical relevance, implementation and legal issues, and practical usability. We provide recommendations to overcome these challenges. The potential of AI/ML can only be realised if tools are developed collaboratively with GPs, focused on real-world clinical problems, and rigorously validated in practice settings. GP associations, GPs, patients, and primary care scientists should take an active role in the development, validation, and implementation of AI/ML-based diagnostic prediction tools for general practice.