Artificial Intelligence (AI) has emerged as a transformative technology in healthcare delivery and drug discovery. The technology can expedite diagnosis processes, enhance prescription accuracy, and enable personalized care. In pharmacology, AI’s data processing abilities have demonstrated the potential to accelerate drug discovery and lower costs.
Although AI is a disruptive technology poised to positively transform the healthcare sector, its efficacy in the field depends on the quality of data used. According to research and sentiment from leading professionals in the medicine and tech sectors, AI models trained by erroneous or biased data can lead to development of ineffective medicine or therapies that only work in certain populations, exacerbating existing inequalities in healthcare delivery. For example, an AI model may develop a type 2 diabetes treatment plan in record time, only for post-market data to show that the intervention does not work for patients with comorbidities.
To ensure the safe use of AI in healthcare, training data is required to be complete, representative, diverse, clean, up-to-date, and relevant. This paper serves as an end-to-end guide, detailing four domains for healthcare data professionals to enhance system quality. It covers:
- Ensuring diversity and representation in your training data.
- Documenting data provenance and traceability.
- Implementing effective data cleaning processes.
- Maintaining data compliance and security.