To effectively address health equity, AI models must incorporate diverse datasets beyond traditional healthcare data, including socioeconomic factors from public and private sources. This approach can help identify barriers like transportation access that impact healthcare outcomes. However, healthcare organizations must implement AI cautiously to avoid exacerbating health disparities. Ensuring AI's effectiveness involves using complete datasets and validating AI results through additional methods like surveys. As AI adoption grows, critical evaluation and a measured approach are essential to minimize errors and maximize benefits.
Key takeaways:
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- Generative artificial intelligence (GenAI) is increasingly being applied in healthcare, particularly in identifying social determinants of health (SDOH) from clinician's notes.
- AI in healthcare faces challenges such as producing "hallucinations" due to inadequate or biased training data, which can lead to serious risks in medical diagnoses.
- Effective AI requires accurate and complete training data, and relying solely on patient-supplied information can result in flawed insights.
- To address health equity, AI models need to incorporate nontraditional datasets from outside the healthcare industry, such as socioeconomic data, to better understand SDOH.