One emerging field that holds promise for the future is preemptive medicine. This novel concept aims to delay or prevent the onset of chronic diseases through the use of Artificial Intelligence (AI) techniques, genomic analysis, and environmental interaction data. In this article, we will explore the role of AI in preemptive medicine and its impact on population health and health economics.
Advancements in Preemptive Medicine
Preemptive medicine combines AI technology with genomic analysis and environmental data to identify individuals at risk of developing chronic diseases. By analysing vast amounts of data, AI algorithms can detect patterns and predict disease progression with high accuracy. AI can analyse genetic markers to identify individuals with a higher likelihood of developing conditions. These can be diabetes, hypertension, cancer, or dementia.
AI and machine learning (ML) technologies have been effectively applied in parsing vast amounts of data produced by genomic technologies. Deep-coverage whole-genome sequencing of 8,392 individuals from European and African backgrounds helped pinpoint single-nucleotide variants and copy-number variations in Lipoprotein (a). This study discovered that certain LPA risk genotypes pose a higher relative risk for developing cardiovascular diseases compared to the direct measurement of Lipoprotein (a) levels.
These advancements in preemptive medicine can inform healthcare by shifting the focus from reactive treatment to proactive prevention. By identifying at-risk individuals early on, healthcare providers can intervene with targeted interventions and lifestyle modifications. This improves patient outcomes and reduces the burden on healthcare systems by minimising resource use and allocation.
Figure 1: A simplified schematic representation of one of the several machine learning (ML) technologies that can be utilised for diagnosis and management of endocrine disorders.
Challenges to Put into Practice
AI holds great promise in preemptive medicine, there is a need to address several challenges and ethical considerations. The longitudinal nature of variations in human disease, heterogeneity of healthcare data, and personal data confidentiality pose challenges for AI techniques. The need for informed consent from patients, supportive policies, efficient business models, and unpredictable reimbursement further complicate the integration of AI in healthcare.
Furthermore, interpreting “digital biomarkers” obtained through AI analysis is not always straightforward. Certain AI algorithms may outperform older techniques in specific population cohorts. Their implementation across diverse populations may not necessarily result in better diagnoses or outcomes. There is a risk of over-diagnosis and over-treatment in certain patient cohorts. This highlights the importance of careful interpretation and clinical judgment.
Conclusion
AI can elevate preemptive medicine and improve population health outcomes. By leveraging AI techniques, genomic analysis, and environmental data, healthcare providers can identify individuals at risk of developing chronic diseases. This will aid in improving health outcomes by designing interventions.
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