How artificial intelligence can revolutionise the course of precision medicine

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 How artificial intelligence can revolutionise the course of precision medicine
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Highlights

  • Artificial Intelligence (AI) can change the course of patient care and internal administrative procedures.
  • Precision medicine refers to customised healthcare interventions for specific patients or patient groups.
  • Complex algorithms, digital health apps, and omics-based testing are three categories of AI in precision medicine.

Artificial Intelligence (AI) in healthcare refers to application of machine learning (ML), algorithms and other cognitive technologies in the medical field. In simple parlance, AI is imitation of human cognition by computers and other machines, enabling them to learn, think, make decisions, or take actions.

AI and allied technologies are becoming increasingly commonplace in different sectors, and they have begun to be used in healthcare. This technology could change many aspects of patient care and internal administrative procedures at payer, provider, and pharmaceutical organisations.

AI in medicine: virtual and physical

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Numerous studies have already shown that AI can do important healthcare jobs, including disease diagnosis, as well as or even better than humans.

Use of AI in precision medicine

Based on a patient's disease profile, diagnostic or prognostic data, or response to treatment, precision medicine offers the opportunity to customise healthcare interventions for specific patients or patient groups. At every level of a patient's medical care, precision medicine seeks to employ individual biology rather than population biology. This individual application entails gathering patient information, such as genetic data, physiological monitoring data, or EMR data, and then customising patient care based on cutting-edge models.

In particular, scientists now have an unrivalled potential to create novel phenotypes from real-world clinical and biomarker data, thanks to the convergence of high-throughput genotyping and global adoption of electronic health records (EHRs). These phenotypes may justify the need for other medications or help with the diagnosis of disease variations when paired with information from the EHR.

Complex algorithms, digital health apps, and ‘omics’-based testing are three categories into which many AI activities in precision medicine can be categorised.

Complex algorithms: Large datasets such as genetic information, EHRs and demographic data are combined with machine learning algorithms. This offers prognosis prediction and optimal treatment strategy.

Digital health applications: Healthcare apps track patient data such as food intake, health vital inputs from wearables, mobile sensors, etc. Some apps employ machine learning algorithms to find developments in the data, make better predictions and offer personalised treatment guidance.

Omics-based tests: A population pool is used to extract genetic information and is then combined with machine learning algorithms. This helps in finding correlations and interpreting the treatment responses for an individual patient. Additionally, biomarkers such as protein expression, metabolic profile and gut microbiome are employed along with machine learning in order to enable personalised treatments.

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