AI Applications In Healthcare

The benefits of exploiting AI applications for healthcare purposes are substantial. AI is ready to support the healthcare system with a variety of tasks including administrative workflow, clinical documentation and patient outreach; as well as highly specialised support such as in image analysis, medical device automation, patient monitoring, dosage error reduction, and cybersecurity.

Precision Medicine

Precision medicine, sometimes referred to as "personalised medicine", is an innovative approach to tailoring disease prevention and treatment in accordance with a patient’s biological makeup. The tailor-made treatment will take into consideration the genomic variations as well as contributing factors to treatment such as age, gender, geography, race, family history, immune profile, metabolic profile, microbiome and environment vulnerability.

Next Generation Sequencing (NGS) tests are capable of rapidly identifying large sections of a person's genome and are important advances in the clinical applications of precision medicine.  Patients, physicians and researchers can use these tests to find genetic variants that help them diagnose, treat and understand more about human disease. Hence, the aim of precision medicine is to use individual biology rather than population biology at all stages of a patient’s medical journey.

Overall, there are three different types of precision medicine:

a) Complex algorithms: Machine learning algorithms are used with large datasets such as genetic information, demographic data or electronic health records to provide prediction of prognosis and optimal treatment strategy.

b) Digital health applications: Healthcare apps record and process data added by patients such as diet, emotional state or activity, and health monitoring data from wearables and mobile sensors.. Some of these apps fall under precision medicine and use machine learning algorithms to find trends in the data to make better predictions and give precise personalised treatment advice.

c) Omics-based tests: Genetic information from a population pool is used with machine learning algorithms to find correlations and predict treatment responses for the individual patient. In addition to genetic information, other biomarkers such as protein expression, gut microbiome and metabolic profile are also employed with machine learning to enable advanced personalised treatments.

Genetics-Based Solutions

It is believed that within the next decade a large part of the global population will be offered full genome sequencing either at birth or in adult life. Interfacing the genomic and phenotype information is still ongoing. However, the current clinical system would need some redesign to be able to analyse such comprehensive genomics data. Many inherited diseases result in symptoms without a specific diagnosis, and interpreting whole genome data is still challenging due to the numerous genetic profiles. Precision medicine can develop methods that improve and speed up the identification of genetic mutations based on full genome sequencing with the aid of AI.