New-age technologies like AI (Artificial Intelligence) and ML (Machine Learning) have left their impact on many industry sectors. The Healthcare industry is one sector leveraging the power of new-age technologies. The use of artificial intelligence in healthcare has already started on a mass scale. AI is used for many healthcare activities like surgical practice, clinical surgery, and precision medicine. Healthcare organizations are compelled to have a dedicated IT infrastructure in 2022. Since healthcare organizations are using software systems, applications, devices, and more for offering services, a dedicated IT infrastructure is needed. AI in healthcare IT solutions can drive productivity and service availability. Read on to know the role of AI solutions in precision medicine.

Understanding medicine precision

Before knowing the role of AI, one must be clear about the meaning of precision medicine. Medical care is more interested in offering general solutions. For example, there are generic syrups for cough-related problems. Usually, the manufacturer of cough syrup changes, but the contents remain the same. Consider a cough syrup that contains a controlled narcotic or opioid. A practitioner might recommend the same cough syrup to all the patients. However, some patients can be allergic to certain drugs. For example, opioid allergy is very rare but can be found in some patients. What about the patients that are allergic to certain drugs? Would a doctor still recommend the same medicine to the patient even after knowing about the allergy?

Generic medicines aren’t fit for everybody due to allergies, immunity, and many other factors. For the same reason, there was a need for personalized medicines for each individual. Customized medicines are offered according to the preferences of any particular individual. Over the years, healthcare service offerings experienced a paradigm shift toward precision medicine. The branch of precision medicine was formed to offer tailormade treatment to a subgroup of patients. Whenever treatment, medicine, or medical products are tailormade/customized for a specific subgroup of patients, it will be done via precision medicine. Precision medicine strictly opposes the drugs-fits-all model of treatment.

Challenges with precision medicine

Precision medicine is a comparatively new branch of healthcare. However, work on precision medicine has been done for many years. It is now that the healthcare sector is reaping the rewards of the work done on precision medicine. However, precision medicine isn’t going well with outdated technology solutions. Several challenges prevent the 100% success of precision medicine. For the same reason, the use of artificial intelligence in healthcare is being promoted.

First, let us see the challenges in the healthcare sector with precision medicine.

• For precision medicine to succeed, a healthcare entity has to access healthcare data from all sources. By collecting healthcare data, we mean data that makes sense. Only after collecting data from all sources, a healthcare organization can identify personalized medicines for a subgroup of patients. Sadly, outdated databases and software solutions cannot collect healthcare data from all sources.

• Let’s say a healthcare entity is successfully collecting data from different sources. How is the healthcare entity planning to analyse the vast amount of data? Analysing large data sets manually will not be feasible for healthcare organizations.

• Healthcare organizations are struggling to develop a comprehensive IT infrastructure for precision medicine. Many software systems within an IT infrastructure work simultaneously. Healthcare organizations seem to have no idea of uplifting uptime and service availability of crucial software systems.

• As we know, huge volumes of data are involved in precision medicine. For efficient exchange of healthcare data, entities need to be interoperable. Outdated software solutions are not helping healthcare entities to become fully interoperable.

• Healthcare organizations often rely on each other for exchanging patient and medical data. However, healthcare data exchange is guided by some rules set by the concerned authorities. Keeping up with the data standards is a massive challenge for study/research related to precision medicine.

• Large data sets are analysed for precision medicine, and similar patterns are detected. Manually, it is not feasible to detect patterns among data sets. Not all technologies work like predictive analytics models to find patterns and generate insights.

Subfields of AI for precision medicine

As discussed above, several challenges prevent the success of precision medicine. The use of artificial intelligence in healthcare can solve precision medicine challenges. AI-led solutions for precision medicine use a mix of new-age technologies to achieve desired results.

Some subfields of AI needed for precision medicine are as follows:

• Medicine precision is all about using AI and ML algorithms appropriately. Machine learning is one subfield of AI used for medicine precision. Apart from genomic sequencing, ML algorithms are used to extract inferences from huge volumes of data. Every moment, a healthcare organization records patient, medicine, and treatment data. ML algorithms can help draw insights from heterogeneous data.

• For medicine precision, an algorithm goes through many documents, papers, and prescriptions. For example, consider an algorithm has to access previous surgeon prescriptions for medicine precision. How will the algorithm understand the prescriptions written in human language? Well, AI-led solutions rely on NLP (Natural Language Processing) to understand our language.

• Is only text content used in the medical sector? Precision medicine requires intrusive image analysis at times. Videos, graphs, and many other types of content are used by predictive analytics models for medicine precision. With the help of CV (Computer Vision), predictive analytics models can access information from images, videos, and other types of content.

• ANNs (Artificial Neural Networks) are used for extracting rich insights from complex data. With the help of historical data, ANNs can find patterns among data sets. With the help of ANN, an algorithm can find the exact location of medicines needed for a subgroup of patients.

Genotypes and phenotypes are taken into consideration for precision medicine. Apart from them, many other disease factors are taken into account for deciding the right set of medicines for a group of patients. With the use of artificial intelligence in healthcare, research costs can be brought down. Medicine precision can be performed quickly with fewer expenses via AI-led solutions. Adopt AI-led solutions for precision medicine in 2022!