AI to Drive the Adoption of Personalized Healthcare

There are 2 things in this world that money cannot buy; love, and health. While dating app algorithms do not and will never act in your best interest, we can at least say that we are well on the way to improve every person’s health through the use of AI for more specialized, precise, and personalized healthcare.

We can reliably measure the advancement of a species by how well they keep their fellow kith and kin healthy and living. The first advanced humans were identified through healed bones, which meant that a community cared for and healed their members.

Today, we are finding more ways in which to extend the gift of healthcare to many more people and do so more efficiently. Our artificially intelligent autonomous helpers and machine-learning assistants are the latest recruits to our eternal struggle against natures and entropy.

AI in Personalized Healthcare

Individualized, tailored experiences have become the standard in today’s world. Music we listen to, TV episodes we watch, and purchases we make are frequently suggestions based on data acquired about us, including our purchasing and streaming histories. We frequently take for granted our capacity to recognize and comprehend our own goals and requirements.

When it comes to monitoring our health and how we care for ourselves, the situation is similar. Wearable technologies such as smart watches and fitness trackers are becoming increasingly popular, allowing us to measure our health in numbers and stats such as heart rate, calories burnt, and hours of sleep. This is all critical information that we need to be more effective at utilizing to inform how we eat, sleep, and exercise.

In addition to how we monitor our own health, the pharmaceutical industry is looking at this data to adopt a more customized approach in formulating cures and treatments, in order to precisely forecast and control what health concerns may occur among particular patient groups. Despite advances in customized medicine, there is still work to be done until healthcare is suited to each of our needs. To do so, we require tremendous volumes of data and insights on various individuals to generate fully individualized medication and treatment, yet these massive datasets are frequently impossible to acquire or evaluate manually.

When you combine this issue with the complexity of the human body, you have a very inadequate grasp of how the human body’s processes react to and cope with various disorders. This is where smart technology, such as machine learning, can assist manage massive amounts of data.

Fortunately, we live in an era where this technology is readily available. We only need to apply it correctly in order to fully benefit from its utilization and the insights it may bring with electronic medical records, perhaps saving lives and revolutionizing healthcare as we know it.

Data-Powered Personalized Healthcare

Although we haven’t there yet, genuinely tailored medicine on a large scale is only a few years away, and AI technology will be a significant enabler. The quantity of data we gather is expanding dramatically, with IDC research forecasting that the global datasphere will rise from 33 zettabytes in 2018 to 175 zettabytes by 2025.

This massive dataset, which contains genetic information as well as electronic health records such as medical history and allergies, has enabled physicians to examine individual individuals and their diseases in ways they couldn’t previously. They may now use machine learning to detect trends, patterns, and anomalies in data, allowing professionals to make more educated judgments.

The use of data analytics is also vital for customizing clinical trials and experiences for people who participate in them. Many trials are still conducted by administering the same medicine or therapy to a large number of people and using a statistical technique to see how the majority reacts. This is not a ‘personalized’ strategy because each person has a unique genetic make-up and biomarkers. As a result, medication efficacy varies from person to person, which should be represented in clinical trial design.

Building a Clear View of Every Patient

Because everyone of us has a unique variant of the human genome, understanding whether gene mutations or variances may cause various illnesses can help physicians forecast and prevent health conditions from emerging. This understanding allows for more thorough illness management plans to be developed to reduce hazards when they do appear.

Cancer therapies are one example of early intervention in effect. Until recently, individuals with the same type and stage of cancer were usually given the same therapy. However, we now know that different people may suffer distinct genetic alterations in their cancer cells, and/or their genetics may influence how their body responds to the disease; both of these variables will influence how their cancer grows. Precision medicine and targeted therapies may be created and utilized to help anticipate which treatments a patient’s tumor is most likely to react to with a better knowledge of disease progression through the study of patient data.

Building a comprehensive image of each patient is critical to provide individualized care to this level. To accomplish so, we must collect data from health records and lifestyle habits from many sources on a regular basis and combine it into a single comprehensive perspective. This data is critical for understanding and analyzing each patient’s demands, which may be used to drive both medication development and the sort of treatment that a patient receives. These massive databases include critical insights to how chronic diseases appear, allowing drugs and physicians to uncover correlations between lifestyle and sickness development and enable earlier intervention.

However, the capacity to do so is dependent on the ability to gather, map, and evaluate insights from massive volumes of data from various sources – a process that cannot be done manually. To put the amount of power required to manually analyze the data into perspective, modeling a single human’s DNA would require the equivalent of the sun’s output power for a whole week. This is clearly not a sustainable paradigm that will allow us to personalize healthcare at scale.

AI in Personalized Healthcare

This is where AI comes into its own, and it may give significant benefits in addressing the four primary difficulties that healthcare practitioners confront when dealing with large data: velocity, volume, diversity, and validity. In fact, over 80% of respondents in a recent Oracle Health Sciences study said they expect AI and machine learning to improve specific therapy suggestions.

The advantages are obvious. Pharmaceutical firms can gather, store, and analyze massive data sets more faster with AI and machine learning capabilities than with conventional methods. This allows them to do research faster, based on genetic variation data from a large number of patients, and produce tailored therapeutics faster. Furthermore, it offers a clearer picture of how small, specific groups of patients with certain shared traits respond to therapies, and therefore how to accurately map the correct amounts and dosages of medicines to administer to people.

As a result, professionals’ ability to offer high-quality patient care is enhanced. In an ideal world, we would like to avoid sickness. We may introduce preventative measures and therapies considerably earlier, often even before a patient begins to show symptoms, since we have greater understanding about why, how, and in which people illnesses arise.

Closing Thoughts

Personalized medicine has the potential to enhance and possibly save many people’s lives, and artificial intelligence and machine learning are driving forces behind future advances. We may then begin to enjoy the benefits of other novel technologies that are coming in the sector, such as employing 3D printing to deliver a personalized dose of a treatment to each patient, by leveraging their power in conjunction with cloud computer processing.

As wearable technologies and IoT devices become more popular, with an estimated 1.3 billion IoT subscriptions by 2023 and 26.6 billion IoT devices in use in 2019, the amount of personal data we collect on ourselves will only increase, creating more opportunities for individualized healthcare experiences for patients.

There are numerous obstacles ahead for customized medicine, as well as a long way to go before it is mastered. However, as AI becomes more extensively used in medicine, a future of practical, effective, and personalized healthcare will undoubtedly become a reality.

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