Use of AI/ML in Precision Medicine: A step in the right direction?

AI/ML is no longer a far-fetched fantasy that only enterprises like Microsoft, Google, and Apple talk about. It has become much more accessible and a part of our daily lives in our business processes, customer experiences and other such aspects. One of the most prominent and early adapters of AI was seen in the healthcare industry. Some articles and studies suggest AI was used in healthcare all the way back in 1970s to help with biomedical problems.

In today’s time we witness AI being used for multiple use cases such as Radiology, Screening, Psychiatry, Disease Diagnosis, Telemedicine and Primary Care. It is also helping many hospitals take care of the administrative tasks and keeping proper records of all patient’s medical histories. You can hire Java developers or Python developers for achieving similar success in such use cases, since those technologies are the most promising forerunners in the space of AI/ML.

While use of AI in Healthcare is only going to expand and is already at a commendable level, we might have just barely scratched the surface.

Yes, what if AI/ML could be so deeply integrate with our personalized healthcare system, that it is able to diagnose, detect symptoms, understand individual body composition or genetic sequence to provide precision medicine in accordance to all these aspects?

Does this raise social, security and ethical concerns? Or is it going to revolutionize healthcare and is a blessing to the society?

There’s a lot to unpack here, and we will do exactly that today.

What is Precision Medicine and how AI/ML can contribute to it?

Precision Medicine also known as Personalized Medicine is an approach towards healthcare that looks at the genetics, environment and lifestyle of an individual to determine and select a treatment that works best for them. It opens up the scope of diagnosis beyond going through common symptoms and their plausible cures as a generalized guideline, which often has a hit to miss ratio as each individual is different and reacts differently to the same medication or treatment.

AI can improve the capabilities of precision medicine allowing for improving predicted response or risk of a disease to handle it early on and get the right treatment underway.

AI/ML Precision Medicine Use Cases

Here are the important ways AI/ML improve Precision Medicine –

1. Lifestyle Recommendations: A chatbot development company usually sells their chatbot solutions to be AI-powered these days. Such AI based chatbots can help patients with personalized recommendations on their exercise, diet and daily routine based on the patient’s biometrics and medical history. This can enable patients to be more in control of their health and take better heath-centric decisions.
2. Digital Patient Monitoring through IoT and Wearable Technologies: By developing AI-driven wearable apps that can make use of IoT sensors in the user’s smart devices such as smartwatches, developers can flag changes that alert their doctors and/or family members for the need of a medical intervention when the sensors detect abnormalities.
3. Analysing Genetic Data: The biggest potential that makes AI in precision medicine a thing to look forward to is utilizing AI/ML to analyse a patient’s genetic structure to determine the susceptibility to certain diseases and conditions. This can enable health practitioners to develop a more targeted prevention and treatment cure.
4. Drug Discovery: AI can run molecular stimulations on genetic profiles to see how patients with a particular genetic profile react or respond to the newly introduced medication. This can help fasten the drug development and testing process while reducing the need to have test subjects risk their lives or health in order to witness such impacts. On a deeper level, some companies might be able to create AI based digital twin of an individual where the doctor can practically administer their theoretical cure and only move to the patient once the cure is stable and effective.
5. AI and Genomic Medicine: Genomic medicine refers to sequencing an individual’s genomes for identifying genetic mutations or variations that could predispose them to certain diseases. With machine learning, doctors can analyse a patient’s entire genome sequence for surfacing any concerning mutations. This can be used for early-stage identification in serious situations such as breast cancer, which allows preventive screening.

What are the benefits of using AI/ML in Precision Medicine?

As you can see AI/ML can greatly improve precision medicine practices by being useful in different segments. Here are some of the top benefits that can be achieved with AI/ML in precision medicine –

Earlier Disease Detection and Prevention

AI can analyse genetic data and medical images to identify risks and biomarkers for diseases such as cancer. This can help screen, prevent and treat the symptoms before they manifest more seriously.

Customized and Personalized Treatment Plan

AI for personal medicine will asses the health status of an individual on the basis of their genes, lifestyle, medical history and more. This reduces the chance of trail-and-error prescribing while reducing the overall time required for the patient to recover as they would be getting the most accurate medicines as per their body type and other such factors.

Democratized Access to Cutting Edge Tech

AI tools with help of IoT implementation can help bring the benefit of genomic analysis and personalized care to more end users at affordable and scalable pricing.

Continuous Improvement in Care

The more data you enter in such AI/ML systems the better it optimizes predictive diagnosis, treatment recommendations and risk assessments.

AI/ML in Personalized Medicine — Economical Impact

Although healthcare costs can significantly reduce from AI driven preventive care and early intervention, while using lesser resources, all the technology that goes behind achieving this (genomic sequencing, AI systems and data storage) are still expensive operations to develop and scale.

If AI/ML based precision medicine is still practiced in the market, it might be super-expensive at first which could create healthcare access disparity among different economic classes, but with time and government interventions, prices can eventually be brought down, making it more widely available.

Adding new workflows and expertise to leverage AI with genomic data might lead to training and hiring cost hike for healthcare systems, but it could also create new job opportunities.

Challenges surrounding successful implementation of AI

The use of AI/ML in precision medicine holds tremendous potential and promise as we can see above. However, apart from technical aspects of how to implement AI/ML in personal medicine, there are other more serious challenges and concerns that need to be addressed before we can start using AI/ML in healthcare to its full potential. Here are some of the biggest challenges –

Social Concerns regarding AI/ML in Personalized Medicine

There are many social concerns that can be raised when talking about implementing AI/ML in personalized medicine on a global level. These considerations will need to be kept in mind before delivering a promised AI/ML model in the market and making it available to public at large –

1. Unauthorized Use of Personal Data: Such massive data collection requirement of individual’s health profile and genomic data could always invite exploiters and other unauthorized users like hackers, advertisers, employers and insurers to misuse this data against an individual. Strict government rules and policies are needed to avoid such practices.
2. Calculation Inaccuracies: Limited dataset training could lead to AI models providing biased and inaccurate outcomes, giving room to prejudices to lead the algorithm’s logical thinking rather than key factors.
3. Economic and Social Disparities: Economic and social disparities can arise due to the unaffordable pricing of AI based personalized medicine which would only allow the most privileged group of people to leverage its benefits. Hence, such medical practices should be made available only after a thorough discussion on proactive policies to ensure such civil unrest doesn’t arise.

Ethical concerns regarding AI/ML in Personalized Medicine

One of the biggest challenges of where to draw the line with AI/ML arrives from ethical considerations.

1. Patient Consent and Knowledge: Since using AI/ML in Personalized Medicine requires sensitive and personal information of the patients, they should be taken in trust and explained how their personal health information will be used. They should then be asked for their consent for any further examination and also provide an option to participate in research that can help advance AI capabilities.
2. Replacing Doctors vs Aiding Doctors: AI irrespective of how accurate should be looked at as instrumental tool to help clinicians to augment human intelligence rather than entirely replace it. AI can fault at complex situations leading to considerably serious miscalculations for which a human doctor should always be appointed to supervise all operations and decisions.
3. Job Impact: By the time AI gets heavily integrated in regular healthcare systems, the workforce can adapt to it, resulting in lesser impact on existing jobs while creating new ones. However, to ensure these proactive policies would still be instrumental.
4. Regulations and Policies: AI can easily be misused which could lead to some serious medical accidents, distrust amongst the patients and other such situations for which there should be right regulations and policies in place when needed. There should also be policies for re-training programs if they don’t perform as intended.

Final Words

AI/ML in personalized medicine is as promising as it is concerning. With help of a reliable and experienced Healthcare app development company, you can iron out the uncertainties and focus on how you can leverage the platform for improving the reach and efficiency of your healthcare services.

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