The Advent of Personalised Medicine Through Artificial Intelligence
Personalised medicine is the widely growing branch of medicine that utilises the probability of disease and consequently instates preventive measures to either prevent the disease entirely or notably reduce its effects and symptoms on the patient, an innovative concept that relies on the similarly garnering field of medical genomics. This is a significant step forward in medicine, as a failing within the field until recent times has been the inability to tailor to the unique genetic conformation of each patient, otherwise treating each patient as chemically identical which simply doesn’t confer the same consistency to each patient in response to drugs in terms of efficacy; from drug dosage being determined on body mass alone to now considering their genetics, lifestyle and environment (i.e the entire variability of an individual) represents a massive leap in the field. *Also, note that personalised medicine is commonly referred to as precision medicine in the US.
A simple representation depicting how the genetic details of a patient can be noted to obtain specific drug plans that are suitable to the genetic circumstances of the patient, in this case in an oncology setting. Credit: Boston Children’s Hospital (Vector).
Personalised medicine aids doctors in being able to provide more unique treatment plans that tailor to the patient specifically, essentially granting individualistic treatment as opposed to a general plan that assumes effectiveness for a patient population. As the United States National Library Medicine puts it, personalized medicine is "an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle for each person." Through acknowledgement of the patient’s genetics, environment and lifestyle, a tailored plan can be established. An analogy for this would be as follows; an individual hoping to lose weight could use simply source food plans that are massively accessible online or directly consult a personal trainer to creating a food plan that considers their needs and expectations to deliver optimum results, again a tailored approach.
A diagram showing the effects of the HER2 gene mutation, a feature present in 30% of breast cancer cases. The activity of this mutated gene can be reduced by Herceptin and Laptinib which are drugs used primarily for this purpose, evidencing how genotypes and drugs interact. Credit: Aboutcancer.com (website)
As with all areas medical, artificial intelligence promises to deliver results faster and more accurately than a clinical expert, given the apt of deep learning algorithms in deciphering and analysing large data sets, spotting trends and matching them to diagnostic outcomes, and applying these trends in a new clinical setting; although time and effort must be taken before these programmes are allowed to influence a sensitive clinical setting, early efforts such as at the Moorfields NHS Trust in analysing OCT scans have shown AI’s capacity which can only improve with more testing and training with growing data sets, ideal in hospitals as they churn out these data samples each day globally (if patients consent to their data being shared).
Amplion, a leading company in precision medicine* (see first paragraph) and artificial intelligence development released Dx:Revenue earlier in the year, which itself uses machine learning algorithms to provide guidance and informative details for consideration in pharmaceutical and diagnostic environments. According to Forbes, their platform ‘uses over 34 million data sources from clinical trials, scientific publications, conference abstracts, FDA approved tests, lab tests, and other information to match a test provider’s capabilities to pharma’s specific needs.’ CEO of Amplion Chris Capdevlia noted that “[Personalised medicine] is particularly important in cancer, where we’re moving away from the one-size-fits-all approach to care toward a more targeted approach with treatments based on the biological characteristics of each patient, personalizing our approach to healthcare in this way not only results in better outcomes for patients, it also drives down drug development costs through shorter, more successful trials and reduces time to market for valuable drugs – all very good news for better patient outcomes.” From all this, it is clear to say that personalized medicine under AI provides a compelling future for diagnostic purposes, with the ability to refine medical treatment to an effectiveness matching those that would be considered ideal for general drug treatment plans. With the time and effort saved under the application of AI, it could also ensure patients are seen and treated much faster, but now lies the issue in ensuring that personalised approaches can be carried out at a cost-effective standard, perhaps not so much an issue in privatised healthcare situations but certainly so in public institutions such as the NHS.
A kind thank you in the direction of Chris Capdevila and Amplion for their pioneering efforts in this field, one that I can very much appreciate since understanding the potential of personalized medicine. I wish you the very best in your development and hope that these technologies are (appropriately) at the heart of healthcare in the years to come.
Article thumbnail credit: Predictive medicine uses the genetic counselling to measure the likelihood of illnesses that can be suffer and how to prevent them, Inspira Biotech [click here for page]. Note that this article has not contributed to any of the written content of my article, and that you click the above link at your own discretion as the page has not been checked by our team. Thank you to our readers for your continued support, and to anyone who contributes to our fantastic team to keep our services in an orderly fashion.