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How personalised medicine and treatment are transforming the healthcare landscape

The purpose of personalised treatment is to choose and offer patient-specific treatments in order to obtain the best result. Human beings are incredibly diverse -- we all have unique biology, different metabolic responses, and different lifestyle preferences for foods, activities, etc. Because of this, a one-size-fits-all solution doesn't work.

Healthcare should be precise and tailored to the needs and preferences of every individual. While one set of medicines may be effective for one group of patients, it will not prevent another group of patients with nearly identical clinical parameters from developing to a severe stage from a mild or moderate illness. Precision treatment, with its more "customised" approach, offers a solution to this problem.

Precision treatment helps in lowering trial and error-based treatment decisions and patient centric-treatment through the integration of multi-modal data from an individual.

The focal point is the preventive mode of medication instead of the reactive mode as this treatment is time-efficient and cost-effective in terms of pharmaceutical clinical trials. With the help of technological advancements in sensor technology, artificial intelligence, machine learning, and digital twin technologies, it is now a possible way.

Machine Learning (ML) and Artificial Intelligence (AI) go hand-in-hand. ML is a branch of AI which identifies a variety of data patterns to predict or categorise the hidden or unseen patterns which can be applied and used for exploratory data analysis.

The ML algorithms specify the possibilities of recognising target-based medicines, which have been developed on clinical, genomics, laboratory, nutrition and life-style related data. AI plays a key role in developing personalised medicines in all the relevant phases of clinical development. Moreover, AI plays a crucial role in the implementation of new personalised health products.

Artificial Intelligence and digital patient models have significantly helped scientists identify the high risk genes for COVID-19. Determining drugs which were developed for other diseases can also be repurposed to treat coronavirus.

These drugs have such chemical compositions that it can be used to increase the survival rates of patients with severe complications like sepsis while they were suffering from COVID-19 through developing new therapeutic strategies.

Underlying and associated conditions can be isolated and identified through data-driven insights. It also helps in understanding the patients’ developing complications with severe COVID-19 reactions.

Furthermore, by augmenting medical care with digital tracking and advanced modelling of the human body, the use of digital twins in healthcare is transforming clinical processes. Researchers can use these technologies to learn more about diseases, new medications, and medical equipment.

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In the future, it may potentially be used to assist physicians in maximising the effectiveness of patient-specific treatment plans.

However, in the short term, digital twins will aid the healthcare system in bringing life-saving breakthroughs to market more quickly, at lower costs, and with enhanced patient safety.

Sensor technology is also impacting healthcare and medicine by authorising clinical monitoring outside of clinics. This helps in predicting health events of patients.

Sensor technology has already started revolutionising biomedicine via mobile and digital health by permitting perpetual and longitudinal health monitoring outside of the clinics.

Practitioners have started adopting sensors for patient monitoring. Moreover, it also specialises in automated health event prediction, prevention and intervention by facilitating algorithm development.

The cutting-edge technology in non-invasive wearable sensors help capture real-time personal health information at scale. Wearable sensors not only provide opportunities to improve healthcare in-hospital but a variety of settings like, in-clinic care, ambulatory care at home and in remote geographical settings. The remote geographical settings also include rural areas and low-resource environments.

The cutting-edge technological advancements will continue its growth in Artificial Intelligence and Machine Learning, empowering doctors in offering precisioned treatment, enabling them to offer better healthcare to patients, helping people live healthier.

Precisioned healthcare will continue to be more accessible by everyone because of its accuracy and cost-efficiency with rapidly growing advancement in technologies.

The major focus of healthcare will take a shift from treatment to risk definition, patient stratification, and precisioned treatment promotion and disease prevention strategies by 2030.

Precisioned treatment will continue to follow the path of advancements because it has promised prediction, prevention, and treatment of illness that is targeted for individual needs will follow the path of commercialised offerings.

It has been a crucial initiation to use new technologies to make a move from profiling detailed biological data of individuals at molecular level to personalised medicine. For this vision to become a reality, novel technologies will be needed further.

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