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The Theme - Precision Medicine Revolution

(The University of Chicago - Alvin Wei-Cheng Wong)


Smart, Precision and Preventive Medicine in the Era of Big Data



"Precision medicine aims to collect, connect, and apply vast amounts of scientific research data and information about our health to understand why individuals respond differently to treatments and therapies, and help guide more precise and predictive medicine worldwide." -- [Precision Medicine at UCSF]

The availability of the human genome, large amounts of data on individual genetic variations, environmental interactions, influence of lifestyle, and cutting-edge tools and technologies for big-data analysis have led to the age of precision medicine (also referred to as personalized medicine). Imagine receiving a full diagnosis from a simple blood test, or the ability to tailor the perfect treatment to each person's own genetic makeup, taking into account an individual patient's specific susceptibilities to side effects. What if we could apply a patient’s genetic information to gain insights into the genetic variations of diseases and expedite drug development, to create more precise therapies. Welcome to the world of precision medicine. That sounds futuristic, like science fiction, or a cool exhibit, something that promises great things for tomorrow, something that’s not here yet. But this is different. Precision medicine is already here. We have revolutionary new knowledge which has given us new targets for a smarter, more scientific approach that is helping all patients - but particularly those who need it most.

What is Precision Medicine?

Precision medicine is an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle for each person. Precision approach to medicine includes an individual’s genetic profile to guide decisions made in regard to the prevention, better diagnoses, earlier interventions, more-efficient drug therapies, and customized treatment plans. Precision medicine can also be defined as a predictive, preventive, personalized, and participatory healthcare service delivery model. For example, precision medicine is a particular game-changer for cancers, so many of which are responding well to new immunotherapies — treatments that engineer a patients’ own immune cells to better fight disease. 

The goal of precision medicine is to provide drugs and therapies that are uniquely suited to individual patients based on their genetics and other distinguishing health information. Personalized, cell-based therapies and personalized drug selection constitute two important facets of precision medicine, treatments specifically tailored to the individual receiving them. It is transforming the way diseases like cancer, diabetes, cardiovascular and chronic respiratory diseases are treated by tailoring medical care to the person’s genetic makeup, or the genetic profile of an individual’s tumour. In precision medicine, the focus is on identifying the approaches that will be effective for patients based on genetics as well as environmental and lifestyle factors. 

According to the National Institutes of Health (NIH), precision medicine is "an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle for each person." This approach will allow doctors and researchers to predict more accurately which treatment and prevention strategies for a particular disease will work in which groups of people. It is in contrast to a "one-size-fits-all" approach, in which disease treatment and prevention strategies are developed for the average person, with less consideration for the differences between individuals. 

The “one-size-fits-all” medical approach is a thing of the past. Advances in genomics, precision medicine and machine learning have led to powerful new discoveries. Hospitals, networks and the federal government can use precision medicine to drive expenses out of the system, better understand disease and prevent people from getting sick in the first place. So we like to think of precision medicine as the right treatment for the right patient at the right time for the right cost.

Standards of Care

Most often today, you go to your doctor with your symptoms, and you get an evaluation, maybe have a few tests run.  If you are lucky, you’re on your way to a diagnosis and a path to feeling better. However, your treatment plan doesn’t have all that much to do with you specifically. It’s identical to what doctors would hand over to essentially anyone with the same condition. That’s because medicine as we know it revolves around “standards of care,” the best courses of prevention or treatment for the general population, or the average person on the street. With breast cancer, for example, those standards mean self-exams and mammograms after a set age and the usual chemotherapy to treat a tumor if one is found. If the first treatment doesn’t work, doctors and patients move on to the next one and the next. It’s trial and error, with life on the line.

It is estimated that more than half of all cancer deaths could be prevented, and it is well established that early detection of cancer improves cancer mortality. Thus, The sooner these emerging early-detection tools and interventions become standard universal care, the sooner we will see higher survival rates. We can stop cancer before it starts because Precision Prevention doesn’t wait for the diagnosis. 

(Duke University - Cheng-Yu Chen)

Personalized Healthcare

Precision medicine is an approach that emphasizes the ways in which your disease risks are unique and different, just like your other, more obvious characteristics. Those disease risks are based on the predispositions written into your genome at birth, combined with your lifestyle and environment. 

Like unique fingerprints, human genes can vary distinguishably among individuals. Thus, we can monitor each person's health by understanding and characterizing his or her genes. Primary physicians can classify the genetic information of each patient and provide advice regarding prevention or predict diseases that have a greater probability of occurring. When the disease appears, physicians can also effectively provide tailored medications and therapies, ensuring that the most optional treatment is given to the right patient in the right dose at the right time. 

In the case of cancer, the disease has its own genetic makeup, lending each tumor a unique character with unique tendencies and vulnerabilities. And perhaps there is, or soon will be, a drug or treatment or tailored combination of the two that will work better for you than it would for someone else. 

It is now appreciated that some cancers run in families due to an inherited predisposition to cancer development. Due to the widespread availability of genetic testing, we now have the opportunity to successfully identify these families and the affected individuals. Because early detection and prevention can also improve mortality in individuals with an inherited predisposition to cancer, these individuals are an important target population for cancer prevention and early detection strategies. With appropriate attention to implementation, identification of at-risk individuals may empower them to make and act upon informed, cancer-preventing health decisions.

Precision medicine promises to increase patient-centered care, personalized patient-provider relationships, and treatments targeted to individual concerns and needs. However, such advantages come with certain ethical, legal and social controversies.

Next-generation Sequencing (NGS)

Precision medicine relies on an advanced DNA testing process known as next-generation sequencing (NGS). Today, developments in NGS and information technology have made precision medicine possible, with massive amounts of genetic, "omics," clinical, environmental, and lifestyle data now available. The tech­nology is still being developed, but NGS can map almost all of a person’s genome in a single run, making it much faster and cheaper than earlier methods. By detecting genetic markers for diseases, it can be used to inform prevention efforts, improve diagnoses, and suggest which treatments are likely to work best for each patient. The discovery of genetic biomarkers and their subsequent application in oncology research are paving the path toward a future in targeted therapy development, linking genes to disease states and prognosis and, ultimately, more effective treatments.

Seamless Integration of Data

Precision medicine aims to integrate personalized health data both from direct and indirect sources probing the deepest mysteries affecting individual health and well-being. Data integration means bringing together data from diverse sources, turning complex data into coherent and more useful information (i.e., knowledge), and turning it into something you can make use of programmatically. Moreover, integration of datasets significantly reduces the overall data complexity. The data becomes more available for use and unified as a system of its own. Such a streamlined and integrated data system can increase the collaboration between different parts of your data systems. Each part can now clearly see how their data is integrated into the overall system, including the user scenarios and the security and privacy processes around it. In order to do precision medicine we need all the patient’s diverse data in a common repository. It requires data utility ranging from collection and management (data storage, sharing, and privacy) to analytics (data mining, integration, and visualization). 

Because of rapid advances in biotechnologies, highly complex biomedical data are becoming available in huge volumes. Where does all this diverse data come from? If we could look at labeled data streams, we might see research and development (R&D), physicians and clinics, patients, caregivers, etc. The array of (at present) disparate origins is part of the issue in synchronizing this information and using it to improve healthcare infrastructure and treatments. Hence, the present-day core issue at the intersection of machine learning and healthcare: finding ways to effectively collect and use lots of different types of data for better analysis, prevention, and treatment of individuals. To make sense of these heterogeneous data, big data analytics, including data quality control, analysis, modeling, interpretation, and validation, is needed to cover application areas such as bioinformatics, health informatics, imaging informatics, and sensor informatics.

Implementing precision medicine in a clinical setting requires seamless integration of diverse data from clinical evaluations and biomedical investigations with genomics and other physiological profiling to characterize an individual patient’s disease progression. Implementing precision medicine practices in clinical settings requires coordinated efforts to integrate data from both healthy and disease states in individuals. The ideas behind precision medicine are not new, but the development of big data analytics, biobanking, new methods for categorizing and representing patients, and computational tools for analyzing large datasets have provided opportunities for more widespread use.

The Precision Medicine Ecosystem

Modern biomedical sciences are data-driven, and depend on many reliable databases of biomedical knowledge, or simply "knowledge bases". Knowledge bases are particularly important for precision medicine. knowledge bases are essential for clinicians to look up what treatment is known to be effective for their patients.

"Precision medicine consists of seven overlapping and intersecting elements, including basic, clinical, and social/behavioral discovery, plus the enabling tools of digital health, 'omic technologies, and computational health sciences. These elements are integrated by a knowledge network, creating a sort of "Google maps for health," which informs new research and technologies, and leads to more precise and predictive care." -- (UCSF)

"The precision medicine ecosystem contains building blocks that optimally connect patients, clinicians, researchers and clinical laboratories to one another. Patients and clinicians access information through patient portals or EHRs. The ecosystem can include Displays or Clinical Decision Support (CDS) tools augmented by curated knowledge that is supplied and shared by multiple stakeholders. Case-level databases (genotypes, phontypes and outcome, family and medical history, environment and exposures) and biobanks (patient samples) receive case data and samples from clinical and research workflows. Researchers benefit from all of these information sources and also contribute to knowledge sources. Clinical laboratories leverage data and inform the clinical community as they assess genomic variation and its impact on human health." -- (NATURE)

For example, by integrating genomic, phylogenetic, population genetic, statistical and machine-learning, computer vision, and medical imaging techniques, clinicians and biomedical scientists investigate clinical and molecular signatures of human diseases, and develop novel computational methods to discover biomarkers (eg., imaging/biomarkers) for early diagnosis and accurate prediction of therapeutic responses for individual patients.

As genetic testing and interpretation advance, Precision medicine stands to move medicine away from the population-based knowledge that grounds evidence-based medicine (EBM) to the treatment of patients “based on a deep understanding of health and disease attributes unique to each individual. Such understanding requires a different and broader concept of medical knowledge, the development of new methods for generating such knowledge, and approaches for incorporation into clinical practice. As precision medicine advances, for some decisions it will replace the population-based “best evidence” of EBM with specific and detailed understanding of what makes an individual patient different from others. To practice Precision medicine, clinicians should reconsider current notions regarding the relative value of evidence, as case-based reasoning and understanding of mechanisms will figure more prominently.

Interdisciplinary Training for Precision Medicine

Recent developments in molecular biology and information technology make precision medicine a reality today through the use of massive amounts of genetic, 'omics, clinical, environmental, and lifestyle data. Precision medicine has the potential to profoundly improve the practice of medicine, but requires a health care workforce that understands the complexities of this field. One of the most critical is an informatics workforce that has broad interdisciplinary training in basic science, applied research and clinical implementation. However, the advances required will take time to implement.

One important component of precision medicine is the use of an individual’s genomic information to offer targeted treatment, tailored to the individual. Genomics is a branch of molecular biology focused on studying all aspects of a genome, or the complete set of genes within a particular organism. Genomic technologies are generating an extraordinary amount of information. Genetics is already being used to direct clinical decision-making and its contribution is likely to increase. 

To accelerate these advances, fundamental changes are needed in the infrastructure and mechanisms for data collection, storage and sharing. This will create a continuously learning health-care system with seamless cycling between clinical care and research. Patients must be educated about the benefits of sharing data. If precision medicine approaches are to become part of routine healthcare, doctors and other healthcare providers will need to know more about molecular genetics and biochemistry. They will increasingly find themselves needing to interpret the results of genetic tests, understand how that information is relevant to treatment or prevention approaches, and convey this knowledge to patients. 


Bioinformatics, the key to a precision medicine future, aims to store, organize, explore, extract, analyze, interpret, and utilize information from biological data. Bioinformatics optimizes translational medicine through the transformation of data into diagnostics, prognostics and therapeutics. 

Translational medicine is becoming ever-more interdisciplinary. The ability to collect, store and analyze massive amounts of molecular and clinical data is fundamentally transforming the scientific method and its application in translational medicine. Biomedical researchers need new computational approaches to deal with the large amounts of data pouring in from genomics and other fields, and as new advances in physics and materials science offer new approaches to study or diagnose medical conditions.

Translational bioinformatics is a rapidly emerging field of biomedical data sciences and informatics technologies that efficiently translate basic molecular, genetic, cellular, and clinical data into clinical products or health implications. Its focus is on applying informatics methodology to the increasing amount of biomedical and genomic data to formulate knowledge and medical tools, which can be utilized by scientists, clinicians, and patients. Furthermore, it involves applying biomedical research to improve human health through the use of computer-based information system. Translational bioinformatics employs data mining and analyzing biomedical informatics in order to generate clinical knowledge for application. Clinical knowledge includes finding similarities in patient populations, interpreting biological information to suggest therapy treatments and predict health outcomes.

Bioinformatics has revolutionized the growth in the scale and complexity of biological data of the healthcare industry. Recent developments in the strategies and technologies like big data, NGS and microArrays, has changed the pace and driven the industry of genomics and molecular medicine towards commercially viability and impromptu outcomes.

'Omics Medicine

Precision medicine is an innovative approach that takes into account each individual's different characteristics, such as genetic profile, environment, and lifestyle. To capture the entire spectrum of potential differences, a multi-omics approach has been considered. `Omics has far-reaching capabilities and includes many different fields, such as genomics, transcriptomics, epigenomics, proteomics, glycomics, metabolomics, and lipidomics. Such a varied array of research avenues is believed to propel our understanding of diseases, leading to powerful new discoveries and treatments that can be tailored to individuals. It also gives medical professionals the resources needed to design specific treatments for individuals. 
The advance of precision medicine heavily relies on the ability to study biological phenomena at 'omics levels. Patients can be treated according to their own molecular characteristics. Individual omes as well as the integrated profiles of multiple omes, such as the genome, the epigenome, the transcriptome, the proteome, the metabolome, the antibodyome, and other 'omics information are expected to be valuable for health monitoring, preventative measures, and precision medicine. Moreover, 'omics technologies have the potential to transform medicine from traditional symptom-oriented diagnosis and treatment of diseases towards disease prevention and early diagnostics. 
In the future, newborns could be assessed from an ‘omic perspective, looking at genes, proteins, gut microbes, metabolic markers and the like. Beginning even before birth, practitioners would be able to see how these elements of health change as people progress through the course of their lives. These details would not only help the individual, but also provide a wealth of information for comparative analyses between individuals and groups, revealing patterns of risk and response to treatment for individuals and populations.
Born in basic research, ‘omics have become a powerful contributor across precision medicine.

-'Omics Big Data

The two fields, genomics and bioinformatics, have been facing enormous challenges and chances in the past three decades. Development of high-throughput technologies, such as next generation sequencing, have also brought us into an “'omics” era, where not only genomics, but transcriptomics, epigenomics, proteomics, metabolomics, and microbiomics data can be generated in real time, enabling scientists and clinicians to examine for the first time the effect of multi-omics on disease pathogenesis in unprecedented detail. The combined 'omics information leads to a global profiling of health and disease, and provides new approaches for personalized health monitoring and preventative medicine.

'Omics data, mobile Internet real-time data and electronic health record data are the top three areas for big data in medical research. Precision medicine will use all of these three big data.

-'Omics Technologies 

A major objective of 'omics technologies is to understand genetic causality of complex traits of human diseases. New 'omics technologies have made it possible to obtain high-resolution molecular snapshots of organisms, tissues, and even individual cells at various disease states and experimental conditions. It is hoped that these developments will usher in a new era of personalized medicine in which an individual's molecular measurements are used to diagnose disease, guide therapy, and perform other tasks more accurately and effectively than is possible using standard approaches.

Disease progression and drug response may vary significantly from patient to patient. Fortunately, the rapid development of high-throughput 'omics technologies has allowed for the identification of potential biomarkers that may aid in the understanding of the heterogeneities in disease development and treatment outcomes. High-throughput 'omics technologies and their application to medicine open up remarkable opportunities for realising optimised medical treatment for individuals. Integrated 'omics investigations will be critical in piecing together targetable mechanisms of action for both drug development and monitoring of therapy in order to fully apply precision medicine to the clinic.

Molecular Pathology and Precision Medicine

Molecular pathology is another major tool in precision medicine. Molecular pathology is an emerging discipline within pathology which is focused in the study and diagnosis of disease through the examination of molecules within organs, tissues or bodily fluids. Tiny samples of blood or tissue are taken from the patient and analysed for levels of large molecules such as proteins and DNA. Combining these results with other information, such as imaging and clinical data, enables us to precisely divide patients into subgroups and optimise their treatment. Molecular pathology is commonly used in diagnosis of cancer and infectious diseases. 

Developments in diagnostic technology and the rise of precision medicine mean pathologists are more involved in clinical decisions than ever. Clinical applications in molecular pathology have grown exponentially, and the field has evolved to become a focal point of precision medicine,

Systems Biology-Powered Precision Medicine

The field of systems biology has far reaching impact on the disciplines of biology, medicine, and drug discovery. It offers great potential for the betterment of human life and cure against diseases. The rise of technologies that simultaneously measure thousands of data points represents the heart of systems biology. These technologies have had a huge impact on the discovery of next-generation diagnostics, biomarkers, and drugs in the precision medicine era. Systems biology is the study of systems of biological components, which may be molecules, cells, organisms or entire species. Systems biology aims to achieve systemic exploration of complex interactions in biological systems. 

One important aspect of systems biology is data mining. Data management and access can become a daunting task given the tremendous amount of data generated with current high-throughput technologies, and the data size is constantly increasing with time. Challenges exist computationally in each step to handle, process and annotate high-throughput data, integrate data from different sources and platforms, and pursue clinical interpretation of the data. These steps can be quite computationally intensive and require significant computational hardware.

Systems biology is actively transforming the field of modern healthcare from symptom-based disease diagnosis and treatment to precision medicine in which patients are treated based on their individual characteristics. Development of high-throughput technologies such as high-throughout sequencing and mass spectrometry has enabled scientists and clinicians to examine genomes, transcriptomes, proteomes, metabolomes, and other omics information in unprecedented detail. The combined 'omics information leads to a global profiling of health and disease, and provides new approaches for personalized health monitoring and preventative medicine.

(McGraw Tower - Photo Courtesy of Cornell University)


Current practices for prescribing medication within classes of drugs are relatively arbitrary - performed using the ineffective "test and react" approach - and many patients do not respond to the first medication they are prescribed. Diagnostic tests to identify whether a person will respond positively to a given drug, or have a potentially life-threatening reaction to the medication or dose, could save billions of dollars, increase the quality of care and prevent fatalities.

Pharmacogenomics (or drug-gene testing), a relatively new field, is the study of the way genetic differences between individuals influence patient drug responses and drug disposition. Significant interindividual variation exists within the measured human genomes. This variability can have a major impact on the effectiveness of the many therapeutic drugs that require activation or inactivation by the affected enzymes. Therefore, Pharmacogenomics can play a major role in dosage adjustment to ensure effectiveness and/or prevent toxicity.

Pharmacogenomics is the study of how your genes affect the way your body processes and responds to medications. It combines pharmacology (the science of drugs) and genomics (the study of genes and their functions) to develop effective, safe medications and doses that will be tailored to a person’s genetic makeup. Someday, it should be possible for doctors to send individual cancer patients in for a genomic analysis and, based on the results, prescribe the drug they know will be the most effective. While the promise of this kind of personalized medicine is still distant, many researchers worldwide are working on it now, interpreting the genomes of individual cancer patients and searching for clues to how they will respond to various treatments. Pharmacogenomics and a new era of genomic testing will identify and hopefully treat inherited cancer.

Many drugs that are currently available are “one size fits all,” but they don’t work the same way for everyone. Whether used to explain a patient’s response or lack thereof to a treatment, or act as a predictive tool, precision medicine hopes to achieve better treatment outcomes, greater efficacy, minimization of the occurrence of drug toxicities and adverse drug reactions (ADRs). Adverse drug reactions are a significant cause of hospitalizations and deaths in the United States. With the knowledge gained from the human genome project, researchers are learning how inherited differences in genes affect the body’s response to medications. These genetic differences will be used to predict whether a medication will be effective for a particular person and to help prevent adverse drug reactions. Pharmacogenomics testing will play a growing role in guiding medication decisions.

The field of pharmacogenomics is still in its infancy. In the future, pharmacogenomics will allow the development of tailored drugs to treat a wide range of health problems, including cardiovascular disease, Alzheimer disease, cancer, HIV/AIDS, and asthma. 

Mass Spectrometry

Mass spectrometry, an analytical technology capable of measurements with high levels of reproducibility, precision and accuracy, brings precision and clarity to precision medicine. Mass spectrometry systems are helping biomedical researchers gain deeper insight into the mechanisms of disease progression and therapeutic intervention.

The use of mass spectrometry can provide an understanding of how a drug is interacting with the patient, and is orthoganol to the information provided by pharmacogenomic assays. Further, the speed and relatively low expense of drug monitoring by mass spectrometry makes it an ideal test for precision medicine patient management.

Human Biobanks

A biobank is a biorepository that accepts, processes, stores and distributes biospecimens and associated data for use in research and clinical care. The field of biobanking has changed tremendously over the past thirty years. It started with small, predominantly university-based repositories that were developed for the research needs of specific projects. There gradually evolved institutional and government supported repositories, commercial (for profit) biorepositories, population based biobanks and most recently, virtual biobanks. 

The data associated with stored biospecimens have increased in complexity from basics, such as date of collection and the diagnosis, to extensive information sets encompassing many aspects of participant or patient phenotype, now rapidly extending into genetic, proteomic, and other “omics” information. Data from biological samples combined with information from lifestyle and health questionnaires, medication history, electronic health records, physical exams, and environmental exposures and real time physiology tracked through digital technologies, will help researchers examine individual differences in health and disease.

Biobanks give researchers access to data representing a large number of people. Samples in biobanks and the data derived from those samples can often be used by multiple researchers for cross purpose research studies. For example, many diseases are associated with single-nucleotide polymorphisms, and performing genome-wide association studies using large collections of samples which represent tens or hundreds of thousands of individuals can help to identify disease biomarkers. Many researchers struggled to acquire sufficient samples prior to the advent of biobanks. 
Since the late 1990s biobanks have become an important resource in medical research, supporting many types of contemporary research like genomics and personalized medicine. Biorepository and biospecimen science has evolved in response to the changing landscape of external regulatory pressures, the advances made in the biological sciences, and the advent of the computer chip. 
Biobanks have provoked questions on privacy, research ethics and medical ethics. While viewpoints on what constitutes appropriate biobank ethics diverge, consensus has been reached that operating biobanks without establishing carefully considered governing principles and policies could be detrimental to communities that participate in biobank programs. 

Main Barriers to Greater Implementation of Precision Medicine

Precision medicine is a young and growing field. Many of the technologies are in the early stages of development or have not yet been developed. For example, researchers will need to find ways to standardize the collection of clinic and hospital data from a huge number of volunteers around the country. They will also need to design databases to store large amounts of patient data efficiently. Drugs that are developed to target a person's genetic or molecular characteristics are likely to be expensive, just to name a few.

Currently, there are two main barriers to greater implementation of precision medicine: High costs and technology limitations. Fortunately the cost of sequencing a genome continues to drop year-over-year. Advances in the speed, accuracy, and cost of next-generation gene sequencing making it possible for clinical labs to create thousands of new tests. Artificial Intelligence (AI) can speed up precision medicine. To tackle the vast amount of patient data that must be collected and analyzed, and to help cut down on costs many researchers are implementing machine learning techniques. Today, machine learning is playing an integral role in the evolution of the field of genomics. The potential for AI in precision medicine is big. 

The Future of Precision Medicine 

Precision medicine will dramatically change the health care landscape by improving upon current knowledge of such things as disease progression and drug efficacy. Imagine a world where your disease is immediately treated with a drug, designed uniquely for you, making it incredibly efficient. Even better, imagine a technology which, after inputting the parameters of your body, outputs a prediction and hence prevents a future illness. This is the dream of precision medicine.

The future of precision medicine will enable health care providers to tailor treatment and prevention strategies to people’s unique characteristics, including their genome sequence, microbiome composition, health history, lifestyle, and diet. To get there, we need to incorporate many different types of data, from metabolomics (the chemicals in the body at a certain point in time), the microbiome (the collection of microorganisms in or on the body), and data about the patient collected by health care providers and the patients themselves. Success will require that health data is portable, that it can be easily shared between providers, researchers, and most importantly, patients and research participants. 

Massive and growing databases of gene sequencing data promise long-sought breakthroughs in medicine. How will we balance the pursuit of better health with ethical questions raised by this fast-moving field, from defining acceptable applications to establishing ownership of our genetic data?

Precision medicine is already changing lives and improving outcomes through targeted therapies that help patients live longer and better lives, even after battling serious disease. While significant advances in precision medicine have been made for select cancers, the practice is not currently in use for most diseases. Many efforts are underway to help make precision medicine the norm rather than the exception. The vision holds enormous promise for the future but healthcare still has huge ground to cover when it comes to making precision and personalized medicine an everyday part of primary care.

We can see now that the future of medicine is near and will be mediated through the integration of technologies across multiple disciplines, such as genetics, genomics, big data, and deep learning.


<updated by hhw: 11/6/18>






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