Dr. Ali Tinazli is the CEO of lifespin.health and has 15+ years in Fortune 100 corporate strategy and entrepreneurship (SONY, HP).
Society has benefited from remarkable scientific advances in the 21st century; perhaps few are more profound than the extraordinary achievements in human health, thanks to the convergence of biology, medical science and information technology. It is now possible, for instance, to sequence an individual’s entire genome rapidly and inexpensively.
At the same time, developments in wireless technologies and big data make it easier to collect and store large amounts of health-related information. The convergence of these two trends and others is revolutionizing the diagnosis and treatment of disease and ushering in a new era of deeply precise, individualized medicine that may forever alter healthcare delivery to millions of people.
One of these significant advances is in using bioinformatics, specifically the identification and utilization of human “biomarkers” to accelerate the development of improved and innovative diagnostics. An offshoot of this field is called “metabolomics,” a relatively new branch of medical science that holds great promise in making precision medicine affordable and more accessible.
Metabolomics, from a health standpoint, is the study of how disease impacts a person’s normal metabolism, representing the biochemical process involved in virtually everything human beings do—from moving and growing to thinking. Diseases are believed to cause a disturbance of healthy metabolism, which leaves biochemical patterns in the human body, e.g., in blood.
Physician-scientists have long believed and found, through research, that these changes in a person’s biomarkers, i.e., their metabolism, can provide valuable and early indicators for disease states and other health-related conditions. For example, if two patients come to their physician with identical symptoms, a blood test would differentiate their illnesses and accurately diagnose them. But only until relatively recently, because of advances in the convergence of various technologies, has this promise become a reality.
Today, these metabolic signatures are not only traceable but are also beginning to be very accurately correlated to specific disease states, utilizing powerful digital platforms that use deep learning, advanced software solutions and artificial intelligence, all cost-efficiently scalable via the cloud. Powered by metabolomics as a new and near-universal health diagnostics modality, early and rapid identification and treatment of diseases—as well as general wellness information—may, for the first time, become far more accessible and cost-effective, not just for those living in industrialized nations with well-developed health care systems, but for the global population as well.
Here is a brief explanation of how technology will contribute to the innovative digitization of diagnostics.
Deep learning algorithms map out disease progression based on quantitatively “scanned” patient samples.
Deep learning algorithms have the potential to improve diagnostic accuracy by providing a more comprehensive analysis of a patient’s medical history, lab results and symptoms. Doctors can then use a more accurate picture of their condition to identify any early warning signs of illness.
In addition, deep learning algorithms can enable healthcare providers to monitor a patient’s condition over time, making it possible to detect changes that might indicate the need for further testing or treatment. As a result, deep learning algorithms have the potential to vastly improve the accuracy of diagnosis and improve outcomes in the treatment of complex illnesses.
At my company, our focus is on using proprietary deep learning algorithms to identify significant deviations in metabolic networks of different health conditions. These deviations are captured quantitatively by “scanning” blood samples for metabolites with a physical method called nuclear magnetic resonance (NMR). This interface between the classical diagnostics world with patient samples in a wet lab combined with information technology elevates the technology convergence in healthcare to the next level.
We use these algorithms to map out how diseases progress, which is crucial for diagnosing and developing more effective treatments. For example, our algorithms might identify that a particular illness progresses by causing a build-up of toxins in cells. We can then use this information to develop a treatment that targets the build-up of toxins rather than the disease itself.
AI elevates the analysis of metabolic data.
Understanding how different parts of the human body interact is essential for developing new diagnostics and effective treatments for various medical conditions. However, this information is often scattered across multiple databases, making it difficult to access and use—AI keeps all the data in one place.
AI is beneficial in clinical research because it can differentiate and diagnose chronic diseases through patterns in human metabolism. Doctors can now diagnose and treat chronic diseases more accurately by using AI to analyze these patterns. Analyzing metabolomic data, AI can help identify new drugs that could effectively treat diseases.
Cloud-based platforms are improving access to human health technologies.
Standard diagnostics are typically slow, cumbersome and tedious, making them costly and difficult to access. New technologies sought after for deeper clinical insights are associated with the same attributes, making it challenging to get accepted into the standard of care. These technologies at the interface of biology and IT typically require a lot of computational power but can now be handled cost-effectively in the cloud. A new diagnostics platform that can scale cost-efficiently as a cloud-based solution will lower the barriers to entry. It will be intrinsically more accessible to providers and patients.
Complex diseases are often challenging to diagnose and treat. Cloud-based digital platforms provide new opportunities for providers to offer precise diagnostics tools to their patients and facilitate tailored treatments for these conditions. These platforms can help identify patterns and trends useful in differential diagnosis by analyzing large amounts of data. Therefore, cloud-based digital platforms will be essential in supporting better treatments for chronic diseases.
The future of human health is now.
Human health has been monitored and managed for centuries using crude tools and methods. With the advent of technology, we can now digitize every aspect of our health for accelerated and improved healthcare services. Most importantly, these next-generation cloud-based tools for precision medicine will be more accessible and affordable, enabling adoption on a broad population level. Imagine the impact for millions worldwide as expedited diagnoses and disease prevention becomes an everyday reality.