Life Expander A.I.

Our goal: Aiming to Cure Healthcare by providing a Panoramic View Over Current, and Future Pathophysiological Conditions. The pain: Did you know that over two-thirds of the world's population has limited or no access to primary, high-quality, or on-time healthcare services? In addition, based on a tiny fraction of our extensive Market Research in collaboration with Absolute Reports, global Healthcare Professionals, especially those in the UAE, are experiencing exhaustion, stress, and even depression, mostly due to their increasing workload. Among those, most of the Radiologists, are forced to be generalists instead of specialists (e.g. a Neuroradiologist), due to considerable Workforce Vacancy Rates in the country. Of course, this is an additional pain for this specific group of experts. On the other hand, It’s no secret, the human body is convoluted, and trying to provide precise diagnoses and prescriptions is quite a task, even for the most seasoned doctors. Especially, considering that as of 2021, several thousand diseases affect humans, and only a bit over 500 have any healthcare authorities approved treatment. Other diseases can only be treated or managed, preferably through new drug therapy or clinical trials. To address this disarray, doctors try to collect as much information as possible from their patients. This information comes in an array of sources, including the Doctor’s Notes, Clinical Images, Genetic Tests, Demographics, and Laboratory Results. Today, databases of Electronic Health Record systems contain data for billions of patients, but, despite all that data available, and the best practices of physicians, misdiagnoses, late, or incomplete diagnoses, followed by mistreatments, still, happen on a daily basis. This is due to the lack of a proper Data, Information, Knowledge, and Wisdom Model (or DIKW) that provides a framework for defining the scope of practice for Medical Informatics. In other words, data sets are often comprised of multiple views, which provide consensus and complementary information to each other, and extracting clinically meaningful insights is beyond human capabilities most of the time. In fact, even in frontier countries like the US, the Institute of Medicine estimated the number of Americans dying each year as a result of unintentional medical errors, are as high as 98,000—more than those who die from motor vehicle accidents, breast cancer, or AIDS. In addition, the clinical environments physicians are required to think and act during the diagnostic process can be high-pressured, and extremely time-sensitive. Dealing with uncertainty is difficult. All this pressure can result in diagnostic errors, especially when a doctor runs into problems collecting or understanding information pertaining to physical examinations, patient history, or tests. Things are typically not straightforward when a patients’ disease conditions are constantly progressing, and evolving over time. Why so much pain? The Institute of Medicine identified reasons for this disconnect between an ideal system and what actually exists. These reasons include: (1) Poor design of systems and processes, (2) The system’s inability to respond to changing patient demographics and related requirements, (3) Failure to assimilate the rapidly growing and increasingly complex science and technology base, (4) Slow adoption of information technology innovations needed to provide care, (5) Little accommodation of patients’ diverse demands and needs, and (6) Personnel shortages and poor working conditions. Our approach: As you can imagine, our approach is always the identification of pain points, frictions, and real problems along with Market Dynamics and SWOT analysis first, and then going all the way back to the selection, combination, or creation of bleeding-edge technologies in order to address them, and not the other way around. Our solution: Based on more than three years of intensive R&D along with the brightest minds, our startup, Life Expander AI, is developing an intelligent Web-based Panel that helps Radiologists, and Neurophysiologists, diagnose and provide treatment plans more accurately and efficiently. Also, considering the critical role Medical Imaging plays, in diagnosis and treatment, we have dedicated a major part of our services to early and precise detection of Neurodegenerative Diseases, Cerebrovascular Accidents, and Traumatic Brain Injuries via our automated medical imaging analysis. Another reason is that the Medical Imaging Market-Size in the UAE alone will reach 1.2 billion USD by the end of 2023, of which 25.6% is dedicated to Neurology, and In terms of Volume Segments, 65.6% is dedicated to Neurodegenerative Diseases, and 22.3% to Cerebrovascular Accidents and Traumatic Brain Injuries. Basically, we empower the Radiologists and Neurophysiologists with precise and efficient assistance in detecting upcoming health issues related to the Central Nervous System, and containing them effectively, if they are already in a crucial state. Of course, in the longer term, we will cover the whole human body and a wider target audience incrementally. Our business and technological advantages: The lack of Interpretability of AI Models has been one of the most significant barriers preventing their application in healthcare. Such models exhibit great capacity, but understanding their behavior and following their decision-making process is not trivial. This is another problem that we are aiming to resolve via our proprietary DMPV (or Decision Making Process Visualization) technology backed by our unique approach to Visual Reasoning, Knowledge Graphs, and Cognitive Databases. This technology alone will enable us to develop white-box models or explain current black-box ones. Aside from that, our system is consisting of two major parts: The first part of our system which is completely modular in terms of pipeline modifications is mostly backed by Visual Reasoning and NVIDIA Clara Imaging Application Framework. The second part of our system is designed as a Controversial Expert System that of course is consisting of a Knowledge Base and an Inference Engine, but our approach is to design these two major components in a way that ultimately, resembles a Biomedical Digital Twin of each patient. At Life Expander AI, the meaning of Digital Twin diverges from the conventional definition. There is no physical product to be built, instead, we provide experimenting with therapies, on a Digital Twin that will be risk-free, cost-effective, efficient, and will grant our clients with a rigorous testbed to conduct medical interventions. We designed our Inference Engine Architecture in a way that auto-ingests Graph-Structured, Multimodal/Multi-View, Heterogeneous-Structured, and Unstructured Data from our Medical Image Analysis and various other data sources like healthcare institutions to regularly update our Patient Digital Twin, then infuses the results to provide Superior Suggestions, including Diagnoses, Recommendations for Treatments, or Related Clinical Trials and possible Side-Effects. All, in a matter of a few minutes. We are also in negotiation with Infermedica to utilize their Symptom Checking API to Extract Missing or Additional Necessary Data from Patients needed by Doctors and feed that data to our system. Moreover, our clients will be able to interact with our system in a Bidirectional Manner, predict upcoming issues, test treatments on the Computerized Version of each patient, and visualize the ups and downs of each adjustment in real-time. In our vision, a Composable AI Architecture, (Combination of AI with Mathematical Modeling), will enable the development of automatic analysis, verification techniques, predictions, and accurate prescriptions that are key to addressing difficult diagnoses of Neurodegenerative Disorders (among others), in very early stages. Within this part of our service, our custom AI Model could even enable the Prediction of Disease Trajectories, Before the Insurgence of Symptoms. In more detail, the 1st module of this part is a custom Graph Neural Network, forecasting Clinically Relevant Endpoints, the second part is based on Generative Adversarial Networks, Simulating different Medical Scenarios, and the third part is a handcrafted Ordinary Differential Equation based on our Domain Experts Knowledge that creates a Biomedical Graph Structure (and a Cognitive Database) of the body. The Graph Representation of a Computational Patient, or Organ of the body, has also the potential to solve important technological challenges in integrating Multiscale Computational Modeling with AI. We believe that our solutions represent a step forward toward next-generation services for precise prediction, prescription, and even personalized medicine. Because AI simulations forecasting the evolution of clinical endpoints over time will be able to reshape clinical guidelines, which will no longer be based just on horizontal population studies. Responsible AI Principles, Proactive Cybersecurity, and Patient Privacy in collaboration with BlackBerry, and MedicalChain, in addition to utilizing bleeding-edge techniques such as Federated Learning, designing a Hybrid, and Decentralized Infrastructure, are also considered very seriously throughout the whole process because we are following the European GDPR standard. We are also offering a flexible Business Model including fee-per-study and subscription licensing plans based on our client's capacity and demand. Conclusion: In the end, our goal is to help health professionals to be prepared for the major challenges of today’s healthcare system, even though what we call a healthcare system can hardly be called a system. Rather, it is a dizzying array of highly decentralized sectors. But we are going to fix them with your support. As Sir William Osler once said, healthcare must be: (1) Human-Centered: "The good physician treats the disease; the great physician treats the patient who has the disease." (2) Personalized: "It is much more important to know what sort of a patient has a disease, than what sort of disease a patient has." (3) Data-Driven: "Medicine is a science of uncertainty and an art of probability." Thank you. Farhoud Zolhayat. Founder, CPO, and Solutions Architect. Life Expander A.I.


The Pain: It’s no secret, the human body is complex, and trying to diagnose our illnesses can be quite a task, even for the most seasoned doctors. To better their diagnoses, doctors collect as much information as possible from their patients. This information comes in an array of sources, including doctor’s notes, clinical images, genetic tests, demographics, and laboratory results. Today, databases of electronic health record systems contain data for billions of patients. But, despite all that data available and the best practices of physicians, misdiagnoses still happen on a daily basis. The clinical environments physicians are required to think and act during the diagnostic process can be high-pressured, and extremely time-sensitive. Dealing with uncertainty is difficult. All this pressure can result in diagnostic errors, especially when a doctor runs into problems collecting or understanding information pertaining to physical examinations, patient history, or tests. Things are typically not straightforward when a patients’ disease conditions are constantly progressing and evolving over time. Who We Are: We are a pre-revenue stage and self-funded Healthtech startup. Our Focus: Neurodegenerative Disorders, TBI, and Stroke. Audience: Medical Scientists, Physicians, and Radiologists. Mission Statement: To prevent any unintentional medical errors and biases before the consequences become critical so that we can: ‣ Prolong the lifespan of individuals. ‣ Improve the quality of health care and the intended results. ‣ Reduce wasteful government health care spending. Vision Highlights: We envision a world where not even a single lifespan is affected by medical uncertainties caused by a lack of in-depth, connected, perpetual, and human-interpretable predictive insights.


  • Health Care
  • Computer Vision
  • Health Diagnostics
  • Medical
  • Assistive Technology
  • Electronic Health Record (EHR)
  • Emergency Medicine
  • Genetics
  • mHealth
  • Personal Health
  • Therapeutics