Table of Contents
Key Highlights
Using A.I. to strengthen preventative medical care removes unnecessary diagnostic tests, incorrect diagnoses, or wasted procedures. By engaging patients in their medical care, patient-centered approaches drive measurable cost savings throughout community health systems by dramatically reducing the likelihood of patients being readmitted for easily treatable and preventable conditions.
Chronic diseases are dramatically increasing in prevalence and mortality worldwide. In the United States, $3.7 trillion—approximately 19.6% of the gross domestic product (GDP)—is spent addressing the impacts of chronic disease annually.
However, most chronic disease pathologies are caused by just a few key risk factors:
- Tobacco use and exposure to secondhand smoke.
- Poor nutrition, including diets low in fruits and vegetables and high in sodium and saturated fats.
- Physical inactivity.
- Excessive alcohol use.
As healthcare organizations continue to embrace person-centered care initiatives, there is tremendous potential to reduce the societal impacts caused by chronic disease. Soon, physicians will be better equipped with laser-focused diagnostic insights about patient physiology, risk factors, and individual treatment plans.
Medical research to enhance the predictive power of early diagnosis to predict illness is utilizing augmented artificial intelligence (A.I.), machine learning (ML), and artificial neural networks (ANN) with particle swarm optimization (PSO). Artificial intelligence-based predictive analytics are on the frontlines of innovation to better predict diseases by studying data generated from health devices, sensors, clinical databases, social networks, wearables, and the medical Internet of Things (IoT).
Medical researchers at the Perelman School of Medicine at the University of Pennsylvania and the University of Florida College of Medicine recently received a $4.7 million grant from the National Institutes of Health (NIH) to apply A.I. and machine learning (ML) to information from patient medical records to better predict the risk of rare disease development.
Over the next four years, the team will continue developing predictive models to help understand which patients are more likely to experience five types of vasculitis and two types of spondyloarthritis.
The predictive model being developed through this grant is called “PANDA: Predictive Analytics via Networked Distributed Algorithms for multi-system diseases” and was developed by principal investigators Yong Chen, Ph.D., a professor of Biostatistics, and Peter A. Merkel, MD, MPH, chief of Rheumatology and a professor of Medicine and Epidemiology at Penn, and Jiang Bian, Ph.D., chief data scientist of the University of Florida Health System and a professor in the Health Outcomes & Biomedical Informatics at the University of Florida College of Medicine.
“This is an exciting step forward, building on our current PDA framework, from clinical evidence generation to AI-informed clinical decision-making interventions. Despite the clear need to reduce the dangerous and costly delays in diagnosis, individual clinicians, especially in primary care, face important challenges. The proposed machine learning algorithms will adaptively update their key parameters as more data are available,” said Chen. “We plan to evaluate these machine learning algorithms periodically to ensure they meet our pre-specified standards and can evolve positively over time.”
Soon, artificial intelligence and machine learning will continue to transform the ability of medical clinicians to make more accurate, data-informed decisions. Smart a.i. And machine learning-based solutions are the future of preventive medicine and impact practice at every touch point along the community healthcare matrix.
How A.I. and Machine Learning Are Used to Predict Chronic Disease Pathologies: Smarter Data Sharing Changes Everything
Artificial intelligence-powered healthcare technologies are speeding up core medical diagnostic processes and even delivering higher accuracy than many human clinicians are able to achieve. Machine learning solutions can rapidly compare millions of different pieces of medical data to quickly and reliably enhance C.T. imaging to deliver better cancer screening processes.
Workflow process automation leveraging artificial intelligence paired with medical robotics augment traditional medical interventions in patient care areas such as daily hygiene, surgical prep, remote patient monitoring, and other vital areas.
By allowing automation to handle repetitive administrative tasks, clinicians have more time with patients. Disease prediction with machine learning is one of the most exciting areas of medical innovation. As larger and larger data sets are shared with these systems, their accuracy, reliability, and validity will increase. The advantage of disease prediction using machine learning is the ability to identify, limit, and even prevent severe pathologies from expanding into chronic diseases.
A.I. and machine learning are being used in many web and mobile applications to build conversational interfaces that better connect patients with the services in their community health settings. As telehealth continues to expand, many providers are finding that by augmenting their care with AI-enabled automation they can more reliably serve patients through virtual and in-person care visits.
Chatbot services provide patients access to life-saving medical insights, guidance, and the ability to diagnose everyday community health issues remotely reliably. These new ai-powered chatbot systems can connect patients directly to pharmacy services after successfully diagnosing a treatable medical problem.
How A.I. Disease Detection Works: SISH (Self-Supervised Image Search for Histology)
On October 10th, 2022, Mahmood Lab of Harvard Medical School published a paper in Nature Biomedical Engineering outlining a novel method for using A.I. to detect diseases called SISH (Self-Supervised Image Search for Histology).
“We show that our system can assist with the diagnosis of rare diseases and find cases with similar morphologic patterns without the need for manual annotations and large datasets for supervised training,” said senior author Faisal Mahmood, assistant professor of pathology at HMS at Brigham and Women’s. “This system can potentially improve pathology training, disease subtyping, tumor identification, and rare morphology identification.”
SISH works similarly to how a search engine works by analyzing different diagnostic images of patients and comparing them to large datasets of disease pathologies and treatments. This application uses deep learning to constantly gain new insights and learn from the ever-expanding medical records database.
This application reinforces why delivering digital health solutions of tomorrow indeed hinges on the ability to share non-identifiable relevant personal health information across the community public health landscape.
How can A.I. detect diseases?
This SHISH application studied by Harvard used 22,385 diagnostic whole-slide images across 13 anatomic sites and 56 disease subtypes. The researchers were able to identify limits but found it delivered reliable and scalable results that vastly improved the detection of many complex disease subtypes such as heart disease, kidney disease, and cancers.
In this application, A.I. detects diseases by comparing images of patients against records of their medical diagnoses. The machine learning algorithm constantly learns how to make better connections between diagnostic ideas and the probability or likelihood of certain diseases from expanding.
In the future, more robust data sharing across borders and around the world will provide physicians with public health insights based on population demographics that we are only beginning to comprehend. The future of A.I. detection and prevention of diseases is bright.
The Future of Preventative Medicine: A.I.-Powered Predictive Analytics Take Center Stage
One of the most promising areas of medical innovation is the application of predictive analytics, machine learning, and neural networks to deliver new ways of interpreting individual and community health.
According to the Centers for Disease Control (CDC), 6 in 10 U.S. adults have at least one chronic disease, and 4 out of 10 with two or more. By 2025, more than half of the U.S. population will suffer from chronic disease pathologies connected to heart disease, cancer, and diabetes.
Preventative medicine is a focal point of value-based care initiatives and person-centered care approaches. Preventive models help patients avoid the onset of debilitating disease pathologies before developing into severe or more chronic syndromes.
Using A.I. to strengthen preventative medical care removes unnecessary diagnostic tests, incorrect diagnoses, or wasted procedures. By engaging patients in their medical care, patient-centered approaches drive measurable cost savings throughout community health systems by dramatically reducing the likelihood of patients being readmitted for easily treatable and preventable conditions.
Ai-driven preventative care extends life expectancy and encourages health and wellness initiatives to focus on enhancing the dynamic quality of life measures reducing the societal burden of chronic illness.
Analytics Models: Predicting and Preventing Disease Using Data Science
Advanced data science capabilities drive innovation at the world’s most forward-looking healthcare enterprises. Predictive and prescriptive analytics are being used to explore larger and larger data sets to identify and correct disease pathologies before they become chronic.
Augmented human intelligence driven by A.I. algorithms enhance the physician’s ability to interpret what is happening inside their patient, enhancing their medical decision-making. Machine learning allows the system to continuously improve by continuously integrating new data-driven insights.
As healthcare population datasets continue to expand, providers have a greater ability to cull actionable insights from personal health information and community health records that allow them to better prevent and limit the spread of disease.
Predictive and prescriptive A.i. analytics ensure medical interventions enhance disease prevention delivering better outcomes for patients and community health providers.
Predictive Analytics
Predictive models take past patient health data to uncover trends to impact and shape future outcomes. Predictive trends show physicians’ risk areas and where chronic disease pathologies could develop if left unchecked.
- Predictive analytics provides healthcare teams with additional insights into how symptoms are driving disease and which interventions are most likely to be effective according to evidence-based insights.
Prescriptive Analytics
Prescriptive human intelligence can combine unstructured datasets to look at broader cultural and socio-economic trends and the environmental factors that drive individual and family health.
- Prescriptive analytics offer insights into how personal health information matches up with macro-level societal, demographic, environmental, and other social determinants of health.
How A.I. and ML Models Use Medical History to Predict and Identify Risks
Artificial intelligence and machine learning models can dive deep into patient records and compare them to family records or insights from the local community and patients with similar disease pathologies, physiology, or many other factors.
A.I. and ML models excel by providing data scientists with many different methods for comparing various forms of information and constructing more vivid models to drive medical decision-making than is possible by merely relying on human diagnostic processing power.
FAQ
- What risk factors predict chronic disease pathologies?
Just a few key risk factors cause most chronic disease pathologies:- Tobacco use and exposure to secondhand smoke.
- Poor nutrition, including diets low in fruits and vegetables and high in sodium and saturated fats.
- Physical inactivity.
- Excessive alcohol use.
- How prevalent is chronic disease in the United States?
According to the Centers for Disease Control (CDC), 6 in 10 U.S. adults have at least onechronic disease, and 4 out of 10 with two or more. By 2025, more than half of the U.S. population will suffer from chronic disease pathologies connected to heart disease, cancer, and diabetes. - How is A.I. being used to predict chronic disease pathologies?
Using A.I. to strengthen preventative medical care removes unnecessary diagnostic tests, incorrect diagnoses, or wasted procedures. By engaging patients in their medical care, patient-centered approaches drive measurable cost savings throughout community health systems by dramatically reducing the likelihood of patients being readmitted for easily treatable and preventable conditions.
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Sindhu
Client Success Manager
Sindhu is a tenacious and impassioned digital product and project manager specializing in driving client success across complex healthcare technology implementations and integrations. She is a certified Agile Scrum Master and holds advanced degrees in computer science and software engineering. Her philosophy is that “work is where the heart is” and believes the key to success is creating a solid, supportive, and cohesive team.
