The scope of artificial intelligence and machine learning in healthcare looks promising in the forthcoming decade. From early cancer detection to robotic-assisted surgery, AI is helping medical professionals with its predictive power, today and tomorrow.
With AI taking the center stage in various clinical settings, it will soon be impossible to imagine a visit to the doctor, or avail healthcare facilities that are not impacted by it. Besides, AI coupled with ambient sensors will help doctors shift their focus from less paperwork and admin functions, and more on patient care.
Fast forward to a decade from now. Imagine it’s a cold January day and the flu season, at its peak. Unlike in 2020 where hospitals and clinics were typically overcrowded with patients waiting for treatments, today, (2030) patients and clinician interactions are seamless, hassle-free, and organized with less waiting time involved.
What will bring about this change?
Driven by years of immense pressure on global healthcare due to the shortage of medical professionals in the forthcoming years, connected care powered by AI and data science will soon become a reality.
Let us read how artificial intelligence is set to disrupt the healthcare industry in 2020 and years to follow:
Table of Contents
Increased application in imaging diagnostics
According to the World Health Organization, every year, approximately 17 million people die of cardiovascular diseases, heart attacks, and strokes in particular. Therefore, it has become increasingly important to find a way that facilitates the timely detection of diseases while reducing the burden on healthcare organizations. Symptoms like chest pains compel patients to undergo invasive diagnosis, and in more than half the cases, patients have no significant blockage, deeming the test, unnecessary.
Some healthcare equipment providers have already come up with a non-invasive diagnosis that has helped medical experts and patients with detecting the severity of the condition at a lower cost and without causing any discomfort to the patient. The number of MRI and CT scans are on the rise, and AI has the potential to read scans as accurately as an expert physician. In the coming years, AI and ML will review scans for any disease thereby reducing patient waiting time and easing up the medical staff to focus on treatments.
Predictive nature prevents chronic illness
understanding of the variable factors that influence our health. It not only takes into account the prognosis of an illness, but includes the social determinants of health (SDOH) as well. These factors include medical inheritance, lifestyle, work environment, local pollution levels, access to a safe living environment and a stable income, etc, to anticipate chronic diseases and suggest preventive measures beforehand.
Healthcare systems are anticipating that by 2030, predictive analytics of AI will help detect a person who is at risk of developing a chronic disease, and suggest preventive measures before they get worse. There have been successful developments in early detection of chronic diseases like diabetes, COPD ( chronic obstructive heart disease), and congestive heart failure, that are strongly impacted by SDOH, and are finally on the decline. Data science platform like ClosedLoop is providing health care organizations, a flexible analytics solution powered by AI, that enables hospitals to integrate their data effortlessly into machine learning models and get actionable results.
Digital biomarkers to detect neurodegenerative diseases
Biomarkers are biological markers, and are used as an indicator of a biological process inside the human body. Unlocking new biomarkers is crucial in medical research for early diagnosis of diseases. They are used effectively in the detection of neurodegenerative diseases like dementia and Alzheimer’s.
Neurologists are focusing on how ML can help analyze the scans of cerebral cortex scans for accurate and early detection of Alzheimer’s. Dementia, a complex neurological disorder, demands more comprehensive collaboration between biomarkers and AI to evaluate the progression with higher precision, and develop targeted treatments. Biomarkers powered by AI can enrich cognitive test scores using metrics that are unique to the patient like his/her voice that acts as indicators when they are struggling to perform a given task. Patients who are performing within normal ranges, but are still struggling to overcome the challenges of the task, are most likely, suffering from the early stages of the decline. This early detection can help slow down or prevent further neurodegeneration altogether. In the next couple of years, digital biomarkers will establish a medical breakthrough in improving patient outcomes.
The centralized infrastructure for better care
It is estimated that a decade later, hospitals shall no longer exist as one big building that treats multiple diseases. Instead, they will only treat patients with acute illness and prioritize complex surgeries, while less urgent cases shall be monitored at pharmaceutical clinics, same-day surgery centers, or even at the patient’s home. These medical facilities shall be connected to a centralized infrastructure powered by AI.
This network will help identify patients based on their deterioration risk in real-time, and ensure patients and medical professionals are directed to the best treatment available. This enhances the experience of patients, as well as the clinicians who are experiencing high levels of burnout by attending too many patients with fewer resources. By 2030, AI-powered predictive healthcare networks will reduce wait times, improve staff workflows, and also manage the administrative busywork.
Although today, we are far from achieving this vision, we are certainly on track with these ideas soon becoming a reality.
AI is already augmenting human capabilities by detecting cancerous lesions on an image, analyzing and assessing physician notes, or optimizing patient flow in emergency care. The predictive power of AI has helped cure critical patients inside the ICU as well as identify at-risk groups so that preemptive care and primary care can prevent unnecessary hospital admission.
The global market for AI in healthcare is expected to grow almost 10 times by 2026. Joint efforts of the government, private organizations, and medical research foundations will ensure AI-powered medical systems are transparent, fully interoperable, and prevent any kind of bias and inequality.