Artificial intelligence (AI) is a key component of the transformational change happening in the healthcare sector. AI-powered solutions in health care delivery, which were previously considered science fiction. However, AI solutions are increasingly embedded into a person’s personal healthcare experience, from initial diagnosis to ongoing treatment.
What was once imagined in sci-fi movies, machines predicting illnesses, personalized medicine adjusted to your genes, is becoming a reality. In this blog, we consider how AI is changing personal healthcare, with real-life examples, ongoing trends, and what is to come.
Real-world Use Cases of AI in Healthcare:
Personalized Treatment Plans Based on Data
One of the most useful applications of artificial intelligence in health care will be its ability to analyze a large volume of patient data in order to produce individualized treatment plans. Traditional medicine has a tendency to apply a blanket approach to most illnesses.
However, AI forces the healthcare system to consider a patient’s individual medical history, genetic makeup, lifestyle habits, and beyond- including characterizations of patient data based on data from individuals in similar situations.
Example:
IBM Watson Health has already started to recommend cancer treatments tailored to patients’ DNA profiles as well as medical histories. By scanning vast amounts of case studies, medical record history, clinical trials, and drug chemistries, AI systems are offering solutions suggested by models that estimate success rates that are better than traditional ways of recommending.
AI-Powered Virtual Health Assistants
At present, healthcare is no longer restricted to offices or facilities. Organizations are developing real-time virtual health assistants using AI and can now communicate with patients at a site or location selected by the patient.
The virtual health assistant can remind patients when to take their medication, keep track of symptoms, offer mental health assistance, and even escalate if there is an emergency.
Example:
Some apps like Ada, Babylon Health, and Health Tap have created AI-enabled chatbots in order to provide symptom checking and medical guidance to patients.
Predictive Analytics for Early Diagnosis
AI is good at finding patterns, especially patterns that humans find subconsciously or are incapable of perceiving. Predictive analytics supported by AI can help identify the early onsets of disease, including diabetes, heart disease, Alzheimer’s, and cancers, amongst other illnesses.
Example:
Google’s DeepMind has developed an AI model that can identify over 50 different eye diseases measured with nearly expert accuracy. On the other hand, Zebra Medical Vision is using AI to read X-rays and CT scans to assess whether any diseases may creep in without being detected.
Wearables and Continuous Monitoring
Wearables ranging from fitness trackers to smart watches can track many more things than just step counts at this point. Wearables that utilize artificial intelligence (AI) algorithms are able to interpret real-time data from wearables to track vitals like heart rate, oxygen saturation & sleep cycles.
This has enabled continuous health tracking and monitoring. We can now move forward with chronic condition management and in preventing health conditions.
Example:
Apple Watch and Fitbit, using AI, will be able to detect when users are having abnormal heartbeats or when they have unusual patterns of their sleep and stress. Alerts sent to the user from their wearable device may cause a user to pursue medical attention sooner than normal.
AI in Mental Health and Emotional Wellness
AI is taking a role in mental health well-being to assist with tools to detect emotional distress, depression, and anxiety early. AI-enabled chatbots and emotion tracking mobile apps use natural language processing (NLP) to detect cues based on users’ interactions with their type or talk and behaviors behind their choices.
Example:
Woebot is an AI-based mental health chatbot that is capable of having therapeutic conversations with users to utilize cognitive-behavioral therapy (CBT), while also tracking the users’ emotional tone to offer tools for managing anxiety or depressive thoughts.
Smart Drug Development and Personalized Medicine
AI is markedly shortening timelines and costs in drug discovery. The algorithms can quickly analyze what compounds will do in relation to biological systems, thereby quickly finding therapeutics. AI is being used to customize medications based on unique genetic markers to provide better efficacy.
Example:
Atomwise uses AI to help predict the manner in which new drugs will interact with specific proteins in the human body, which greatly helps to quickly identify new drug candidates worth testing. To date, Atomwise has found new therapies for a variety of diseases, including Ebola and multiple sclerosis.
AI and Genomics: Mapping DNA for Future Health
Genomic data can be used to help predict a person’s susceptibility to a number of diseases. There are AI systems currently being trained to sift through massive genetic datasets in order to identify mutations, patterns, and being able to connect them to hereditary conditions.
Example:
AI and machine learning based data tools are being employed by genomic testing platforms like Helix and 23andMe to provide personalized reports that show disease susceptibility, dietary needs, and fitness recommendations based upon an individual’s DNA.
Real-Time Disease Surveillance and Public Health
AI is being applied to identify trends in public health and predict potential outbreaks. AI can measure location from hospitals and/or wearables, and even keep track of social media to allow for health systems to take immediate action before the spread of disease and symptoms can accelerate. This is important in the new world we are living in after a pandemic.
Example:
BlueDot, a Canadian start-up, developed an AI platform to identify the early onset of COVID-19 in a way that had not yet been publicly identified by W.H.O. or other official sources. If predictive systems were available in every country, we might be able to not only prevent future pandemic scenarios but also act on outbreaks quicker.
Enhancing Radiology and Medical Imaging
AI is influencing radiology by working with doctors to interpret complex images such as MRIs, CTs, and X-rays. AI-based applications are being utilized to reduce the possibility of reporting errors and ultimately increase efficiency by reducing report time (in situations where the doctor perceives time pressure or sees high volumes of patients) by providing assistance for decision making that could be timely for patient outcomes.
These applications employ deep learning to review thousands of labeled medical images (that had been previously evaluated by humans) to identify issues such as tumors, fractures, and infections, while serving as a second set of eyes for the radiologist. AI detection and diagnosis systems have been utilized to improve detection rates, but not reduce human interpretation.
Example:
Qure.ai’s tool helps radiologists find 30+ conditions in chest x-rays, while also triaging patients with the most severe conditions. For example, Aidoc’s AI can alert clinicians to acute changes as they begin to develop, like an expanding bleed in the brain, which may ultimately be faster for the clinical to evaluate and respond.
This allows radiologists to be more effective by reducing unwarranted delays and improving outcomes for patients.
Patient Engagement and AI-Driven Healthcare Portals
AI has driven improved patient engagement through personalized health portals and interactive engagements. Individuals utilize AI-powered platforms with health portals to access records, schedule appointments, track wellness, and engage with assistance, all powered by algorithms that learn and develop based on user engagement (to improve over time).
Example:
The Mayo Clinic portal uses AI to personalize health tips, reminders, and appointment suggestions by learning from how users behave in the portal and who they are. This presents an opportunity for more proactive healthcare and a more tailored patient experience.
Platforms created using white-label solutions are being utilized to implement such AI-based functionality across healthcare apps reliably and securely.
Conclusion:
AI is no longer a complementary approach to our healthcare system. It is fast becoming the foundation of every modern personal health system. AI is shifting our diagnostic process, and it is shifting the manner in which we evaluate our health each day, particularly with respect to mental wellness, social cohesion, and disease surveillance.
Healthcare will be even more accurate, personal, predictive, and preventative as these technologies continue to advance. And it is critical we prepare for a radical shift diametrically; the experience of healthcare will not just be actionable; it will remain inevitable, and enter the world of personal health care powered by AI.
If you’re planning to build AI solutions for healthcare, partnering with an experienced AI development company can help bring your vision to life at a budget-friendly price.
