The Covid-19 Pandemic has turned our world upside down and has taken a significant toll on almost every aspect of our lives. While it had a significant detrimental influence, it also positively changed our culture. For instance, it greatly hastened the digital revolution. Every industry is now more reliant on technology as a result. There is no turning back from this point on!
The healthcare sector is at the core of all industries for obvious reasons. When the infectious coronavirus was killing people worldwide not too long ago, the healthcare industry worked nonstop to limit the damage. Because of all the commotion, telehealth became essential, increasing the use of artificial intelligence in the medical industry.
In recent months, the role of artificial intelligence in the medical field has been one of the hottest talking points, and the adoption of this tech is not showing any signs of slowing down. The healthcare sector relies on AI applications for better decisions, managing patient data, establishing personalized medication programs, identifying novel medications, and much more since AI can make medical care more human with enhanced accuracy and efficiency.
Use cases for AI and digital health
As more researchers and healthcare startups look for innovative ways to enhance patient care and support healthcare professionals’ delivery, there has been an upsurge in the use cases for artificial intelligence in healthcare. We may anticipate even more radical changes in how healthcare is delivered as AI technology develops. Among the significant new applications of AI in digital healthcare are:
Blood pressure monitoring
An important aspect of cardiovascular health is blood pressure. The chance of acquiring life-threatening medical conditions, including heart disease and stroke, can rise in those with high blood pressure (hypertension).
In the doctor’s office, checking someone’s blood pressure is a standard checkup procedure. If you need to check your blood pressure readings frequently, you would be familiar with the cuff and equipment used to test it, and you might even have one in your house. These techniques are still crucial for taking blood pressure, but they are only sometimes precisely because of the wearer’s movements or how well the cuff fits.
The startup, an AI solution called CardioX, uses an ECG recorded by a wearable device to assess a person’s blood pressure. It offers to email the data to the attending doctor. Although this method needs cuff-based instruments to produce accurate measurements, it frequently provides information. It highlights variations in the individual’s blood pressure, enabling earlier diagnosis and treatment of illnesses like hypertension.
- Analyzing blood sugar levels
Consistently high blood sugar levels can harm vital organs, the brain system, and the eyes, in addition to causing health issues. Analyzing blood sugar levels is crucial for people with diabetes as a result. For many years, finger pricking has been the primary technique. However, finger pricking is not always convenient, and sometimes people forget to check their blood sugar levels. This is about to change for those with the illness thanks to new wearable technology and AI.
The GluCare system is one instance of a wearable AI gadget. The device can be worn on whatever body region is most comfortable for the user. The wearable continuously tracks the user’s blood sugar levels and utilizes artificial intelligence to notify the user when levels decrease or rise. Doctors can subsequently use the information gathered to guide their patients’ treatment decisions.
- Diagnosing diseases from chest x-rays
Medical professionals frequently use X-rays to detect and identify lung cancer and other respiratory illnesses. To identify health issues on x-rays, radiologists can use AI in conjunction with radiography. AI analyzes photos more quickly than humans, pointing up areas of concern so a doctor can look at them more closely. While doctors’ trained eyes can spot abnormalities, some may be difficult to spot on a scan, or the doctor can benefit from a program to help them if they are pressed for time.
Taking the x-rays themselves is an additional aspect of AI and radiology. Similar to the blood pressure cuff, situating the patient and equipment correctly can impact the quality of an x-ray. Carestream is one business that seeks to enhance imaging with sophisticated solutions that guarantee proper equipment location and settings. Again, radiologists already have these abilities, but they might significantly increase patient throughput and accuracy.
AI adoption challenges in the digital medical sector
So, what stops us from adopting AI solutions if beneficial use cases like these exist?
- Data limitations
Data availability is one of the obstacles to adoption. AI needs data to be trained, but healthcare data is challenging and cannot be considered anonymous even after being de-identified. Before the widespread adoption of AI in healthcare, these ethical and privacy problems must be addressed.
The absence of standards is another obstacle. Healthcare data is frequently unstructured and differs from one facility to the next. Different labs utilize various measurement tools with various normal operating ranges. Because of these, it is challenging to create models that can be applied to multiple datasets and institutions. Interoperable healthcare solutions would significantly aid in resolving this issue.
- Compliance with regulations
While AI has enormous promise to change healthcare and enhance patient outcomes, several regulatory obstacles must be removed before it can be broadly used. Strict compliance with healthcare data standards introduces new technology, including AI, to market challenges. This is related to data privacy once more. Many of these rules are concerned with protecting patient information and ensuring that it can only be seen by and shared with specific individuals.
Another challenge is the review and approval processes that healthcare technology must undergo before being released to the public. The time it takes for such items to reach the market increases since new AI solutions must be validated through clinical research. We require a solution that makes it simple for healthcare technology companies to promote their goods without compromising patient security.
Future of AI in Healthcare
Artificial intelligence research in the medical field is expanding quickly. The following are (at least) forecasts for the trends that will be most popular in the future of AI in healthcare:
- Medical Diagnosis of AI. Diagnosis prediction is one of the primary areas where AI will have a substantial impact. Future machine learning models will make it possible for AI medical forecasts to identify medical issues before they manifest themselves.
- Telemedicine. In the future, people will be able to receive hospital-quality care in the comfort of their own homes thanks to machine learning-based models. Advanced AI chatbots can advise patients on their medical care and gather information from them regarding their symptoms.
- Electronic Health Records (EHR). Although electronic health records and artificial intelligence are not entirely related, they will play a significant part in the future of healthcare. EHRs are nothing more than the patient’s medical records in digital form. It contains information on the patients’ 10-year medical background, health trajectory, and diagnoses. They are enormous databases that can be mined for vital patient information using AI and machine learning approaches.
Wrapping Up
In short, healthcare is one of the most significant industries that artificial intelligence will transform. The industry has already committed trillions of dollars to work with AI, demonstrating the magnitude of AI’s influence on the healthcare industry.
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