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Artificial intelligence in healthcare

COVID-19 has shed light on the most important gaps in global healthcare. It became obvious that innovations were required to make the industry more effective, and artificial intelligence (AI) could influence healthcare profoundly. As mentioned by Dr. Tedros Adhanom Ghebreyesus, the Director-General of the World Health Organization, only after the spread of COVID-19 it became obvious that countries didn’t have the necessary capacity to collect and use health data, especially in the midst of an ongoing crisis. 

One of the first responses of AI to the global pandemic was the collaboration of TytoCare and Sheba Medical Center in Israel. They supplied patients with special stethoscopes that listened to their hearts right in their homes and transmitted the images of the patients’ lungs to the special care system to spare the diagnostic time. In this blog post, Innowise Group will highlight how the medical field can benefit from AI in the post-pandemic period and bring more interesting examples of a similar kind.

What is artificial intelligence in healthcare?

Hospital staff and physicians can obtain reliable data-driven CDS (Clinical Decision Support) through machine learning. In particular, artificial intelligence technologies can efficiently analyze health records and images, as well as clinical trial data. As a result, healthcare organizations can improve both diagnostics speed and quality, thus saving more lives.

AI in healthcare

How AI is changing healthcare

There are several benefits of AI in healthcare, including automating tasks, faster and more efficient diagnostics, and safer surgeries. 

For instance, wearable healthcare technology that uses AI analyzes data and informs the user and the healthcare provider of potential health-related risks and issues. FitBit, one of the most notable names in the industry, has developed an advanced health watch Sense to track such metrics as oxygen saturation, skin temperature, stress level, high or low heart rate, breathing rate, sleep and awakening, snoring, complexion, and even menstrual health in the real-time mode. A customer gets the valid data right on the FitBit app installed on a smartphone and can share the data with a healthcare provider for further consultation or diagnosis.

Another successful example of AI implementation in practical surgery is the robot developed by Borns Medical Robotics. It can perform minimally invasive surgeries remotely. The robot can be particularly useful for conducting surgeries in isolated areas, such as those affected by war. London-based Digital Surgery medical tech company, meanwhile, has introduced artificial intelligence that guides surgeons through every step of an operation, significantly reducing the possible fatal outcome.

One of the cases of AI’s brilliant incorporations into healthcare dates back to 2016. University of Iowa Hospital and Clinics implemented AI tech into their surgical procedures. It predicts the probability of infection during the surgery even before the doctor closes the wound. As a result, the hospital has reduced post-surgery infection by 74% and saved $1.2 million.

Insider Intelligence states that around 30% of healthcare expenses are related to administrative tasks. AI facilitates pre-authorization insurance, as well as quickly checks on unpaid bills, helps process patients’ records, and thus simplifies the workload of hospital staff. 

AI in healthcare examples

Artificial intelligence for healthcare is about to revolutionize the industry and assist healthcare providers in addressing upcoming challenges. Here we have selected the most vivid AI in healthcare examples from real life.

Machine learning and deep learning

In healthcare, machine learning techniques can be applied in many innovations. For instance, according to Mercury DataScience Portal, machine learning (ML) is expected to significantly improve central nervous system clinical trials, given the difficulty in diagnosing CNS disease progression. Machine learning is capable of making the most accurate predictions of future outcomes via rules-based logic and pattern recognition. This, in its turn, reduces the time and costs in the execution of clinical trials.

Another successful example of ML and DL (deep learning) in healthcare is that of Subtle Medical. The company provides clearer medical images for radiologists. Its product SubtleMR blocks image noise, focusing on areas such as the head, neck, breast, and abdomen. As a result, radiologists acquire higher-quality images.

Deep Learning networks enhance clinical practice, in particular, DL algorithms are widely used for diabetic retinopathy detection. For instance, by building a Convolutional neural network, Aravind Eye Hospital will be able to estimate the severity of the patient’s blindness by simply looking at the eye.

Moreover, as stated in the study posted on HealthITAnalytics, CNNs (convolutional neural networks), based on DL, identified melanoma dermatologic disease with more than 10% accuracy than experts.

AI in healthcare

Physical robots

Robots have been around for quite a long time performing various actions from lifting goods to delivering supplies. AI chatbots are perhaps the most popular ones. Today, the probability of incorporating them into the healthcare industry is more palpable. For instance, since 2000, surgical robots have been approved in the United States as “boosters” for doctors. For instance, they can stitch wounds most accurately or create invasive incisions. Perhaps the most common surgical procedures with the participation of physical robots (surely, important decisions are yet after the doctors) include prostate, gynaecologic, as well as neck and head surgeries.

Diagnosis and treatment applications

Artificial Intelligence was first implemented in diagnosis and treatment back in the 1970s with MYCIN-diagnosed infections of blood-borne bacterial origin. However, it remained at Stanford and did not reach clinical practice because of insufficient power. The situation has changed dramatically with technological advancement. Almost every week, forefront AI companies launch AI applications in healthcare for diagnosis and other medical treatment purposes, ensuring equal to human or even more accurate results. 

At the same time, such apps mostly address only a single aspect of care rather than complex issues. Yet, there are some exceptions. MySugr Diabetes tracker app allows users to insert their daily blood sugar, bolus, carbs, and estimated HbA1c (Glycated Hemoglobin) at once. This way, the patient gets better control over their condition and can pass the information to their doctor for more effective treatment.

Virtual reality in healthcare

Virtual Reality solutions enable patients and healthcare providers to interact with simulated environments. This may involve pain management and rehabilitation, as well as surgery training. And if the estimation of the global healthcare VR market is approximately $2.07 billion in 2022, by 2026, it is projected to reach $9.25 billion, according to ReportLinker

VR can be implemented in the AI for healthcare in several ways:

  • Medical education: Theoretical learning is backed by detailed 3D medical models. Most often, it will be applicable in anatomy learning.
  • Medical training: By switching the simulated environment in a VR app, users virtually participate in a training scenario. The most common cases of VR medical training include medical procedure simulation, testing medical equipment, and emergency case simulation.
  • Surgery: VR reduces functional skill deficiency quicker. It’s used in surgical training and pre-surgical planning.
  • Pain management: VR diverts a patient’s attention, thus lowering the extreme discomfort and level of pain and minimizes the use of painkillers, thus reducing healthcare expenses. As highlighted in Net Health, VR allows patients to experience their preferable interactive environments, thus helping them to battle pain and discomfort.  In most cases, the efficacy of VR in pain management is vivid through acute pains, chronic pains, stress, mental health, and medical procedure pain.
AI in healthcare

The future of AI healthcare

We believe that artificial intelligence will have a huge role in the healthcare industry. The development of artificial intelligence solution companies for healthcare and AI integration into hospital systems will bring about dramatic changes in hospital patient health outcomes.

However, the biggest challenge of artificial intelligence in healthcare is not whether the apps will be useful and powerful enough to deliver accurate results but rather ensuring they are adopted in daily clinical practice. AI apps must be approved by regulations, taught to clinicians, and accepted by the population.

While we can expect palpable use of artificial intelligence in clinical practice within five years, it becomes obvious that AI systems can’t substitute human clinicians completely. On the contrary, they will only enhance and facilitate healthcare.

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