Artificial intelligence is starting to make inroads into healthcare

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Artificial intelligence
Artificial intelligence

We don’t even realize it, but today we regularly use artificial intelligence (AI) on social media to navigate ourselves while driving or shopping online. But where we will increasingly use AI is in healthcare. Already today, it has excellent results, especially in disease diagnosis, treatment management, pharmacy, etc.

According to a number of ongoing studies, using ai and machine learning in medical devices can perform tasks such as disease diagnosis at least as well as or better than humans and also artificial intelligence in business has improve the online presence of businesses.

However, the enthusiasm for the rapid use of AI should be tempered by the fact that it must go through expensive clinical trials and approval processes before being deployed. So let’s take a look at the potential that AI offers for healthcare, as well as some obstacles to its faster adoption.

What types of AI are relevant to healthcare?

Artificial intelligence is often spoken and written about in the singular. In reality, it consists of a set of technologies, most of which are relatively directly relevant to healthcare programs.

Related: Software Development Trends to Revolutionize the Healthcare Sector

Machine learning, neural networks, and deep learning

  • Machine learning (ML) is the adaptation of data models using statistical methods. In healthcare, traditional machine learning is most typically used in predictive medicine. That is, determining the probability that, based on various attributes and treatment context, specific treatments will be effective for a patient. The vast majority of machine learning and precision medicine applications also require data on which systems are undergoing further training under supervision.
  • Artificial neural network (ANN) technology has been successfully used in medical research for several decades. They are typically used to classify data to determine the likelihood of a patient contracting a particular disease.
  • A comprehensive deep learning (ML) tool with the ability to self-learn from increasingly large data sets is increasingly being used, for example, in radio networks or image data analysis, where it is used to detect clinically relevant image patterns that are beyond what the human eye can perceive. It is also very commonly used in oncology to recognize potential cancerous growths in X-ray images.

Related: Advances in machine learning in different areas of our lives

Natural language processing

  • Deep learning is also used for speech recognition in the form of natural language processing (NLP). This includes applications such as speech recognition, text analysis, translation, and other language-related uses.
  • In healthcare, the dominant use of NLP applications is to create, understand, and classify clinical documentation and to process published research. Thus, NLP systems can be used to analyze unstructured clinical records of patients, prepare reports (e.g., radiological examinations), transcribe patient interactions during examinations, etc.

Diagnostic and therapeutic applications

  • Diagnosing and treating diseases has been a subject of interest in AI for decades. However, these systems have not been widely adopted in clinical practice. On the one hand, they were not significantly better than live diagnostics, and, on the other hand, they were poorly integrated into clinical workflows and patient record management systems.
  • However, this is starting to change, even if it is now more about standalone installations in research laboratories and technology companies. At the same time, researchers are focusing more on analyzing incoming images, which are mostly radiological images, retinal scans, or images of genomic structures.
  • Large tech companies and startups are also working on their own AI solutions. Google is collaborating with healthcare providers to create predictive models that alert doctors to high-risk conditions such as sepsis and heart failure. Other companies, such as Langate and a number of other startups, are developing algorithms to interpret images using AI. Other companies are focusing on diagnosing and treating certain types of cancer-based on genetic profiles.
  • Predictive models are also used by health insurance companies and healthcare providers to predict the risk of certain diseases in the population or to predict the readmission of patients to hospitals.

Application to patient engagement and treatment adherence

  • Patient engagement and adherence to treatment is considered significant issue and challenge to achieving effective healthcare. Active patient engagement significantly improves patient outcomes, healthcare utilization, and financial performance.
  • A promising area of research is the connection between machine learning and population health management systems. Such systems allow for the preparation of personalized alerts, messages, and other relevant targeted content for individual patients, which will trigger the necessary actions in patients at important moments.
  • Designing a behavioral “Choice Architecture” is gaining more and more attention. It is used to develop structures for personalized choices of options and functions to ultimately change patient behavior in the expected direction. Healthcare provider systems can create personalized recommendations in collaboration with patient biosensors, smartwatches, smartphones, and other tools. Recommendations can also be shared with other healthcare providers and patients, employers, family members, etc.

Administrative application

  • Although the use of artificial intelligence is somewhat less revolutionary in the field of administration compared to patient care, on the other hand, it can significantly increase efficiency and increase the required capacity. They are very much needed in the healthcare industry.
  • The technology of robotic process automation (RPA) is relevant to this area. It can be used for a range of activities where decisions can be made based on clear criteria. This can include, for example, processing clinical documentation, economic plans and reports for health insurance companies, or managing medical records.
  • Automation of administrative processes in combination with cognitive technologies (machine learning, artificial intelligence, or natural language processing) can be just as progressive. Healthcare organizations are gaining experience in using chatbots to interact with patients during remote triage to organize appropriate treatment. Thus, these NLP-based programs can be useful for organizing doctor’s appointments or filling out medical records.

Impact on healthcare professionals

Of course, a lot of attention in connection with the introduction of AI in healthcare is paid to fears that this technology will lead to process automation, job destruction, and workforce reorganization. The most likely to be automated is the work with digital information, not the work in direct contact with the patient. On the other hand, there are real opportunities to create new jobs for the work and development of AI technologies.

Ethical implications

  • The intention to use AI in healthcare raises a number of ethical issues. Healthcare decisions today are made exclusively by humans, and the use of intelligent technologies to make them calls into question accountability, transparency, patient consent, and patient privacy.
  • Perhaps the most challenging issue that modern technology addresses are transparency. Examination results processed by artificial intelligence algorithms, especially deep learning algorithms used for image analysis, will be almost impossible to interpret or explain to patients. AI systems will also undoubtedly make mistakes in diagnosing and treating patients, and it may be difficult to determine who is responsible for them.
  • As AI is introduced into healthcare, we will face many ethical, medical, professional, and technological changes. It is important that healthcare institutions, governments, and regulators establish standardized structures to monitor key issues, respond responsibly, and establish ongoing control and governance mechanisms to limit negative impacts.

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