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Artificial Intelligence in Healthcare

artificial intelligence in healthcare

Artificial Intelligence in Healthcare

Artificial intelligence (AI) can be described as a range of different techniques that allow the computers to perform a specific task requiring human power-solving skills and reasoning. Artificial intelligence and its related technologies have been becoming prevalent in society, business, and even the healthcare system. These AI technologies are having high potential for the transformation of patient care and administrative processes as well.

 

According to several types of researches conducted on AI in the healthcare system, AI is proved to be efficient in performance in healthcare tasks, specifically “diagnostic testing.”

 

In today’s age, algorithms are replacing radiologists for the identification of malignant tumors, and the guidance of researchers to conduct a clinical trial.

 

Artificial intelligence is not only one technology; instead, it is a collection of technologies. Some of the most particular AIs relevant to the health care system will be discussed in this article:

Machine learning:

In the field of healthcare, AI’s application of machine learning is the most commonly observed in precision medicine, which refers to the prediction of prognosis and choice of treatments according to patient-specific requirements.

 

A neural network is a more sophisticated type of machine learning which has been available since the 1960s. This system is linked to the processing of neurons signals and functioning of the brain, which determines whether a specific patient will get a specific disease.

 

Deep learning is the most common type of AI machine learning with several variables or features that help in predicting the outcomes. A most common application of in-depth knowledge in health care is potentially cancerous lesions recognition. This application is being used in radiomics for the detection of features and characteristics of data observed in imaging, which is otherwise beyond the perception of a human eye.

In an analysis of oncology-oriented images, deep learning and radiomics are both widely used. The combination of these two applications gives a promising accuracy for diagnostic purposes in comparison to previously used generations as CAD (computer-aided detection).

Robotics:

Physical robots are recognized for their ability to perform some predefined tasks as repositioning, assembling of objects, welding, and lifting. Recently, robots have started their collaboration with humans to perform a specific or desired task.

 

In the 2000s, the USA approved the introduction of surgical robots in the healthcare system to provide some superpower to the surgeons for improvement of their abilities to see deeply, to create minimally invasive and precise incisions, to stitch the wounds accurately and a lot more. However, the most common surgical procedures performed with the help of robotics are gynecological surgeries, prostate surgery, and neck and head surgery.

Robotic process automation:

This technology is used to perform digitally structured tasks for several administrative purposes. In comparison to other AI forms, they are a bit inexpensive, transparent, and easy to program for their actions.

In the health care system, this automation is used for repetitive tasks as prior authorization and updating of patient’s billing or records. If combined with other technologies such as image recognition, this automation can be helpful in the extraction of data as faxed images to input them into the transactional system.

Natural language processing:

Since the 1950s, the goal of AI is to make sense of human language. NLP (natural language processing) includes applications as text analysis, speech recognition, and several other goals associated with language.

 

There are two fundamental approaches to this processing.

  1. Statistical NLP
  2. Semantic NLP

 

In the field of health care, NLP’s dominant applications include classification, creation, and understanding of published researches and clinical documentation. NLP systems can also analyze the unstructured clinical notes of patients, can prepare reports as radiology examinations, can transcribe the patients’ interactions, and conduct conservational artificial intelligence.

Interventional and diagnostic applications:

Since the development of MYCN in the 1970s at Stanford for diagnosis pf blood-borne bacterial infections, intervention and diagnosis of diseases have been a focus of artificial intelligence.

In the most recent times, IBM’s Waston has been receiving considerable attention for its ability to focus on precise medication specifically for treatment and diagnosis of cancer.  In this system, Watson employed a combination of NLP and machine learning capabilities.

 

However, Watson is not a single product; instead, this is a set of “cognitive services,” which is provided through APIs (application programming interfaces), including language and speech, machine learning based-programs for data analysis, and vision.

 

In recent researches, AI is observed to be able to diagnose and intervene in the disease with greater or equal precision and accuracy in comparison to human clinicians. A lot of these findings are more focused on radiological images analysis through retinal scanning or precision medicine (genomic-based).

 

As these findings are based on statistical machine learning models, they are giving rise to evidence-based and probability-based medicines, which is a positive aspect of artificial intelligence.

Health monitoring through personal devices:

Almost all the population has access to specific tools having sensors to collect the data about their health. Ranging from smartphones to wearables, which can track the heartbeat 24 hours a day, can generate vast data just on the go.

 

Artificial intelligence is playing a significantly important role in the extraction of actionable insights from this varied and large treasure of the data.

 

In the field of health care, smart devices are very critical for patients’ monitoring either in ICU or anywhere else. The use of artificial intelligence can enhance the ability of deterioration identification, tracking of sepsis, or the estimate of complications development, which can efficiently improve the outcomes and reduce the costs and expenses related to hospital-related penalties.

An example of connected health in digital medecine: PODOSmart

 

PODOSmart® is a solution using artificial intelligence algorithms that allows paramedical professionals to analyze the patient’s gait and refine their diagnosis with 13 biomechanical data.

PODOSmart® also helps to detect mobility disorders such as limping or abnormal changes in prono-supination angles in less than 30 seconds. PODOSmart® is a CE certified solution recognized as a medical device.

Web interface podosmart

Immunotherapy advancements:

Immunotherapy is the most promising treatment for cancer.  By the use of a body’s own immunity to attack foreign bodies, malignancies, and diseases, patients might be able to fight against stubborn tumors. However, in the recent past, it was difficult for physicians to find a more precise and accurate method for the identification of patients suitable for this treatment.

 

Machine learning algorithms and their capability to produce highly-complexed data sets can illuminate the new options for giving therapies according to the patient’s genetic makeup and requirements.

 

In the most recent innovations, the most exciting invention in the development of checkpoint inhibitors. These inhibitors block some proteins made up of immune cells.

Revolutionizaton of clinical decision making:

Artificial intelligence is providing a more powerful predictive analytics and tools to support clinical decisions which can clue the physicians for the problems long before they occur.

 

AI system can also provide early warnings for several diseases as sepsis, seizures, which require highly-complexed datasets and intensive analysis fo the patients’ profiles.

 

Machine learning can also provide support for decisions about whether a critically ill patient should continue the specific care or not.

 

Typically, EEGs are the data obtained from patients who are interpreted through physicians. Interpretation of their results is a subjective and time-consuming process that is based on experience and the skills of the specific physician. But, with the help of AI algorithms in this field, it has become easier to effectively and accurately interpret them, and now it is easier to compare and match what you are seeing and detect even the subtle improvements which can significantly influence health care decisions.

Conclusion:

With the introduction of more innovative and new generation AI tools, healthcare is more advanced in the sense of more awareness, efficiency in delivering care, identification of developing complications, accurate diagnosis of diseases ahead of time, and most recent approaches for interventions. AI can bring an era of exceedingly high clinical quality with extraordinary breakthroughs in the care of patients.

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