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How AI is Used in Healthcare?

Healthcare has always been on the cutting edge of technological evolutions. In an industry driven by scientific advancement in order to promote public health and wellbeing, it is no wonder that the healthcare industry and medical professionals were among the first to adopt emerging artificial intelligence (AI) practices. But what does that mean? And where is AI going in healthcare? In this blog, we breakdown the history of AI in healthcare and how it continues to evolve to help healthcare professionals and their patients.

What are the emotional challenges faced by healthcare?

Empathy Burnout in Healthcare Professionals

People working in the healthcare industry have to deal with patients who struggle with emotional or physical pain. This can definitely leave a toll on healthcare professionals that is important to acknowledge and address. Empathy burnout is a common problem. Excessive care for others can make it difficult to prioritize your own feelings and mental health. What can we do to help healthcare providers who are experiencing empathy burnout? Can we prevent empathy burnout altogether?

The Growing Gap Between Patients and Doctors

The doctor-patient relationship has been broadly studied. Doctors who are unable to connect with patients are correlated with diminished paces of prescription adherence. Simply put, when patients do not feel comfortable, they are less likely to adhere to prescribed regimens. Studies show most doctors incline in the direction of a more paternalistic methodology in managing patients. This means that doctors want to guide their patients, build relationships, and be a valuable resource.

Empathy burnout and other daily stressors stand in the way.

This gap in the doctor-patient relationship is extending. It is unmistakable that patients and doctors need tools to help course correct this relationship. So as the centricity of the doctor-patient relationship needs to be embraced, it’s significant it be seen unbiasedly.

What is Machine Learning?

How Machine Learning is Used in AI

Most AI is driven by machine learning algorithms. Machine Learning refers to the action of training machines on data that humans are unable to recognize due to complexity and the sheer amount of data available. Machine learning provides opportunities for software to be more accurate without active human interaction.

ML is extensively characterized as the capacity of a machine to mimic shrewd human conduct. Manmade consciousness frameworks are utilized to perform complex errands in a manner that is like how people tackle issues.

Machine Learning in Healthcare

AI is used in healthcare across various sectors and departments. Administrative healthcare professionals often use AI in Natural Language Processing (NLP) applications to better understand and organize documents. AI can also be utilized to diagnose more quickly and accurately. AI facial emotion recognition pairs well with these commonplace features, adding an extra layer of empathy and compassion to healthcare professional’s tool belts.

How Emotions are Displayed on Your Face

The capacity to discern emotions from people’s expressions is an essential talent. It’s even possible to call it a superpower.

Several tools in the twenty-first century detect people’s emotions.

Emotion Recognition Tools

1) MoodMe

MoodMe is a unique software developer that provides emotion detecting softwares. This sort of software may be valuable in a variety of industries:

  • Business: optimize performance of internal team members and better understand customers
  • Marketing: better understand customer interactions, increase conversions, and insights on marketing direction
  • Education: assist the education of students—particularly in online and remote environments
  • Healthcare: Detect and evaluate patients’ emotional journeys and make their discomfort a little less severe. Doctors can assess what their patients are going through and adjust their treatment accordingly.

2) VoiceAnalytics.io

Voice Analytics.io was designed for call center applications. It is trained on keywords and phrases to detect emotions, pauses, and music.

Responding to Patients with Informed EQ

Each patient has an individual experience that combines mental and physical stress, so it is essential to develop individual approaches and act accordingly. Having access to the patient’s emotional journey and being able to analyze it is extremely important. Doctors will be able to understand what is needed for each particular patient, which will generally help improve the health system as a whole.

The Ethics of AI in Healthcare

Powerful AI technology poses many new ethical challenges that need to be identified and mitigated, as AI technology has a tremendous potential to threaten patient preferences, safety, and privacy.

One of the key questions in this issue is how to measure the benefits and risks of artificial intelligence technologies. The rapid integration of artificial intelligence technologies into the healthcare system has an advantage because artificial intelligence has the potential to improve care efficiency and the quality of patient care.

Another critical issue is the use of unbiased AI databases in healthcare.

The Importance of a Unbiased Database

Algorithmic bias refers to the common and systemic flaws in an automated system that result in inequitable outcomes. This includes favoring one group over another. Bias can be caused by a number of variables, including algorithm design, “training data” used as input, and unexpected implementations.

These risks must be actively reduced. Organizations should consider going forward by subjecting their governance models and processes to more scrutiny, such as that of independent external auditors or even the general public. A thorough examination of the model training data is also necessary, as biases can inadvertently leak. Removing notes detailing race or ethnicity, for example, appears to lessen the likelihood of bias; but, when the real world is separated from the postal code, a person’s address can have sensitive traits and, despite good intentions, yield biased findings.

As a result, having well trained software is critical. MoodMe was founded with ethics and privacy in mind. Our algorithms are trained to recognize various emotions, gender, age, and ethnicity using a diverse and large dataset with more than 1 million faces. With consistent improvement and conscious bias mitigation, MoodMe promises to continually improve and evolve.

Privacy in Medical Settings

Patient privacy encompasses various aspects, including personal space (physical privacy), personal data (privacy), personal choices, including cultural and religious preferences (privacy in decision making), and relationships.

Privacy in terms of care means that everything you tell your doctor, what he writes about you, the medications you are taking, and other personal information is confidential.

Respect for privacy therefore seems to be a very desirable behavior in the areas of organizations. MoodMe’s algorithms all run locally on your device. Faces are never stored and are never sent to the cloud.

The Future of AI in Healthcare

In medicine, AI automates repetitive tasks, supports doctors with diagnoses, develops treatment plans, identifies risks, and streamlines processes. The future of AI for healthcare will help doctors perform specific tasks and give them more time to focus on what matters most. This will happen, at least shortly, when Artificial Intelligence fully assumes the role of the doctor.

The integration of AI solutions in medicine offers great potential and we are only at the beginning. The impact of artificial intelligence on global health care is expected to be huge, but it is clear that much remains to be discussed. addressing the key challenges to public adoption of AI in healthcare and only then can we expect exponential growth in AI for the medical industry and breakthroughs on a global scale.

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