AI-Analyzed Crowd-Sourced Data in the Medical World



  • The data is then analyzed by a machine-learning (ML) algorithm to identify the most effective and most tried treatment for a particular disease.
  • The use of AI in the medical world is not limited to automating routine administrative tasks and modernizing healthcare organizations.
  • Crowdsourcing is the technique of gathering information and insights about a particular topic from the public through the internet.

Technology has always played a key role in advancing healthcare and medical research. From the use of sophisticated surgical techniques and equipment to the application of IoT and big data analytics – modern technology is integral to medical science.

One of the most important tech innovations revolutionizing the medical world is artificial intelligence (AI). According to a Deloitte survey from 2019, 95% of mid-sized healthcare organizations in the U.S. invested nearly $50 million to procure and deploy AI solutions.

AI in Healthcare: A Closer Look

The most common goals healthcare organizations aim to achieve through AI include:

  • Enhanced efficiency
  • Increased patient satisfaction
  • Reduced costs
  • Improved services

The use of AI in the medical world is not limited to automating routine administrative tasks and modernizing healthcare organizations. Medical practitioners can use AI-powered devices to monitor patients’ vitals and identify impending health risks.

AI-driven solutions can even help predict whether a patient is likely to develop chronic ailments, such as heart disease, diabetes and hypertension. They can also identify symptoms of heart attacks or strokes in patients and alert their physicians.

Similarly, AI has played a key role in the development of COVID-19 vaccines. The technology has even helped medical professionals identify patients at greater risk of suffering complications from COVID-19.

Additionally, AI-based algorithms can be used to analyze patients’ EHRs (electronic health records) to identify infectious disease trends. Applications, such as the IBM Watson Health unit, can even empower cancer treatment by matching patients with clinical trials.

Another outstanding application of the technology is AI-analyzed crowdsourcing. While crowdsourcing has been informally used in healthcare, AI helps skyrocket the accuracy of efficiency of the technique.

Judicious use of AI-powered crowdsourced data will be instrumental in making medical care accessible to more people. Let us delve deeper into AI-analyzed crowdsourcing and explore its benefits in medical care and research.

How Does AI-Based Crowdsourcing Work?

Crowdsourcing is the technique of gathering information and insights about a particular topic from the public through the internet. Unlike looking for information on Google, crowdsourcing provides access to data and knowledge from subject matter experts.

Even though the term had not been coined until 2006, its use has been prevalent in the medical world. Medical practitioners would often refer to their peer-reviewed studies and other research to diagnose and treat rare ailments. However, the rise of social media and the internet have made crowdsourcing even more prevalent.

Nevertheless, collecting and analyzing crowdsourced data can be difficult. The absence of proper data processing could lead to inaccurate diagnosis, or worse, wrong treatment. That is where AI steps into the picture. AI-powered crowdsourcing platforms help eliminate the drawbacks associated with the approach.

AI-Crowdsourcing in the Medical World: Access to Alternative Treatments

AI algorithms simplify the process of procuring data from different sources and analyzing it to provide accurate insights. StuffThatWorks, an AI-powered crowdsourcing community, uses this technique to provide members with access to information about lesser-known treatment options for over 550 conditions, including fibromyalgia, osteoarthritis, hidradenitis suppurativa and perioral dermatitis.

The platform collects data from various members in the form of precise, organized questions. The data is then analyzed by a machine-learning (ML) algorithm to identify the most effective and most tried treatment for a particular disease. The data becomes more and more personalized as more contributors join.

It helps provide members with in-depth insight into how different treatments work and equip patients with tools to take a more active role in their condition management. The information on the platform is regularly updated by the algorithm based on data from new member reports.

Take perioral dermatitis, for instance.

Perioral dermatitis treatment includes:

  • Oral antibiotics, such as Doxycycline and clindamycin
  • Lifestyle and dietary changes
  • Immunosuppressive creams, such as tacrolimus

AI-analyzed data can show patients the most tried and most effective treatment options. For example, StuffThatWorks used AI-analysed crowdsourced data to determine that Doxycycline is both the most tried and the most effective perioral dermatitis treatment.

While the above mentioned are the most commonly known remedies, other perioral dermatitis treatment options, including anti-fungal creams and ketoconazole, are also effective. Similarly, a few member reports also list Soolantra cream (ivermectin) as a potential treatment for the disease.

The power of AI-analyzed crowdsourcing helps people learn about alternative, non-traditional, and less common treatment options for their ailments. In the absence of such platforms, patient care would be limited to the most widely used treatments.

Key Benefits of AI-analyzed Crowdsourced Data?

Apart from helping patients get more information about new, emerging and uncommon treatment options, AI-based crowdsourcing provides the following additional benefits:

Widespread Access to Quality Medical Care

In today’s digitized world, internet connectivity is available in most regions across the globe. People residing in remote and rural areas also have access to high-speed internet connectivity.

While these platforms cannot substitute medical care and expertise, they can provide information to patients who do not have immediate access to healthcare facilities.

Diagnosis of Rare Diseases

AI-analyzed crowdsourcing is not only useful for patients. The technology can also help medical practitioners seek suggestions and insights from healthcare professionals across the world. For instance, they can upload a patient’s symptoms and test results on an AI-powered platform.

Other medical professionals can check the symptoms and share their recommended diagnoses. After that, an AI/ML algorithm will collate all the responses and provide the most accurate suggestions. The algorithm can even be modified to rank different diagnoses based on effectiveness and accuracy.

Increased Accuracy and Efficiency

Another key benefit of AI-powered crowdsourcing is that it can improve the efficiency of medical practitioners. When physicians and surgeons have access to actionable insights from crowdsourced data, they can better identify the right treatment options for patients. It minimizes the scope of error in diagnosis and treatment.

Modernizing Medical Care With AI-Crowdsourcing

Effective use of AI-analyzed crowdsourced healthcare data can revolutionize the way healthcare facilities diagnose and treat various ailments. Also, it empowers patients with a plethora of useful information about their condition and available treatments.

It is, however, important for patients to understand that AI-crowdsourced data and insights are not an alternative to medical supervision. They are advised to consult their physicians before trying any new or non-traditional treatment.

Similarly, medical practitioners should use their judgment and discretion before trusting the diagnostic recommendations from such platforms. As with any new technology in the medical industry, AI-analyzed crowdsourcing will provide maximum benefits when combined with the power of human decision-making.

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