In this blog, we are going to see how KlicKare can boost up medical diagnosis by using deep learning.
Medical diagnostics are a category of medical tests designed to detect infections, conditions, and diseases. These medical diagnostics fall under the category of in-vitro medical diagnostics (IVD) which be purchased by consumers or used in laboratory settings. Biological samples are isolated from the human body such as blood or tissue to provide results. Today, AI is playing an integral role in the evolution of the field of medical diagnostics.
We are a group that believes that basic healthcare is right to every individual living on the planet. Hence with our AI and ML solutions, it is now more than easy to provide that basic healthcare.
Problem in Traditional Diagnosis
According to a survey, nearly a thousand or millions of people are dying because of the wrong diagnosis.
Here are some of the common problem in traditional medical diagnosis
- Availability of diagnostic tests Diagnostic tests limited in scope, availability or quality
- Access to high-quality primary care Limited access due to lack of money, remoteness, illiteracy, travel constraints or a limited number of health care facilities
- Care coordination Consultations delayed or test results lost or a lack of health records documenting care.
- Availability of health care professionals and specialists Lack of sufficient, competent health care professionals, for example, due to lack of training, outward migration or a poor employment situation. Specialty expertise may not exist or may be limited in number or quality
- Availability of health informatics resources Health informatics resources, including internet access, may not be available, especially in remote areas; unaffordable subscription or download fees for medical information
There was a set of problems with these traditional ways of diagnosis
The Institute of Medicine at the National Academies of Science, Engineering, and Medicine reports that “diagnostic errors contribute to approximately 10 percent of patient deaths,” and also account for 6 to 17 percent of hospital complications. It is important to note that physician performance is typically not the direct cause of diagnostic errors. In fact, researchers attribute the cause of diagnostics errors to a variety of factors including:
- Inefficient collaboration and integration of health information technologies (Health IT)
- Gaps in communication among clinicians, patients, and their families
- A healthcare work system which, by design, does not adequately support the diagnostic process
In the above figure, we can see different social media platforms’ new about how misdiagnosis making an bad impact and different stories from people who got misdiagnosed by doctors.
AI Systems can apply its “advanced vision” to scan medical images for abnormalities. Using training data, the AI Systems can spot these abnormalities and bring them up for review by the radiologist. Alternatively, autonomous AI Systems can make the diagnosis efficiently for diseases that have clear cut causes that are easy to find for the AI System.
The repetitive work of reviewing the scans to look for abnormalities is automated. Once the abnormalities are identified, the radiologists can then apply his or her experience to make the actual diagnosis.
The improved efficiency in the Radiology workflow results in a more accurate diagnosis as well as a more timely diagnosis.
- Improves information organization and display Decreases the cognitive burden and distraction and highlights key information to ensure they are not overlooked
- Improves access to reference information Provides access to information, journals, images, and clinical guidelines.
- Provides tools for collaborative diagnosis Facilitates access to second opinions from experts and makes it easier to solicit colleagues to discuss challenging cases, for example, via telemedicine or electronic consultations
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- Helps generate a broad differential diagnosis Suggests key follow-up questions or tests consider; differential diagnosis generators are used to offset premature closure around a single diagnosis.
- Helps detect diagnostic errors Double checks can help catch mistakes; electronic algorithms can detect missed opportunities for diagnosis and discrepancies.
- Facilitates diagnostic feedback to clinicians Establishes a clear chain of events while documenting the care process more accurately; any errors ultimately discovered can be fed back and shared as a learning experience by all.
- Provides tools for collaborative diagnosis Facilitates access to second opinions from experts and makes it easier to solicit colleagues to discuss challenging cases, for example, via telemedicine or electronic consultations.
- The common platform between healthcare experts and users
- Adding other diagnoses services adding first-aid manuals for user interaction
Interested in implementing AI and machine learning in your product or services?
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