Machine learning is an application of artificial intelligence that uses algorithms and statistics to find patterns in large amounts of data. Machine learning parses this data and learns from it by inferring patterns from it and making predictions from the aforementioned patterns.
The data can be anything from pictures to numbers and words. In healthcare, machine learning can be used to diagnose diseases. It is mainly used to help screen for breast cancer.
AI diagnosis in healthcare is an alternative to immune based diagnostics. Immune based diagnostics have been used extensively in the past in the diagnosis of diseases. Immune based diagnostics work by deploying an antigen to detect the presence of an antibody or lack thereof of the pathogen in question in the specimens.
Fields of Machine Learning Applications in Diagnosis
There are a lot of medical fields where AI healthcare diagnostics come into play;
Dermatology
Dermatology is one of the areas where AI healthcare diagnostics is applied. AI is used to improve clinical decision making and ensure the accuracy of skin disease diagnoses. The idea behind implementation of machine learning is to reduce the number of unnecessary biopsies dermatologists put their patients through.
Some of the machine learning implementations include tools that track the development and changes in skin moles over time to help detect pathological issues at their earliest stages. There are algorithms to separate melanomas from skin lesions with better accuracy than a human and those that pinpoint biological markers for acne and nail fungus.
Mental Health
Mental health is just as important as general physical wellness – perhaps even more important. The impact of misdiagnosed and untreated mental issues is catastrophic to say the least. Increased health spending, low quality of life, and low productivity are just some of the consequences.
AI – through machine learning – can have a huge impact on mental health research and improve the efficiency of mental issues diagnosis. Some of the top applications of AI in this field are machine learning tools that help high-risk individuals avoid social isolation. Early detection of mental disorders through machine learning and data science makes diagnosing clinical depression, bipolar disorder and other mental-related issues easier.
It is also possible to identify individuals with high risk of suicide and provide them with support with personalized cognitive behavior therapy fueled by chatbots and virtual therapists.
Oncology
The most important bit of oncology is detecting malignant tumors on time. As such, time, accuracy and precision of the diagnosis are absolutely crucial.
AI diagnosis in healthcare helps oncologists detect the disease in its earliest stages. Medical professionals can identify somatic mutations easily. Somatic mutations are acquired changes in the genetic code of one or more cells. AI pinpoints mutation markers faster and with higher accuracy than humans.
On top of just pinpointing the tumor, machine learning can accurately determine if the tumor is malignant or benign in a matter of milliseconds. It’s not error-proof of course but it boasts of 88% accuracy of classification rate.
Pathology
There aren’t exactly enough pathologists in the world at the moment, which makes the need for AI diagnosis in healthcare in this field that much more substantial. Pathology comes with large data sets that need to be processed, which makes pathology really lucrative for AI implementation.
Some applications of AI in this field include mapping disease cells and flagging areas of interest on a medical slide, creating tumor staging paradigms, and improving healthcare professionals’ productivity by increasing the speed of profile scanning.
Genetics and Genomics
Of late, AI has helped experts in this field in the transcription of human genes. Machine learning and other AI technologies are key in preventive genetics. Scientists rely on algorithms to determine how drugs, chemicals and environmental factors influence the human genome.
There is hope that AI will be able to help in improving the efficiency of gene editing and in particular changing DNA fragments to protect a fetus from the impact of a mutation.
