AI in Diagnostics
Hey guys, welcome back to the series of 'AI in Medicine'. Today we will be discovering how AI can transform diagnosis in medicine.
AI can read X-Rays, MRI, CT-Scans and blood tests faster and sometimes more accurately than humans and contribute to early detection of diseases like cancer, diabetes and Alzheimer's before symptoms even show up!
AI is particuarly good for handling large datasets, providing physicians with a wealth of insights for informed desicion making, which is particularly important when attempting to diagonse patients correctly.
AI in Early Detection of diseases:
A clinical trial has recently been carried out on AI in diabetes diagnosis, where ECG (electrocardiogram which is used to identify irregular heartbeats and rhythms) is used to diagnose diabetes type 2, as earlier as 10 years before any visible symptoms! If more information and data like age, gender BMI is given to the algorithm, accuracy increases.
AI can detect several cancers in early stages, eg lung cancer, improving patient well-being and alleiviating eccenomic burden of mallignant tumours in healthcare systems.
AI tools analyze genetic data to identify mutations and gene variations linekd to rare diseases
AI can use electronic health records (EHRs) to forecast who is at risk of which diseases
AI chatboxes can guide patients through symptoms and provide them with potential diagnoses before seeing a doctor
AI in interpreting and analysing patient data and scans
Identifies subtle anomolies in scans that humans may miss.
AI can integrate and analyse patient data, helping to find patterns for patient diagnosis and prognosis.
If genetic data and history of the patient is provided, AI can even create a tailored treatment plan for the patient in a way that maximises impact of treatment and minimises side-effects
Limitations of AI in Diagnostics
However, AI is still unreliable with diagnosing uncommon diseases.
Exisiting machine algortithms for illness diagnosis lack "explanation capactiy" as it is the outcome of training and hard work so any diagnosis is made by discrepancies between sick and normal photographs
Some AI descions are hard to interpret which may disturb trust and accountability
Limited dataset which may perform differently in diverse settings and populations