Artificial intelligence (AI) is poised to revolutionize the way we diagnose and treat diseases. It may be particularly useful for depression, as it allows us to make a more accurate diagnosis and determine which treatments are more effective.
Several 20% of us You will experience depression at least once in your life. In the world, 300 million People are currently experiencing depression; 1.5 million Australians can become depressed at any time.
For this reason, depression is described as: who as the single greatest cause of disease worldwide.
So how exactly can AI help?
Depression can be difficult to detect
Despite its frequency, depression is difficult to diagnose. In fact, it is very difficult for general practitioners to accurately detect depression. less than half of the case.
This is because there is no single test for depression. Doctors use self-reported symptoms, questionnaires, and clinical observations to make the diagnosis.but symptoms of depression It's not the same for everyone.
Some people sleep more, others less. Some people may lack energy or interest in activities, while others may feel sad or irritable.
People who have been correctly diagnosed with depression have a variety of symptoms. treatment options Things like talk therapy, medication, and lifestyle changes. However, everyone responds differently to treatment, and there is no way to know in advance which treatments will be effective and which will not.
AI trains computers to think like humans, with a particular focus on three human-like behaviors: learning, reasoning, and self-correction (fine-tuning and improving performance over time).
One of the areas of AI is machine learning. The goal is to train computers to learn, find patterns in data, and make data-based predictions without human guidance.
In recent years, there has been a surge in research on applying AI to diseases such as depression that are difficult to diagnose and treat.
what they have found so far
scientists have compared Chat GPT Diagnosis and medical recommendations from real doctors amazing results. Given information about hypothetical patients of varying depression severity, gender, and socio-economic status, ChatGPT primarily recommended talk therapy. In contrast, doctors recommended antidepressants.
we, Englishman and Australian person Guidelines recommend talk therapy as the first treatment option prior to medication.
This suggests that ChatGPTs may be more likely to follow clinical guidelines, whereas GPs may be more likely to follow clinical guidelines. overprescribe Antidepressants.
ChatGPT is also less susceptible to gender and socio-economic bias, whereas doctors men more likely to be prescribed antidepressantsespecially those in blue-collar jobs.
How depression affects the brain
Depression affects certain parts of the brain. According to my research, the areas of the brain affected by depression are: very similar Among different people. That's why he can predict with over 80% accuracy whether someone has depression just by looking at their brain structure on an MRI scan.
other the study The use of advanced AI models supports this finding, suggesting that brain structure may be useful for AI-based diagnosis.
Research using magnetic resonance imaging (MRI) data Brain function at rest Depression can also be predicted with over 80% accuracy.
However, combining functional and structural information from MRI provides the highest accuracy and can accurately predict depression. 93% of cases. This suggests that using multiple brain imaging techniques for AI to detect depression may be the most viable path forward.
MRI-based AI tools are currently used for research purposes only. But as MRI scans become cheaper, faster and more convenient; portableperhaps this type of technology will soon become part of doctors' practices. tool kithelp improve diagnosis and enhance patient care.
Diagnostic tools you already have
While MRI-based AI applications are promising, there may literally be simpler and easier ways to detect depression at hand.
The capabilities of wearable devices such as smartwatches are being studied. Detect and predict depression. Smartwatches are especially useful because they can collect a variety of data such as heart rate, steps, metabolic rate, sleep data, and social interactions.
Recent review All studies conducted to date on using wearables to assess depression have found that they correctly predict depression 70-89% of the time. Because wearable devices are commonly used and worn 24 hours a day, this study suggests that they have the potential to provide unique data that would otherwise be difficult to collect.
There are some DisadvantageHowever, this includes significant costs for smart devices that many people may not have access to. Others include the ability of smart devices to detect biological data being questioned. people of color And that lack of diversity In the study population.
Research is also being conducted that focuses on social media to detect depression. Using AI, scientists predicted the presence and severity of depression. Language of posts and community membership on social media platforms.
Specific words used predicted depression by up to 90% success We display prices in both English and Arabic. Depression has also been successfully detected early. emojis we use.
Predicting response to treatment
Some studies have found antidepressant effects treatment response I could predict Electronic medical records alone provide more than 70% accuracy. This could provide doctors with more accurate evidence when prescribing drug therapy.
Combining data from people participating in antidepressant drug trials, scientists predicted Whether taking medications can help certain patients remit their depression.
Although AI shows great potential in the diagnosis and management of depression, recent findings require validation before they can be trusted as diagnostic tools. Until then, MRI scans, wearables, and social media may help doctors diagnose and treat depression.
sarah herewellResearch Fellow, Faculty of Health Sciences, Curtin University, and Perron Institute of Neurological and Translational Sciences; curtin university
This article is republished from conversation Under Creative Commons License.read Original work.
