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Smart speakers in the clinic are prepping to relieve clinicians of their EHR burdens, capturing free-form conversations and translating the content into structured documentation. Physicians and nurses will be able to collect and retrieve information more quickly while spending more time looking patients in the eye. With companies like Amazon achieving HIPAA compliance for their consumer-facing products, individuals may soon have more robust options for voice-first chronic disease management and patient engagement. But the symptoms are often hiding in plain sight for radiologists.

Artificial Intelligence In Mental Health Care - The Medical Futurist

Using artificial intelligence to flag worrisome injury patterns or mismatches between patient-reported histories and the types of fractures present on x-rays can alert providers to when an exploratory conversation is called for. Every second counts when a patient experiences a stroke. In far-flung regions of the United States and in the developing world, access to skilled stroke care can take hours, drastically increasing the likelihood of significant long-term disability or death. Artificial intelligence has the potential to close the gaps in access to high-quality imaging studies that can identify the type of stroke and the location of the clot or bleed.

In rural or low-resource care settings, these algorithms can compensate for the lack of a specialist on-site and ensure that every stroke patient has the best possible chance of treatment and recovery.

Source: Getty Images Reducing administrative burdens for providers The costs of healthcare administration are off the charts. Medical coding and billing is a perfect use case for natural language processing and machine learning. NLP is well-suited to translating free-text notes into standardized codes, which can move the task off the plates of physicians and reduce the time and effort spent on complying with convoluted regulations.

NLP is already in relatively wide use for this task, and healthcare organizations are expected to continue adopting this strategy as a way to control costs and speed up their billing cycles. AI will combine with another game-changing technology, known as FHIR , to unlock siloes of health data and support broader access to health information. Patients, providers, and researchers will all benefit from a more fluid health information exchange environment, especially since artificial intelligence models are extremely data-hungry.

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Stakeholders will need to pay close attention to maintaining the privacy and security of data as it moves across disparate systems, but the benefits have the potential to outweigh the risks. Image-heavy disciplines have started to see early benefits from artificial intelligence since computers are particularly adept at analyzing patterns in pixels.

Ophthalmology is one area that could see major changes as AI algorithms become more accurate and more robust. From glaucoma to diabetic retinopathy, millions of patients experience diseases that can lead to irreversible vision loss every year.

David D. Luxton, PhD

Employing AI for clinical decision support can extend access to eye health services in low-resource areas while giving human providers more accurate tools for catching diseases sooner. Artificial intelligence is accelerating discovery by helping providers interpret the incredibly complex data that the brain produces.


From predicting seizures by reading EEG tests to identifying the beginnings of dementia earlier than any human, artificial intelligence is allowing providers to access more detailed, continuous measurements — and helping patients improve their quality of life. Nearly half a million people died from the mosquito-borne disease in , according to the World Health Organization, and the majority of the victims are children under the age of five.


Deep learning tools can automate the process of quantifying malaria parasites in blood samples, a challenging task for providers working without pathologist partners. One such tool achieved 90 percent accuracy and specificity, putting it on par with pathology experts. This type of software can be run on a smartphone hooked up to a camera on a microscope, dramatically expanding access to expert-level diagnosis and monitoring.

Source: Getty Images Augmenting diagnostics and decision-making Artificial intelligence has made especially swift progress in diagnostic specialties , including pathology. Suicide is the tenth leading cause of death in the United States, claiming 45, lives in Suicide rates are on the rise due to a number of complex socioeconomic and mental health factors, and identifying patients at the highest risk of self-harm is a difficult and imprecise science.

Natural language processing and other AI methodologies may help providers identify high-risk patients earlier and more reliably. AI can comb through social media posts, electronic health record notes, and other free-text documents to flag words or concepts associated with the risk of harm.

Researchers also hope to develop AI-driven apps to provide support and therapy to individuals likely to harm themselves, especially teenagers who commit suicide at higher rates than other age groups. Since first launching in , Youper has steadily increased its user database with a monthly download growth rate of Other apps such as Calm and Ginger.

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A growing number of AI mental health tech apps have been created in an effort to address these statistics. Apps such as Moodpath track anxiety patterns by providing users with daily assessments: after two weeks of use, the app generates a document that can be shared with a health care professional. In order to combat this often neglected population of patients, health tech often utilizes a variety of traditional therapy approaches.


Health tech app PE Coach works alongside therapists using prolonged exposure PE treatment and includes educational resources and breathing training tools to help therapists provide continuous care. The app employs a variety of prompts and weekly tasks to coincide with in-person treatment. While research on the long term impact of mental health tech remains fairly limited, initial results are providing both healthcare professionals and users with promising results.

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