AI and Machine Learning for Health Apps

6 min read

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Artificial intelligence (AI) in healthcare is becoming more widespread as the technology matures and its potential value becomes more widely recognized. AI can help to improve the accuracy of diagnosis, the efficiency of care delivery, and the overall quality of care.

There are many uses for AI in health applications. Still, one area where AI is particularly well-suited is in developing apps that can provide personalized guidance and support to patients. It can potentially improve patient care by providing more personalized treatment, earlier disease detection, and better disease progression predictions.

The future is today

The recent arrival of chatGPT in the media and our lives has created a stir. Already an inevitable revolution in the way we see and use AI daily. Apart from chatGPT's ability to write medical reports, pass doctors' written exams and -sometimes- find missed diagnoses, AI is enabling significant advances in the medical world and will continue to do so at an accelerated pace.

For example, a recently published paper explains that a deep learning model successfully created an AI-Cirrhosis-ECG score. This score can be used to diagnose and evaluate the disease severity of cirrhosis (a liver disease) from an ECG (a heart exam) with a higher accuracy than the usual standard of care (1).

Another example is a deep-learning cancer risk model that was named Sybil. With only one chest computed tomography scan, it can predict healthy lungs or lung cancer in the next year in up to 94% of cases and to 80% after 6 years (2).

As you can surmise, no human eye or brain, no matter how great the effort, can reliably accomplish these feats!

Different types of AI

There are many different types of AI, but some of the most common are:

  • Machine learning, also called deep learning, is a type of AI that allows computers to learn from data without being explicitly programmed.

  • Natural language processing is another type of AI that enables computers to understand human language.

  • Computer vision is a third type of AI that allows computers to see and interpret images.

Each of these has different applications in healthcare. For example:

  • Machine learning can be used to develop predictive models for disease progression and treatment response;

  • Natural language processing can be used to process electronic health records and extract information about patients’ symptoms and diagnoses, and converse with patients or health professionals;

  • Computer vision can be used to detect abnormalities in medical images.

How AI is used in apps

There are many different ways that AI can be used in healthcare apps.

Early disease detection is one of AI's most promising applications in healthcare. By analyzing data from patient medical records, wearables, and other sources, AI can help to identify patterns that may indicate the presence of a disease before symptoms appear. This early warning system could potentially save lives by allowing patients to receive treatment sooner.

Chatbots for health coaching are computer programs that simulate human conversation. They can be used to support and guide patients on a wide range of health topics. For example, a chatbot could help a patient manage their diabetes by providing information about diet and exercise, reminding them to take their medication, and answering any questions they may have. To learn more, read this other AppGuide article Are chatbots changing the healthcare industry?

Apps can use machine learning to customize therapy and personalized health recommendations. They develop personalized treatment plans for individual patients based on their specific symptoms and medical history. Depending on their needs, they may provide patients with customized recommendations for lifestyle changes, diet, and exercise.

Furthermore, apps can use natural language processing and algorithms to help patients identify possible diagnoses or health risks based on their symptoms. By images analysis, they can help in the early detection of diseases. Some apps may use computer vision to screen for diseases such as cancer at an early stage when they are more likely to be curable.

Remote monitoring of patients can also make use of AI. For example, connected medical devices that obtain vital signs and other health data from patients at home allow patients to leave the hospital earlier following a hospitalization while still receiving closer medical monitoring than they usually would. AI can then use predictive analytics to analyze the patient's data and identify trends that may suggest a deteriorating health condition, allowing the care team to address the issue before the patient deteriorates significantly.

Apps using AI

Let's now illustrate the above with examples of apps that use AI.
For apps offering an intelligent chatbot functionality, please look at this article Are chatbots changing the healthcare industry?

SkinVision - Find Skin Cancer

The SkinVision app uses AI to assess your risk of skin cancer. It is a quick and easy way to check your skin for signs of cancer. The app is also a great way to learn about your skin type and get advice on protecting your skin.

apple is available for this applicationandroid is available for this application
Free
In-app purchases

Ada – check your health

Ada is a free symptom checker that uses AI to assess your symptoms and provide a personalized report on possible causes and what to do next. You can check your symptoms anytime, anywhere; the app is available in 7 languages.

apple is available for this applicationandroid is available for this application
Free

Cardiogram: Heart Rate, Pulse, BPM Monitor

Cardiogram is a heart rate monitor that uses deep learning to analyze health data from connected devices and generate a cardiovascular risk prediction score. It allows you to track medical conditions and to understand your sleep, stress, fitness, and health through interactive charts, comprehensive metrics and notes that help track day-to-day fluctuations in your heart health.

apple is available for this applicationandroid is available for this application
Free
In-app purchases

Youper - Therapy Journal

Youper is a therapy journal app that helps improve your mood. It is based on cognitive behavioural therapy techniques, but it also asks questions and learns what works for you as to create a personalized strategy for the most effective change.

apple is available for this applicationandroid is available for this application
Free
In-app purchases

MDacne - Custom Acne Treatment

The MDAcne app uses artificial intelligence to analyze your skin and create a custom acne treatment plan just for you. It then offers to send you a supply of your customized treatment products.

Conclusion
As we've seen, artificial intelligence is being used in healthcare apps too, among other things, personalized recommendations and interventions based on the user's unique health data, resulting in more effective preventative care and earlier intervention.

Because of its capacity to quickly process and analyze large amounts of data, health apps that use AI already have and will continue to transform healthcare by providing accurate, reliable and timely health care, improving patient outcomes, reducing costs and improving the overall quality of care.

It is not much of a risk to predict that many more apps will use AI in even more reliable and innovative ways in the future! You don't need to wait, though: if you need it, there are already health apps that use AI and can help you today! Feel free to use AppGuide to find one or ask your health professional some help on choosing the perfect health app for you.

References

  1. Joseph C Ahn, Zachi I Attia, Puru Rattan, Aidan F Mullan, Seth Buryska, Alina M Allen, Patrick S Kamath, Paul A Friedman, Vijay H Shah, Peter A Noseworthy, Douglas A Simonetto, American College of Gastroenterology, 2022 Mar, Development of the AI-Cirrhosis-ECG Score: An Electrocardiogram-Based Deep Learning Model in Cirrhosis, https://pubmed.ncbi.nlm.nih.gov/35029163/

  2. Peter G Mikhael, Jeremy Wohlwend, Adam Yala, Ludvig Karstens, Justin Xiang, Angelo K Takigami, Patrick P Bourgouin, PuiYee Chan, Sofiane Mrah, Wael Amayri, Yu-Hsiang Juan, Cheng-Ta Yang, Yung-Liang Wan, Gigin Lin, Lecia V Sequist, Florian J Fintelmann, Regina Barzilay, Journal of clinical oncology : official journal of the American Society of Clinical Oncology, 2023 Jan 12, Sybil: A Validated Deep Learning Model to Predict Future Lung Cancer Risk From a Single Low-Dose Chest Computed Tomography, https://pubmed.ncbi.nlm.nih.gov/36634294/

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