Artificial Intelligence (AI) in healthcare mobile apps is mainly used for pattern recognition. Patterns that humans cannot see due to the volume, nature, and nuance of data that our smartphones can collect and process. It does so by identifying patterns and correlations that are not visible to the human eye.
The secret sauce of almost all important AI healthcare apps is that they leverage the fact that we already connect our smartphones to our bodies through sensors and that we know what's best for our bodies through centuries of medical research.
Here are the top ways AI advances mobile healthcare apps in the personal space:
Everyone knows that sleep is important for our health and well-being. The hard part about sleep enhancement is that we are all individuals and our patterns differ. With AI, it becomes possible to track our sleep and generate recommendations for us individually based on our diverse health needs. After a few nights of data collection, AI can tell you what times, what level of room light, and what other circumstances lead to optimal sleep for an end user.
Because there are a lot of habits we want to track to get the best out of our time and lives, habit-tracking apps have become very popular over the past few years. So much so that many of them form part of your smartphone’s series of apps already - think about how you are able to track your steps per day or remind yourself of today's yoga or Pilates class.
The same goes for working out, swimming, and running. It's less the activity that is important but when and how you observe it. That is the valuable data that helps AI to make suggestions and remind you to do what's good for you. Paired with advancements in wearable technology, AI and habit tracking are set to go even further.
Next to health supplements, counting calories, tracking macronutrients, and simple water intake, there are now apps that go beyond the above and more. With the help of food image recognition, they are able to give you a detailed report of the nutrients you are about to eat.
This is not only good for people with allergies, but also for people who want to improve their eating habits in general. With the help of AI, friction is further reduced while tracking your food intake. This means more time for other things while getting the same data insights out of your app.
For people with chronic health conditions like Diabetes or Celiac disease, these AI-based healthcare apps can radically improve their quality of life, medical reporting, and more.
Breathing exercises, meditation, and mindfulness have all been scientifically proven to reduce stress and boost mental resilience during the day.
The problem is that our commitments to mindfulness and meditation are quickly forgotten during stressful days - they don’t become the daily habit that they are supposed to be. With the help of AI, mobile healthcare apps are able to remind you at the perfect time to observe the habits you promised your future self to do for your mental well-being. Apps like Calm which helps people access guided meditation and sleep readings are already proving the market for this technology is huge.
By collecting feedback from you on how you felt during which exercise, AI can make the perfect recommendations for your well-being based on your own data.
AI in mobile healthcare apps is used to identify patterns and correlations that are not visible to the human eye. It can be used for sleep enhancement, habit tracking, diet improvement, and stress reduction. AI is not black magic; it is merely a black box for pattern recognition.
This means we can use it ethically to improve our own routines and save a lot of time doing so.
Artificial Intelligence (AI) in healthcare is one of the most anticipated domains for technological progress today. Because of its potential to save and change lives for the better, a lot of AI researchers focus on this topic.
As AI is very good at pattern recognition, medical AI is used to diagnose humans and spot diseases or anomalies in the human body as early as possible. The promise is to get information out of data that humans cannot see with their naked eye.
Everyone has heard about the amazing stories of AI detecting cancer way earlier than any radiologist could. But these cases were recorded during perfect data conditions. AI diagnosis tools like that are not yet available for the average patient.
The race for universal access to AI diagnosis is between human privacy and hardware adoption. More data means more quality AI models, and better hardware and data platforms mean more data for everyone to train the models on.
The opposite of privacy is accessibility. Data needs to be anonymized, cleaned, and stored very securely. Medical data is not public data due to the rights of individuals over their personal data.
Software solutions that store and share private data in a privacy-centric way will change the medical AI landscape for the better. The goal is to facilitate access to as much data as possible without infringing on the privacy rights of civilians.
While radiologists are professionals, they are still humans and think twice about buying the latest technology for their clinic. They are trained at using what they have at hand, not what AI might see. For them to use the latest AI technologies, the AI models must either become better at a diagnosis under suboptimal data conditions, or the clinics have to buy better lenses and hardware.
AI in healthcare is still in its early developmental stages. In order to fully take advantage of AI's diagnostic capabilities, hardware needs to be updated and data privacy concerns need to be addressed as soon as possible. The software solutions that tackle these vital issues of medical AI will be the most impactful for the healthcare industry.
With the importance that safety in AI healthcare apps carries, it’s important to partner with an AI and machine learning agency like NineTwoThree Studio which has more than a decade of experience bringing these apps to market.
Dennis Bosch is a writer at Passivebasics: a Technical Newsletter for non-technical Founders. Learn the top-down basics of Web Apps, APIs, Git, Docker, ML/AI, et al. without the usual hassle. You can also follow them on Twitter @passivebasics.
