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. AI has the potential to help healthcare professionals automate time-consuming tasks, make better decisions faster, and even diagnose diseases earlier. The value that these efficiencies hold is untold for both the medical expert and the person being treated alike.
But what are the powerful advantages AI can bring and how is it already transforming our approaches to healthcare apps?
The Benefits Of AI in Healthcare
One of the main benefits that AI brings to healthcare is its ability to process large amounts of data quickly and accurately. AI can go through patient records, lab results, and X-rays much faster than a human can, and it can find patterns that a human might miss. Diagnostically, this means that more potential diagnoses can be covered in a shorter time compared to a doctor working traditionally.
AI can help doctors identify diseases earlier and plan treatment more effectively. For patients with chronic diabetes, for example, AI can be used to predict when they might need insulin based on their past readings. AI can also help monitor a patient’s condition and send alerts to their doctor if there are any changes.
Preventing human error is another big advantage AI brings to the table. AI doesn’t get tired, it doesn’t make mistakes due to bias, and it can offer second opinions. AI can help doctors catch errors in prescriptions and diagnoses, something that is vital given that medical errors are the third leading cause of death in the US. This also drives the cost of medical care down by reducing the number of malpractice suits.
AI can also be used to create personalized treatment plans for patients. AI can take into account a patient’s medical history, family history, lifestyle, and even genetic makeup to come up with a plan that is unique to them. This type of AI-driven healthcare is sometimes called precision medicine and will go hand-in-hand with the development and evolution of wearable technologies like smartwatches.
Unfortunately, as with all things, there are some drawbacks to machine learning that need to be taken into account before implementing it in a field as sensitive as medicine.
The Disadvantages Of AI in Healthcare
While AI holds a lot of promise for healthcare, there are also some dangers that need to be considered. One of the biggest dangers is privacy breaches. If an AI system gets access to a person’s medical records, it could be used to exploit them or their family members.
Another danger is that AI could be used to make decisions about who gets what treatment if there is no human supervision. If AI is left to its own devices, it could start to ration care based on factors like age or health status. This would be a huge problem because it would put the most vulnerable members of society at an even greater disadvantage.
AI systems are also biased. They can be trained on data that is biased, which means that they will continue to be biased when they make decisions. This is a big concern in healthcare because AI systems could start to discriminate against certain groups of people if they’re not properly monitored.
It’s also important to remember that AI is still in its early stages and there are bound to be some teething problems. AI systems have been known to give false positives and negatives, which could lead to a patient being misdiagnosed. AI systems can also be expensive to develop and maintain, which could put them out of reach for some healthcare providers.
Despite the concerns, AI in healthcare is still a promising area of development. The potential benefits are too great to ignore, and as AI systems become more advanced, the disadvantages will become less and less significant. It’s important that we continue to research and develop AI in healthcare so that we can reap all the rewards it has to offer.
The Future of AI in Healthcare Apps
AI is already being used to develop new treatment types and data storage capabilities in healthcare apps today, and this will advance even more in the future.
One of these AI applications is machine learning being used to develop personalized treatments for cancer patients. AI can analyze a person’s DNA and find mutations that are specific to them. This information allows for the development of targeted therapies that are much more effective than traditional treatments. AI is also being used to develop new drugs and it’s estimated that AI will be responsible for the development of 50% of all new drugs by 2025.
Another area where AI is being used in healthcare is in the development of digital health assistants. These are AI-powered chatbots that can provide patients with information about their condition, answer basic questions, and even offer emotional support. Digital health assistants are becoming increasingly popular as they can provide 24/7 care and support to patients, even when human doctors are unavailable,
AI is also being used to develop wearable technologies that can monitor a person’s health. These devices can track things like heart rate, blood pressure, oxygen levels, and other vitals. Because AI is very good at pattern recognition, medical AI is used to diagnose humans to spot diseases and 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 fantastic stories of AI detecting cancer way earlier than any radiologist could. But these are recorded during perfect data conditions. AI-driven results like that are not yet available for the average patient. This is because of data privacy and the hardware used in the professional fields.
Why Healthcare App Development Needs Extra TLC
The opposite of data privacy is data accessibility. Healthcare is one of the most data-intensive industries, and as such, it is also one of the most heavily regulated.
The problem is that in order to train better AI models, we need more data. 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. Medical data is rare and precious because of that.
Software solutions that store and share private data in a privacy-focused manner 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. Combining many technologies like encryption and cloud security best practices might eventually enable the data to exchange medical AI needs to thrive globally. Making more out of the data that's present in data silos is also a way to circumvent the scarcity of medical data.
Beating Hardware Bottlenecks
There are very few quick sales processes when it comes to medical equipment. Furthermore, they are trained to use the technology at hand. The hardware that would be optimal to use for AI software can be expensive.
Unless practicing medical professionals or hospital owners have good incentives to improve their services by purchasing this equipment, they simply won't.
How To Develop High-Quality AI Healthcare Apps
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.
These reasons and more are why it's essential to partner with a reputable app development agency that has experience working with the needs of the healthcare sector. NineTwoThree is a leading venture studio that specializes in AI and healthcare app development. Our team of experienced developers can help you navigate the challenges of developing a high-quality AI healthcare app. \
About The Author
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. Follow him @passivebasics on Twitter.