Mobile Applications in Healthcare
Robot doctors, virtual medical assistants, diagnosing diseases before they show symptoms—what once seemed like a science-fiction movie script, is now conceivable thanks to artificial intelligence. AI healthcare companies are actively trying to change the industry one app at a time.
What you have to remember, however, is that building healthcare apps, especially the AI-powered ones, presents a unique set of challenges. Fortunately, you can respond to them with some powerful tools: industry know-how, proven software development processes, and state-of-the-art technology.
Before we jump into details on how to approach creating your own AI healthcare app, let’s review the multiple applications of AI in healthcare.
Artificial intelligence-based technologies like machine learning (ML), natural language processing (NLP), or robotics can be applied to many aspects of healthcare, from disease prevention to treatment. Let’s take a closer look at some applications of AI in healthcare:
AI diagnosis - AI healthcare companies are working on solutions that can increase the speed and accuracy of medical diagnostics. It’s not a new concept—MYCIN, an AI prototype system for treating blood infections and diagnosing blood clotting diseases, has been developed at Stanford University in the 1970s. At the time, it was not a viable program to use in practice, but with the current state of technology, using AI diagnostics is a more realistic scenario.
Read more about how AI and machine learning are transforming the diagnosis process.
Drug discovery - Developing pharmaceuticals is a lengthy and costly process. The organization Pharmaceutical Research and Manufacturers of America estimates that only 1 of 5000 compounds entering preclinical testing will eventually be approved for human usage and sold in pharmacies. AI can change these odds: when you pair a group of experienced researchers who can set the direction of the study with AI software that is able to review thousands of potential compound combinations, you can research and develop new drugs more efficiently.
Early detection and predictive care - AI algorithms can accurately detect medical issues in their early stages. They’re also able to warn patients about potential diseases they don’t yet have. By analyzing factors from different areas of our lives, AI apps could identify at-risk patients and suggest steps to stay healthy. Similar algorithms would be able to warn doctors that their patients could develop dangerous conditions, like sepsis, in time for physicians to take preventive actions.
Accurate treatment - The decision as to what kind of treatment to administer may be straightforward in some cases, but often it’s an extremely complex process. Designing oncology treatment plans, for example, requires doctors to take multiple factors into account and, ideally, stay up to date with many clinical trials happening at any given moment. What’s next to impossible for human beings, however, is doable for AI systems. They can use their computational power to cross-reference study results and match the patient with an optimal treatment plan.
Supporting the medical staff - From robot-assisted surgery to virtual nursing assistants, AI apps are able to help medical professionals with both exceptionally challenging and mundane tasks. Such collaboration can lead to a decrease in human medical errors, but it also generally improves the quality of healthcare as experienced by patients and medical staff alike.
Patient engagement - Patient engagement is all about involving patients in their own care. It provides a personalized experience that is appreciated by patients but also supports the improvement of the actual health outcomes. Artificial intelligence can be used in this space to engage patients through smartphones and wearable devices with relevant and highly personalized messaging. Such an app can, for example, check in with the patient and make sure they’re following the recommended treatment: taking prescribed medications or exercising.
Medical training - Medical training can greatly benefit from employing artificial intelligence. Thanks to the development of computer-generated natural speech, AI-powered chatbots can help medical students to practice patient interaction in realistic simulations. Similar training can be customized for different medical specialties so that students learn how to handle conversations that are likely to happen later in their careers.
Administrative tasks - Artificial intelligence is able to increase the efficiency of the healthcare industry by taking care of administrative responsibilities of doctors and nurses as well as healthcare insurance providers or pharmacists. The average US nurse spends 25% of work time on regulatory and administrative activities. Automating even some of these tasks would free a lot of valuable time.
Introducing artificial intelligence to medical procedures has great potential to disrupt the industry by improving both the health of populations as well as the experience of care. The standard of work for healthcare professionals may also be elevated, and the costs of healthcare should drop. While these prospects sound very exciting, we have to remember that there are considerable limitations to what can be achieved with the current state of AI and the healthcare industry.
One of the biggest challenges facing companies that work on AI healthcare apps is data fragmentation in the industry. Without access to datasets owned by other organizations, very few entities have an adequate amount of data. Many healthcare organizations lack proper data infrastructure needed to make sure that AI solutions work consistently for different patients. Creating a general repository of healthcare information, on the other hand, leads to questions about confidentiality and data security.
Gaining the trust of medical professionals and patients will also require time and a greater dose of transparency. The way AI algorithms work is often explained as incredibly complicated or even mysterious (black box, anyone?). With the complex nature of algorithms and companies being very protective of their proprietary systems, patients may be faced with a difficult choice: “do I want to be diagnosed/treated by an enigmatic machine or a human being?”. In order for everyone to feel more confident in AI-driven suggestions, both doctors and patients should know more about the way they are generated.
Of course, there are also challenges faced by AI-based software in general, including the algorithmic bias or fitting unreliable confounders. Both of these are potentially very dangerous in healthcare, where human life is often on the line.
The global market for AI in healthcare was valued at over $2 billion in 2018. It is predicted to grow dynamically (CAGR of 41.5%) and reach $31.3 billion by 2025.
In their report on the future of artificial intelligence in healthcare, experts from Deloitte claim that AI technologies (including NLP and ML) will become more and more critical in the industry, especially when used together and across all stages of the patient journey.
Developing healthcare software, you may face very different regulatory processes depending on the nature of your product and the country you’re pursuing regulatory clearance for. Regardless of the type of product, however, you still need to pay attention to these three aspects of building healthcare startups.
You need to rely on proven tactics for creating meaningful and impactful products—knowing your users’ needs and the reality of your market is essential. This is why, when tasked with building a healthcare app, we kick things off with product discovery workshops. It’s a part of a data-driven product development strategy that allows you to challenge assumptions regarding the product and strip it down to features that are necessary and the most valuable for the users.
When your app is based on innovation, whether it’s a new approach to treatment, diagnosis, or medical training, you simply can’t skip the prototyping part of the process. Software prototypes will help you to test and validate your product before you invest a lot of time and money into building it. Innovation in healthcare always entails a degree of risk, but prototyping allows you to significantly reduce it and move forward with more confidence in your app.
Read more about our approach to product development.
At Apptension, we build AI apps based on the following technologies that allow us to create applications with tensors (n-dimensional arrays) as their building blocks:
Working on your AI healthcare app, you cannot lose track of the business side of things. Getting traction is very important, plus it’s likely that, at some point, you will need to seek investment to develop your product further.
If you’re currently trying to take your business to the next level, check out our guide to growing a healthcare startup. You’ll learn strategies for securing financing for a healthcare app and breathing new life into your business.