Covid- 19 did not just come in front of us as an infectious disease but also brought a lot of opportunities for artificial intelligence to perform advancements in various fields. More impact can be seen in the healthcare industry. According to Gartner reports, 75% of healthcare delivery organizations (HDOs) are interested in investing in AI to improve operational performance and clinical outcomes. This report is a representation of a substantial rise in complexity and an abundance of data.
At DSW, we have worked with various clients in various fields, and the medical field is one of them. In such a crucial field, we have designed, developed, and deployed AI solutions and found that Artificial Intelligence can significantly impact the following critical areas of healthcare.
Table of content
- Patient Prescreening
- Patient Intake and Triage
- Diagnosis and Medical Imaging
- Preventative healthcare
- Drug Discovery
- Optimised Standard of Treatment
Let’s start our reading with the first area.
Patient Prescreening
This is a simple but crucial use case of AI in the medical field because it ensures patient care before they arrive at the medical facilities. Nowadays, we can see that this use case has taken the form of questionnaires, our online symptom checker. Although these checkers have a wide range of accuracy but involve artificial intelligence, we can make them more accurate. For example, a team of Harvard and Boston children’s hospitals analysed that only 58% of the time, 23 online symptom checkers had provided the right advice. At the same time, this number can be increased using AI.
There are two ways in which we can add AI to patient prescreening. The first one is to apply AI-Bot(voice or text-based) in the user interface so that every user can easily use such facilities. These AI-Bots can be utilised to mimic a real-life medical professional’s expertise. Here, natural language processing plays an important role. Patients can communicate their problems in text or voice format as they perform with a doctor and simulate a real-life doctor and patient conversation.
The second one is using AI’s absolute power to make predictions. AI brings the power of predicting a significant addition to this field. Since these systems are good at learning the patterns in the data and making predictions, they become much more successful in diagnosing patients more accurately. By analysing the symptoms passed by patients, AI models can tell what disease patients are suffering from and what can initially help them to deal with so that the aim of providing care before reaching the health facility can be fulfilled.
Patient Intake and Triage
The traditional patient intake and triage procedure require manual paperwork AI can replace manual paperwork. AI is more efficient than humans at performing manual and repetitive tasks. In the healthcare domain, emergencies can be very stressful, and AI can be very helpful in such situations. Filling out crucial paperwork can be very challenging. Here,AI-enabled chatbots and voice assistance quickly gather patients’ essential information and tell them about the appropriate next step.
To perform these tasks, AI needs digitised data that goes under the AI algorithms that give information about matching doctors and steps to perform in intake processing. In many possible cases, AI algorithms can also predict if someone can or should wait for a specialised doctor to take care of them. These capabilities of AI are very helpful in scheduling the patient’s doctor’s meetups.
Diagnosis and medical imaging
This one is one of AI’s most crucial use cases in the medical field. Many algorithms in machine learning and artificial intelligence are well equipped to handle rare events such as uncommon diseases. In addition, they are more accurate in detecting various anomalies than human professionals, which also helps professionals to understand how to start their treatment appropriately.
Both of these are very complex, and when we look at the traditional approaches, we find that feature development and engineering have been used for so long to represent transformed data in a model. But this is not the end because expertise(SME) is always required in such domain subject matter. However, deep learning and neural networks are the subjects of AI that are compelling solutions for many complex topics and problems that always need human-like thinking for imaging and diagnosis. Such innovations in the field help a lot in wasting time and money on studying these topics and producing better results.
Preventative Healthcare
Preventative care can be defined as the steps taken by us to prevent diseases before they manifest. In today’s scenario, we can say that these steps are related to procedures like routine physicals, vaccinations, and healthy diets. In this use case, AI finds a better space so that this can all be updated in better directions.
AI uses its predictive powers to update this scenario. There are various healthcare eequipments, such as smartwatches and Fitbits, that uses Ai-enabled sensors that support the updation of the preventative care domain. In a simple way, these sensors collect data about heart rate, physical activity, nutrition, VO2, and sleep, and AI uses this data to predict health issues and their appropriate solutions. Such devices can also be used for collecting large amounts of data, which makes AI more accurate in predicting specific diagnoses. Here, AI helps in deriving high-quality care and also helps reduce inspection time.
Drug Discovery
We first started with the covid-19 that also highlights the vaccination discovery processes. We have witnessed that it took around one year to find some vaccines to prevent Covid-19. This process took so much time, and still we have only temporary solutions for it, which shows how expensive and time-consuming drug discovery process is.
This time, consumption and expenses made drug makers turn toward AI solutions. These AI-enabled models are helping drugmakers with developing and testing new drugs. It can be easily imagined that discovering a new drug requires the processing of a large amount of data, and AI’s capabilities address this challenge by using its power to work and calculate well with a large amount of data. This ability of AI helps in producing a large number of approved drugs faster and less expensive. According to this report, AI has been utilised for covid-19’s vaccine discovery processes.
Optimised Standard of Treatment
According to the CDC(Center for Disease Control) report, 85% of physicians use the digitised medical record system, representing the opportunities for enabling AI in the healthcare domain. These records can be beneficial in optimising and finding standard treatments and care. Utilising AI, we can make guides compiled with best practices for medical care. As we all know, every disease has its own different symptoms, conditions, and treatments. If we have predefined standards of treatment for diseases, we can become more dominant.
If we can maintain such guides and renew them frequently, we can be sure that each patient receives the most optimized and informed medical care. Moreover, one of the best benefits of this use case is reducing the chances of mistakes or human mistakes, especially when the patient suffers from rare diseases.
Final words
In the above sections, we have seen some key areas of implementing AI in the medical field. These are advancing the medical domain and helping people get the best out of it so that they can be treated well and accurately. Moreover, in today’s scenario, we need to leverage such technologies in such a crucial domain.
We at DSW take charge of democratising the power of AI using our flagship platform, UnifyAI, across all possible domains. As a result, we help many healthcare clients in their journey to develop, design and deploy AI-enabled solutions into their operations to leverage AI technologies towards growth, improve patient care and overall healthcare ecosystem.