We’ve spoken in the past about how AI is transforming research, though this mainly covers the concept of using AI to help you pore through hundreds of papers and studies in a matter of minutes, rather than days. If you branch off from this, you enter the world of scientific research – which is also closely linked with healthcare. Both have seen uptakes in AI tools over the last few years, particularly when you look at the use of conversational AI. 

For a quick recap, conversational AI is the type of artificial intelligence most people use daily. It’s the question and answer scenario where the user types a question, and the AI bot responds as best as it can. The general AI tools – like ChatGPT or Gemini – that the average person uses are trained with quite a broad knowledge spectrum. However, we’re seeing a greater deal of conversational AI tools trained with an intensive back knowledge of healthcare and scientific research. 

This type of thing tends to happen whenever you see rapid growth in an emerging technology. It should also be pointed out that healthcare and scientific research are two critical industries where the demand for instant information is more important than almost anywhere else. On the healthcare side of things, institutions are constantly under pressure to improve productivity and reduce things like patient waiting times and booking backlogs. Conversational AI could be one of the biggest solutions to this – and we’ll talk more about that later on. 

Likewise, when it comes to scientific research, you’re normally dealing with a combination of things that demand instant information. From the use of chatbots to help improve procurement for scientific laboratories to AI that helps researchers make quick sense of detailed information, this technology has the potential to be everywhere. 

So, what are some of the biggest use-cases for conversational AI in healthcare and scientific research – and is it proving to be as beneficial as it seems? 

Better Admin Efficiency For Healthcare Providers

One study found that, after implementing conversational AI through website chatbots, healthcare providers saw a 28% reduction in administrative workload. What this effectively means is that patients call upon AI chatbots and conversations instead of bothering admin teams directly. It reduces the burden on healthcare staff and allows them to be more efficient with their overall operations. 

The same research also found a 42% improvement in appointment attendance, largely thanks to how much easier it was for patients to get appointments without needing to use the phone and be on hold for ages. Conversational AI, in this instance, can also assist healthcare providers in pre-diagnosis to decide which patients actually require an in-person appointment. Many were found to simply need a repeat prescription, while appointments were saved for those with a more urgent need. 

A Reduction In Scientific Research Costs

As noted earlier, conversational AI has a unique use case for scientific research: procurement. Scientists depend on analytical equipment to help them carry out essential research in laboratories. The problem they face is that it’s very hard to find the right equipment for their specific needs. Many manufacturers and suppliers aren’t that helpful, as they simply provide a list of equipment on their websites with nothing else. 

In comes the analytical instrument chatbot to simplify the process. This conversational AI model is trained to understand lab research equipment and helps scientists discover the perfect analytical instrument for their use cases. The buyer can talk to the AI assistant and ask questions about column compatibility, applications, and so on. It goes beyond what a sales team can manage, but an AI assistant is capable of answering all of these questions. 

As a result, scientific research can actually become more affordable. How? Because laboratories are wasting lessing money on analytical instruments that aren’t as effective as they should be. It’s not unheard of for some labs to use two separate instruments to help them analyze samples, when there may be one instrument that’s capable of doing these jobs in one. Conversational AI helps teams identify what they need so they only buy the right equipment and keep the costs down. 

Improved Patient Satisfaction

A big study involving close to a thousand patients found that satisfaction scores increased across the board when conversational AI was used as part of the healthcare process. This was down to a combination of things that happened from the patient’s first interaction with their healthcare provider, right down to the treatment/outcome stage. 

For one, being able to interact with a chatbot during the booking phases allowed patients to find the right outcomes a lot faster than usual. Some discovered that they didn’t need an appointment, while others were able to book themselves in with the right specialist based on their symptoms. It reduced cases where patients visit a general health practitioner and then get palmed off to a physical therapist or a different specialist. Conversational AI allowed for better pre-screening, which meant someone with chronic back pain could go straight to an expert. 

Moreover, AI was used throughout the research phase by practitioners to better diagnose patients based on symptoms and studies. They could compare thousands of cases based on a patient’s symptoms and health history to determine the best treatment options. Again, it cuts down on cases when a patient is initially prescribed one course of treatment, only for that not to work, leading to another type of treatment. AI in the research phase can identify that certain patients with other underlying medical conditions have a poor track record when undergoing one type of treatment and usually do better with another one. 

Conversational AI is now used in almost every healthcare and scientific research organization, and it clearly carries benefits. However, as with all uses of AI, it still has a few concerns and drawbacks. There’s the ongoing debate about the ethics of using AI in healthcare, mixed with worries about hallucinations and accuracy. The good news is that, for now, AI is simply an extra tool that healthcare providers and researchers can use to improve their work/services. If it continues in this way, then it should only be beneficial for everyone.