By John P. Desmond, AI Trends Editor
Conversational AI has come a long way since ELIZA, which was intended by its creator in 1964 to be a parody of the responses of a psychotherapist to his patient, as a demonstration that communication between a human and a machine could only be superficial.
What surprised Joseph Weizenbaum of the MIT AI lab was that many people, including his secretary, assigned human-like feelings to the computer program. It is acknowledged as the original chatbot.
In the 50 years since then, chatbots have evolved first to engage users in dialogues for customer service in many fields, and now to dialogues on personal medication information. “With the advent of cognitive intelligence, chatbots were given a facelift. They were able to analyze context, process intent, and formulate adequate responses,” stated Pranay Jain, cofounder and CEO of Enterprise Bot, in a contribution to AI Trends. The Switzerland-based company was founded five years ago.
Still, chatbots incorporating AI today are challenged to successfully process technical commands, to understand human intent, to exhibit conversational intelligence and understand different languages, accents and dialects.
Today, “The ability to understand the subtle nuances of human tonalities, speech patterns, and mimic human empathy in the form of texts and voices is what makes a chatbot truly successful across industries and verticals,” Jain stated.
Chatbots in healthcare had been perceived as high risk, with healthcare professionals skeptical that patients would provide confidential medical information to a virtual assistant. “Today, chatbots are being designed and deployed to perform preliminary pathology and aid healthcare professionals,” Jain stated, noting that chatbots now gather initial personal information and then ask about symptoms.
Cloud Vendors Now Into Serving Medical Markets
Top public cloud vendors have developed technology to enhance patient and provider experiences, from diagnostics to appointment management to transcribing the notes of doctors.
For example, in December 2019, Amazon Web Services launched Transcribe Medical, a speech-to-text service enhanced with a medical vocabulary. This complements AWS Comprehend Medical, a fact extraction engine built to identify and code key clinical elements in text, according to a recent account from CB Insights.
In a similar way, Google’s Healthcare Natural Language API uses AI to identify and code clinical facts in written medical text. Meanwhile, with its $19.7B pending acquisition of Nuance, Microsoft now offers a full suite of voice-based patient engagement and provider documentation solutions. “These services are igniting a wave of innovation among startups, which are developing on top of the cloud-based offerings,” stated the CB Insights account.
The Dragon Ambient eXperience product offering from Nuance is used in healthcare settings, especially for transcribing doctor’s notes based on spoken conversations. It uses ambient sensing technology to listen to the conversations and offers some workflow and knowledge automation to complement the electronic health record of the patient.
“It automatically generates the notes that doctors need to write using natural language generation,” stated Guido Gallopyn, VP of Dragon R&D at Nuance, who leads the project, in an interview with AI Trends. “It takes the transcript of what the doctor and the patient say and translates it into a clinical report.” It’s ability rivals that of a human transcriber doing the same thing, he said.
VCs Have Invested Over $800 Million in Health Chatbot Startups
A recent analysis by Crunchbase found that VCs have invested more than $800 million in at least 14 known startups that offer some version of a chatbot with health features, according to a recent account in The Medical Futurist.
The rise in healthcare chatbots can ease the burden on healthcare professionals, the authors stated, through better organization of patient pathways, medication management, help in emergency situations or with first aid, or offering a solution for simpler medical issues.
Northwell Health in New York launched a chatbot incorporating AI to help reduce no-shows for colonoscopies, which were running at about a 40% rate among less-privileged patients, according to the Futurist account. The AI chatbot is being offered at Long Island Jewish (LIJ) Medical Center and Southside Hospital. Northwell states that the personalized chatbot will “encourage patients by addressing misunderstandings and concerns about the exam, delivering information in a responsive, conversational way over email or text”
The ability of conversational chatbots with AI to complete a series of steps across different channels makes them powerful. “AI-based conversational chatbots are able to converse with users in a highly personalized way, even completing transactions, and across all channels such as web, mobile, messenger apps, and voice,” stated Chris Ezekiel, founder and CEO of Creative Virtual, a supplier of the V-Person virtual assistant, in an account in Interesting Engineering.
“They are also implemented within contact centers as virtual contact center agents listening into conversations and advising on the best answer or next best action. The best deployments are where the AI and humans work in harmony to provide the best possible customer experience,” stated Ezequiel.
Other trends in conversational AI chatbots include, according to an account from Analytics Insight:
Greater personalization. Conversation bots are able to remember conversation context, past dialogues and user preferences. They can also understand sentiment and mood and respond accordingly, especially to cross-sell and up-sell products and services to users. .
Augmented reality is making its way into chatbots, such as to show how a coffee table might look in your living room, or how some new clothes would fit you. Organizations including IDEA, Zara, Loreal, and Amazon are testing its potential.
Business users in chatbot development. No longer the exclusive domain of developers and linguists, chatbot creation is now including business users who are closer to understanding customer needs, to make the chatbot more engaging. This includes script writers who guide the flow of conversation through brand value to open-ended questions.
Selection of Healthcare-oriented Chatbots Described
A selection of healthcare-oriented chatbots incorporating AI was assembled by The Medical Futurist site, including:
Babylon Health. This British subscription, online medical consultation and health service, founded in 2013, offers AI consultation based on personal medical history and common medical knowledge as well as live video consultation with a doctor when a patient needs it.
Users report symptoms of their illness to the app, which checks them against a database of diseases, then offers an appropriate course of action. The UK’s National Health Service (NHS) began using the chatbot to dispense medical advice for a trial period in 2017. During the COVID-19 spread in 2020, the NHS launched an app-based AI triage model with Babylon, which is expanding in the US and worldwide.
Ada Health. Over 1.5 million people have tried the health companion app, which aims to help assess the user’s health based on the indicated symptoms using its vast, AI-based database.
Daniel Nathrath, CEO of Ada Health, based in Berlin, Germany, stated to The Medical Futurist that in the future, “Ada will become a standard diagnostic tool for doctors. That is already the case; users can share their health assessment with their doctor or, in the UK, they can choose to consult with a qualified GP via our Doctor Chat feature. Ada will also become much more of an ongoing health companion, helping patients and doctors to intelligently monitor health data over the long term to enable predictive and proactive care.” A voice interface allows users to trial Ada through Amazon Alexa.
HealthifyMe is India’s top health and fitness app, but the company’s long-term goal is to become the global leader, according to an account in TechCrunch. It is popular among Indian expat and Indian American communities and it will target other customer segments after raising $75 million in a Series C round recently, co-founder and CEO Tushar Vashisht stated.
HealthifySmart and HealthifyStudio, its newest products, now contribute 25% to the company’s line. Its user base and revenue has doubled over the last year, recently surpassing 25 million downloads, and is currently on target to reach $50 million in annualized recurring revenue within the next six months. It has about 1,500 trainers and coaches on the platform, with plans to add 1,000 more to support its expansion.
“Today in the U.S., you have free DIY calorie counting solutions like MyFitnessPal and expensive human-assisted coaching and diet solutions like Noom and WeightWatchers,” stated Vashisht. “But nothing in the middle exists that allows one to track nutrition and calories while getting advice at an affordable price point.”
Under the Hood of Enterprise Bot
Pranay Jain, cofounder and CEO of Enterprise Bot, offered these responses to some queries from AI Trends about his product.
How does the product achieve advanced natural language processing (NLP)?
NLP allows a computer algorithm to understand and interpret a user’s request. Enterprise Bot
uses a variant of Bidirectional Encoder Representations from Transformers or better known as BERT. BERT was created and published in 2018 by Jacob Devlin and his colleagues from Google and was then open-sourced. Using a bidirectional transformer allows us to truly understand the context of different words. Let’s look at two simple phrases “Book me a ship” vs “Ship me a book”. If you utilize a keyword approach or do not keep the context of how words co-relate you may not be able to do what the user really wants.
How does the product achieve sentiment analysis?
To achieve sentiment analysis, the Machine is given tens of thousands of examples of different sample datasets that help the machine learn examples of different sentiments. Using this sample data, the algorithm is able to understand and create a model of how different words are connected and is able to understand the sentiment. It also learns from other signals such as capitalization, exclamation marks, and many other indicators. The beauty of AI based on sentence structure is that it can understand the difference between what a teenager would mean by saying the word sick, and does not assume directly that this is related to a negative emotion.
Does Enterprise Bot make any use of the GPT-3 large language model?
We have benchmarked against GPT-3. [GPT-3] is an excellent resource for a generic model, but when you have industry-specific, pre-labelled data with a focused deep learning model, [Enterprise Bot] outperforms GPT-3. GPT-3 is still one of the best models out there for untrained and unlabeled data, but for large enterprises with specific goals, it presently scores lower than some other algorithms, including BERT.
Anything else to mention about the AI tech underlying the product?
AI is changing every day. Better AI models and higher training are enabling us to move from supervised to unsupervised models. Enterprise Bot is launching a new product soon that shall showcase how you can go live with a truly intelligent chatbot without having to spend days feeding it with samples and responses. This will truly change how conversational AI chatbots are made, updated, and improved in a way never seen before.
Is anyone using Enterprise Bot in healthcare?
Yes, we are presently piloting the solution with healthcare providers and we will be going live with a major healthcare provider to their customers in the next couple of months. Due to an NDA, we cannot reveal their name until then.
About Enterprise Bot
Enterprise Bot currently has diverse clients including Generali, Afterpay, and London North Eastern Railway (LNER). The company employs 70 people between Switzerland and India. The company generated €2 million in revenue in 2020, with over €4 million predicted for 2021.
Learn more at Enterprise Bot.