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Conversational AI and its Positive Impact on Healthcare

Healthcare is an industry that faces a plethora of challenges. Be it in terms of patient handling, discovering new drugs, catering to the needs of patients with chronic diseases, and whatnot. Earlier, major healthcare challenges were believed to be solved by appointing more doctors or increasing consultation hours. But, this resulted in an increased load on doctors. And, ultimately didn’t help the whole situation.  

This highlighted the need for a system that would solve all challenges and not burden doctors, and healthcare staff more. Conversational AI in Healthcare branched the right advantages for this. And only a few years after its implementation in healthcare, it is providing some beneficial applications. Let’s hop on to discuss this in sheer detail. 

Virtual Assistants: Conversational AI’s Real Heroes

Virtual agents that can help patients online or via their devices are the best applications of conversational AI. These agents can help patients 24*7, can provide them with multilingual support, and so forth. These agents can also let patients get first aid medical help in unfavorable times, and at unusual hours. 

These play an important role while bridging the gap between patients, and doctors. From diagnostics to treating diseases, VAs are slowly streamlining healthcare. Conversational AI agents also assist in maintaining patient support, delivering online consultations, supervising prescription records, analytics generation, etc. 

These agents help healthcare facilities, doctors, helping medical staff, and patients as well. And, as per the reports, the healthcare virtual assistant market is expected to reach $1.7 billion by 2025! Let’s now discuss some of the recent developments in healthcare that are taking modern healthcare a level up!

Recent Developments in Healthcare 

Technology, AI, or conversational AI specifically is evolving every day. And, scientists are leaving no stone unturned to take today’s healthcare a level up. Let’s comprehend some of the new progress in healthcare– 

  • In 2017, HealthTap by opening an Asia Pacific Hub Office in Hamilton, New Zealand connecting New Zealanders with neighboring healthcare clinicians. This was through their phones, tablets, or personal computers. 
  • In 2018, a cloud-based speech recognition program was launched in Canada. Launched by Nuance, Dragon Medical One enabled physicians to rapidly capture a patient’s complete history! 
  • Introduced by Verint Systems and UCB, April is an app that has a patented and conversational virtual health assistant. 

One more AI-powered virtual health assistant is launched by Gravitas AI. TINA, developed on robust AI & ML principles, is helping a number of healthcare facilities, and professional doctors all over the country. 

It can gather data, deliver patient support & assistance 24*7, and can cater to patients of different locations due to its multilingual feature. It is platform agnostic, and hence propound no hassles in integration and deployment. Further, TINA can be customized according to your needs, and requirements which make it one of the best available conversational AI agents. 

Conversational AI: a Tomorrow for Healthcare 

With the speed with which healthcare is moving towards digitalization, conversational AI indeed is the future. The virtual assistants providing first-line support to patients, and helping doctors automate their regular tasks is commendable. 

The future of healthcare is indeed digital. And, conversational AI agents are an important contender. If you also want to leverage these agents for your healthcare business or want to deploy them in a hospital or your clinic, meet the team of Gravitas AI today! 

AI Healthcare

Conversational AI(TINA) for Healthcare: Retransforming with the intelligent AI

It was not much before when COVID hit the world and made everyone realize the importance of stable healthcare services. Though the advancements in the field of healthcare were already in process, COVID highlighted the need. And, since then the use of Conversational AI in healthcare has been on the road. 


One such AI principle being extensively leveraged in healthcare is Conversational AI! 


Conversational AI in healthcare

Believe it or not, conversational AI sets new applications in healthcare. Today, the rapid resolutions, service, and finding help online in minutes is the new “but-obvious” achievement. But, the healthcare sector struggles here! And, for that, it is now believed that conversational AI can help resolve the challenges, and retransform how healthcare used to operate! Let’s deal with both aspects in detail! 


Healthcare, and its challenges! 

Healthcare is the sector that operates with continuous risk and scrutiny! A new treatment technique comes up? An advanced diagnostic method? Or even a new online software? Everything in healthcare is seen through a lens of criticism. And, why should it not be? It would directly affect human lives! That’s where transforming healthcare becomes a challenge. 


But, recently, when the times took a 360 degrees turn and made everything available online a necessity, Healthcare also dug up, and found its solution in AI, and machine learning! Though the principles such as Data Science, data handling, and record keeping are also on the road in the healthcare industry, one of the most believed AI-based principles is conversational AI! And, it is believed that it can solve some of the healthcare industry’s challenges! 


Conversational AI; a solution for Healthcare! 

Healthcare is one of the top 5 industries that get leveraged by conversational AI, and that explains how significantly it can transform this sector. But, do you know? 


How effectively, or precisely conversational AI can benefit your business depends on how efficient the solution or Chatbot is! And, that’s what needs the mention of an efficient Chatbot; TINA! 

Meet TINA! 

TINA is an AI-powered chatbot designed by the experts of Gravitas AI! It is designed on the basis of robust principles of machine learning, and NLP makes it an efficient addition to healthcare operations. Here are some of the ways in which TINA can help retransform healthcare- 

  • Bridges the gap in communication

It has been long realized that the most challenging bottleneck of the Healthcare sector is providing patient support outside of Doctors working hours. And, conversational AI solutions like TINA can handle this well! 


TINA Can handle patients’ inquiries 24*7, that too in a patient’s comfort language This happens because of the functionality of TINA to be able to chat in over 20 languages. While eliminating language barriers, TINA also ensures the right patient-doctor communication. It ensures patients get the help even outside of doctor availability hours. 

  • First-hand support: Right time, right help! 

Conversational AI can indeed help, but the end game is Doctors. And that’s where the need of connecting patients to Doctors at the right time becomes a concern. 


TINA can seamlessly connect patients to doctors at the right time. It can do this while becoming a patient’s reliable first-hand support. So, that no patient feels unattended! 

  • Analytics

In today’s time, analytics, and data are the strengths! And, that’s what TINA provides Doctors! 


Once integrated, and enabled, TINA gathers critical data. This helps healthcare facilities, or Doctors to monitor, record, and also identify patients’ pain points. Also, this gives deep dive into critical insights that help one make healthcare services better, and more patient-centric. 

  • Operations Management

As far as operation management is concerned, Healthcare facilities, and even independent doctors struggle! But, with little automation, and optimization this can be solved! 


TINA does the same! It helps you manage everything from a single dashboard. Be it lead management, or handling day-to-day operations of healthcare facilities, it manages everything from end to end. This helps keep things recorded, and well-managed. That too with complete automation, and precision! 

TINA: For your Business’ smart future with Conversational AI in Healthcare

The future of healthcare is Smart! And, it’s time that medical facilities and Doctors also embrace the “smart” technology to serve their patients better. And, TINA seems like a reliable solution! 


With advanced tech, and NLP integrated, TINA can be your one-stop solution. It can be your tireless AI teammate that can handle operations, appointments, and first-hand patient support right with ease. That too, with complete automation, and precision. If you are also from the healthcare industry, meet TINA’s creator today to understand its complete potential. Also, dig more into how integrating TINA can retransform your healthcare services! Schedule a demo with experts now! 


AI: A blessing for Healthcare System

The repercussions of crippled healthcare are severe. In addition, the emerging cases of health casualties due to pollution, pandemic outbreaks are becoming recurrent. Such a present condition demands optimization and advancements of healthcare a necessity.

A significant time before, the proposal of incorporating tech advancements and innovations in the medical field has reflected a significant optimism. The amalgamation of tech innovations such as Telemedicine, health care apps, Artificial intelligence incorporated gadgets and software, and new machine learning principles have all been the focus of research. Fortunately, today, the medical field has seen some successful integrations of artificial intelligence and medical science that can fuel the human race’s well-being.

The principles of AI are well-implemented and explored in the medical field. It propounds some incredible applications that can elevate healthcare facilities to another level. Let’s dig deep into Artificial Intelligence’s application in the world of medicine.

AI bracing medical Research

 Every day, new medical research is perpetuated. To find robust findings and firm solutions to medical problems, drugs, etc. a careful study on massive sample size is done to cover extremes of the medical situations. During this, hefty data is generated that needs to be evaluated, sorted, studied, and analyzed.

Manual interpretation of data can be cumbersome and time-consuming. AI on the other hand, with the aid of deep learning can run rounds and rounds of interpretation and analysis on data that too in minutes. This can highlight some critical data findings which otherwise could get unexplored. These new findings can help developing drugs, gathering medical facilities, help in forecasting future medical conditions, and whatnot!

Drug development with AI

 The conventional drug development process consisted of several steps and took years and years of research without any confirmation and affirmation of an efficient drug. This exploited resources and time of research professionals. AI on the other hand can bridge this and can assist in the rapid development of drugs.

This can be done via analyzing early symptoms of diseases and establishing some critical data connections amidst small size to large sized samples. AI, in this way, can increase drug efficiency by facilitating drug discovery in the early stages of medical conditions thereby saving time and effort.

AI facilitating the process of disease diagnosis and treatment

This is one of the most useful and practical areas of AI’s involvement in medicine. The use of AI algorithms in the detection of critical diseases such as cancers, eye diseases, tumors has been discovered soundly. The AI’s capability in diagnosing a disease can save millions and millions of costs. Apart from mitigating the costs, it saves a number of lives as the detection of diseases during the early stages can aid in implementing treatment processes.

Not only diagnosis, but It can also assist in finding appropriate treatment methods for diseases. Specific AI algorithms can trace the widely available drugs and can then map their mode of action with the disease’s underlying symptoms. In this way, it can help in developing a treatment for diseases at the right moment thereby saving lives, costs, and time.

AI-driven Surgeries

Implementation of robotics and Artificial intelligence in surgeries can elevate accuracy and precision. Medical science is complicated and so are critical surgeries. A mere inaccuracy or carelessness in surgical processes can cost a patient a life. Specific AI algorithms equipped robots have been widely seen as the best alternatives for carrying out error-free, and stable surgical procedures.

This field is under extensive research and propounds some optimistic endeavors. We hope to see the elevation in AI-driven surgeries by the end of 2025.

AI giving hopes for stabilized future healthcare 

AI is unstoppable. Even after the implementation of some unbelievable principles into daily lives, AI still has dimensions completely unexplored. The medical field is experiencing the need for new inventions and advancements. In such a scenario, AI can become an optimistic companion.

The development of telemedicine apps to serve patients with quick opinions of healthcare officials, the implementations of virtual reality assisting in developing one-to-one doctors assistance, the development of AI-enabled gadgets to monitor health vitals, etc. all are fueling the hopes of a better and stable medical healthcare system.

Ankita Sinha, Co- Founder & CTO, Gravitas AI



AI in Healthcare: Data Protection Challenges

Through computer science, in reference to human intellectual ability, Artificial Intelligence (AI), also called Machine Intelligence is intelligence exhibited by computers. Founded in 1956 as an academic field, Artificial Intelligence has undergone many waves of excitement over the years, accompanied by frustration and loss of funding (known as “AI winter”), accompanied by alternative approaches, progress, and revived investment.

AI research’s typical concerns (or objectives) include inference, representation of information, preparation, reading, processing of natural language, perception, and the ability to move and process information.

AI research’s conventional challenges (or goals) include reasoning, representation of information, scheduling, training, interpretation of natural language, awareness, and the ability to shift and avoid obstacles. General intelligence is one of the lengthy-term priorities of the sector. The strategies include numerical, mathematical, and conventional conceptual AI approaches.

AI in healthcare

Artificial Intelligence’s ultimate research goal is to create technology that enables computers and machines to operate intelligently.

Descriptive AI

Descriptive AI is probably the most frequently used in biomedical innovation and has the most promising quick-term potential. It takes into consideration incidents that have already taken place and uses this information to gain more insight, such as identifying patterns and subtle changes that might otherwise be prevented by healthcare professionals.

Predictive AI

Predictive AI uses detailed data to try to foresee the future. Medical professionals use AI to provide information and recommend behaviours in a proactive manner. AI may play a vital role in predictive healthcare and hospital administration developments.

Prescriptive AI

Prescriptive AI fosters the intent of predictive AI, not only identifying patterns which may not be anticipated by individuals but also recommending potential clinical nuances-based treatment. This ability to create decisions makes prescriptive AI in the near term the most fascinating and contentious use case.


For medicine, there are growing magnitudes of Artificial Intelligence. AI can help in the creation of online services that allow physicians and practitioners to access thousands of therapeutic tools within the blink of an eye. In terms of efficiency, AI can assist a physician, offering both quantitative and qualitative statistics based on feedback, enhancing early diagnosis, precision in treatment and estimation of the outcome. AI’s capacity to “learn” from the information offers an opportunity to enhance effectiveness based on input responses. Medical care AI programs also work in real-time, which ensures that the information is constantly updated, increasing reliability and significance. Physicians have nearly unlimited resources to achieve their care capacity with the collection of continuously updated data.



Accessibility to detail is the limitation of AI in healthcare implementation. The primary data concerns involved in gaining consent and making the information secure and accurate.


The need for traditional design requirements for proposed AI systems has now been discussed by industry experts. Standards for different models can help to provide frameworks to ensure the AI approach towards privacy, security, performance and accuracy and address issues of ethics as well as trust.


The introduction and acceptance of AI pose a number of challenges. Some of these include the following:

  • Regulatory Authority: — Medical councils’ authority to regulate the clinical aspects, and a data privacy bill regulator to monitor data issues is required for effective solutions.
  • Infrastructure: — For instance, cloud computing architecture is mainly focused on outside servers. Delays in spending on indigenous resources lead to better access to technology and research for start-ups outside the nation.
  • Investment: — Expenditure, while increasing, is currently restricted in health-related AI and work is poorly funded and examined
  • Asymmetries and interpretations of data: — AI-based medical systems are most often faced with the challenge of asymmetric information between the physicians who use the technology and the programmers who designed the program. In fact, the understanding of AI innovations can be a primary cause of how successfully they can be used in care.

Therefore, design criteria are required to encourage the growth of reasonable AI. Key principles to encourage responsible AI include the following:

1. Transparency (user-visible operations)

2. Explainability (it is possible to trace the whole process followed by making a decision)

3. Suitability (comprehensibility)

4. Legitimacy (acceptable results should be there)

5. Auditability (it is simple to calculate efficiency)

6. Dependability (AI systems are operating as intended)

7. Recoverability (if necessary, manual control may be presumed)


Term “confidence”, sums up the greatest obstacle to the medical acceptance of AI. Patients don’t know whether they can trust new software to provide diagnoses, track their condition or interpret scans when nobody can explain in terms of how it works. A new Data Protection and Development Centre should be established to serve as administrator of data, including confidential information, to make it more accessible to entities within a set of standards and criteria so that the use of information is ethical.


Data protection standards allow entities (device and technology suppliers) to ensure that confidentiality is protected as a standard in any program and that it is configured to comply with the law. This is to prevent refurbishing measures of security into frameworks and code and to make privacy a prerequisite of layout instead of an afterthought.

Measures for physical and technical security should always be a fundamental principle of data privacy is to ensure that safety is sufficient to the quality of the data and the damage that could be incurred by misuse.


Artificial Intelligence has a variety of medical applications which can be carried out by analysing through descriptive, predictive and prescriptive artificial intelligence. AI-powered frameworks are followed by some challenges — they require an effective legal structure to regulate confidentiality and authentication while addressing issues of acceptability, medical intervention, transparency, and clarification. The steps needed to create a thriving health care environment for AI are-

  • Strong open data policy
  • Rigorous confidentiality regulation
  • Deploying labour forces with the relevant skills to embrace AI
  • Preparation for the improvements that AI will bring and a regulatory regime that maintains accountability and transparency but does not impede the advancement

Aarsh, Co- Founder & COO, Gravitas AI



How is data science reshaping the healthcare industry?

Data science has proven beneficial for various segments of the industries. Healthcare is one of the foremost sectors, which have benefited from big data analytics. In the contemporary healthcare industry across the globe, the demand for data scientists is escalating.

Data science can effectively strengthen this particular industry in several ways. From preventing epidemics to reducing costs and curing diseases, data science has extensively reshaped the modern healthcare industry. Data science, when applied to this particular segment, uses specific data of a population to generate valuable insights for decision making.

Presently, the healthcare industry generates around 30% of the warehouse datain the world. Leaders in the healthcare domain have realized the potential of data science. Integrating data science in healthcare can result in saving around $300 billion annually. Technology has generated the opportunity to leverage the healthcare industry. Business leaders have successfully implemented some of these technologies in the domain of healthcare.

What makes data science important in the healthcare industry?

In the healthcare industry, data analytics has been one of the most important revolutions in the last decade. Adoption of data analytics can pacify healthcare to a significant extent. Here are a few instances that show how data science is changing the healthcare segment.

  • The hospitals and healthcare organisations can analyse structured as well as unstructured data using various digital tools like EHRs.
  • Based on the available data, medical practitioners can make valuable predictions and inferences using analytics tools and machine learning.
  • In developed countries, analytics tools have already been used to enhance clinical outcomes and increase their efficiency.
  • Integrating cloud computing infrastructure, large volumes of data can be processed in real time. The data scientists analyse the same to generate valuable insights needed for decision making.
  • Presently, leadership in healthcare organisations has become technology-oriented. Hospital authorities are ready to embrace new tools and they are adopting new technologies.

Embracing data science: How is the healthcare industry benefiting from technology?

Here are some areas in the healthcare industry, where data science has already been implemented.

  • Medical image analysis

In medical imaging, the healthcare industry has derived great benefits, applying data science. Popular techniques of imaging include computed tomography, X-ray, magnetic resonance imaging (MRI) and mammography, among others. In order to deal with the differences in resolution, modality and image dimensions, various methods are used.

Other techniques are being used to extract information from images and enhance the quality of data. This results in obtaining interpretations that are more accurate. The diagnostic accuracy can be enhanced by integrating deep-learning algorithms, learning from examples to suggest better solutions for treatments. Popular techniques for image-processing focus on denoising, segmentation and enhancement.

  • Electronic health records (EHR)

HER is probably one of the most extensive applications of data science in the healthcare sector. This technology allows each patient to have a digital record. This includes medical history, demographics, allergies, results of laboratory tests and so on. Secure information systems are used to share these records.

Providers from both the private and public sector have access to these. A single modifiable file comes with each record. This enables the doctors to implement changes in course of time. This eliminates the possibilities of data replication and paperwork. EHRs have the ability to trigger remainders, in case a patient needs a lab test. They are also used in tracking prescriptions, so that it can be verified whether a patient is adhering to the orders of the doctors.

  • Real-time alerts

The leading hospitals use Clinical Decision Support (CDS) software to scrutinise medical data in real time. This saves time and helps the doctors make precautionary choices. For instance, if the blood pressure of a patient shows an alarming rise, the system will send a real-time alert to the medical practitioners. They will take a timely action, reaching the patient and taking the necessary measures to lower the blood pressure.

Analytics, therefore, has become a new strategy in treatment processes. The data from patients is collected by wearables and these are sent to the cloud.The hospitals use sophisticated technology to regulate this massive volume of data. Each time a disturbing result shows up in the form of alerts, the doctors take precautionary actions.

  • Improving patient engagement

In recent times, people have been more interested in smart devices and wearables. This has unleashed the opportunity for the healthcare industry to enhance patient engagement significantly. The smart devices can keep a track of their heart rates, number of steps and sleeping habits.

On analysing this information, the doctors can gain important insights about their health conditions. Integrated with other trackable information, this data can be used in identifying possible health risks. For instance, an increased heart rate or chronic insomnia can indicate the possibility of heart disease in future. Therefore, data analytics tools can help people enjoy a healthy lifestyle, where they can monitor their activities on their own.

  • Reducing healthcare expenses

Doctors are able to make informed decisions, when they use analytics tools for data processing. As a result, they can help the patients save costs. For instance, when data analytic techniqueswere used to optimise the process of knee replacement, the healthcare provider was able to make an annual saving of 1.2 million dollars.

The organisation can benefit immensely from techniques based on data science. It is for this reason that most of the reputed hospitals across the world are hiring seasoned data scientists.In the process, preventive analytics have turned out to be one of the core areas of data science. In the coming years, technologies are expected to grow smarter. Integrating machine learning and data science in the healthcare industry can eventually reduce treatment costs.


Other areas where data science has strengthened the healthcare industry is the creation of drugs, and virtual assistance for customer support and patients. Presently, machine learning algorithms are being used to process natural language and generate accurate information.

This can be used to map the patient’s condition accurately and deliver a personalised experience. Diagnostic accuracy has been enhanced manifold using data science. The coming years are likely to witness further sophistication of data science technologies.


Mukesh Kumar Sinha, Co- Founder & COO, Gravitas AI


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