Dr. Cherian, Ms. Shraddha, and Mr. Rohit Ghosh, who joined remotely, were welcomed by Mr. Sachin Gaur, Executive Editor of InnoHEALTH magazine. He emphasized the importance of using AI in the medical industry with a brief overview of the meeting’s agenda  and the flow of the session. The questions planned to be asked  to the experts were in the realm of comprehension, such as what it takes to make an AI product successful in a clinical setting? From a technical standpoint as well as in terms of the actual obstacles and challenges they confront. Participants in the meeting are more likely to obtain insights and learn some crucial lessons if they are aiming to create a business.

Mr. Gaur welcomed the first panelist for this club meeting Mr. Rohit Ghosh who is the founding member and Chief strategy officer of Qure.ai.

Mr. Rohit discussed the difficulties they confront on the ground while installing AI. He added that Qure.AI has deployed AI in approximately 50 countries and 500 hospitals in the United States, the United Kingdom, Europe, Africa, Southeast Asia, and Asia. For smooth running of the installation, he devised a plan to comprehend some of the problems and lessons learned in due course and those he will be sharing during the session.

He initiated his questionnaire session with the first question on AI, “Do you need to enlarge the data sets for training?” “Could you explain the difficulties here”? He reciprocated that indeed the datasets for training are the most important item for any AI company. His company Qure.Ai has almost finished processing 4.2 million photos for a chest x-ray algorithm they developed. He underlined the need of using data sets for training, as this leads to accuracy. Although delta improvement necessitates a large amount of data where any amount of data counts. 

In response to the second question, “How can we measure completeness of data, representation of groups, and other such things?” He explained that complete data is a theoretical concept where fluctuations such as regional, disease, and seasonal variations, are data sets that should be addressed more. He underlined the hardship to track down all of the data.

His next question was, “How objective are the ground truths of your training data sets, and what can you do to improve the quality objective of ground truth?” In response, Mr. Rohit stated that in AI, you must have an objective function, however in real life or reports, ground truths are not always as objective, such as when radiologists do not always take complete background of the case. So, the need to train any algorithm becomes important. Now, to improve their ground regularity quality, they’ve standardised ground truthing techniques, such as having a panel of radiologists review reports instead of just one. Another thing they have implemented  is to construct a complete NLP ( National Language propository) terms that they use to represent such findings. Therefore it uses multiple reads instead of one to get the objective that a person normally gets from physicians.

“Are the outcomes the system gives explainable and interpretable to clinicians?” comes the next inquiry. Do you have a way to visualise and explain them in a more user-friendly interface or report”? According to Mr. Rohit, explainability is at the heart of machine learning and AI research at the moment, but in his interactions with physicians and radiologists, it is a minor problem because clinicians are already familiar with AI medical imaging.

The next question is what happens when AI and physicians disagree. Is it true that they provide feedback? He justified the query by explaining that there are times when AI and physicians disagree, but just because one result differs from the other does not mean the AI is erroneous. So they have a discordance meeting to discuss the cases that are discordant. Then it’s assessed by a panel, which gathers any discrepancies and trains the AI to release future versions.

The next topic was how to provide feedback on your system’s performance in a clinical situation. The discordance meeting has already been explained by him and there is also post-market surveillance alongwith a FDA regulatory approval for the algorithms. A subset of everyday assessments is also examined by a panel in order to determine whether AI is making the correct decisions. Qure reads exam samples and then rereads them. AI is just used to ensure that the quality is up to par on a daily basis.

“Does Deployment Change Care Pathways?” was the next question in the discussion. Is there a way to retrofit or intervene? In response, Mr Ghosh elucidated  that retrofitting and intervention are both possible as it alters care patterns in some regions. Qure.AI has been able to make a difference since receiving WHO approval for TB diagnosis. The entire TB diagnosis takes one hour.

Finally, what value does your technology add to the healthcare process, such as improving the quality of clinical decision-making systems, automating manual processes, or something else? What do you do to build consensus on the impact? In your perspective, clinicians perceive a gain to the extent you foresee, so what do you do to build consensus on the impact? They’re basically increasing patient outcomes, according to Mr.Ghosh. 

At Qure.ai, one of the use cases is to reduce work burden and manual labour. Radiologists’ turnaround time should be reduced so that reports may be produced more quickly and accurately. Early detection of severe disease and prompt treatment are essential.

In AI, there is a lot of agreement. There is a lot of maturity in the ecosystem right now. Rohit’s part of the meeting came to an end with that.

What value does your technology add to the healthcare process, such as improving the quality of clinical decision-making systems, automating manual processes, or something else? What do you do to build consensus on the impact?.

Sachin Gaur moderating the session welcomed the next panelist Dr. Cherian, Co-founder at Synapsica

Dr. Cherian introduced himself and gave an overview of Synapsica’s work. In terms of the data sets, he and Mr. Ghosh had different viewpoints. He told us that they have enough data and are working to extend their data sets so that they can build more features and capabilities in AI using the tools they already have. He noted that data preparation, objective ground truthing of data cleansing, and knowing how it will impact your AI system not just in terms of money but also in terms of time are all expensive inputs into the system so it is critical to maintain a sense of equilibrium. From a medical standpoint, adding additional data does not necessarily imply that the AI’s output will improve. You can construct more accurate algorithms by using updated algorithms and technological advances that can be used for learning from more data sets. The output is influenced by the quality of the algorithms.

Dr. Cherian agreed with Mr Ghosh that there is no clear technique to measure the completeness of data sets while responding to the next question. The only way to know if your AI is functioning well enough on the data it has been fed is to conduct a real-time clinical setting trial.

Moving on to the next question, he told us that at Synapsica, they do multiple rounds of annotation and take intermittent consensus to achieve an objective to use a true analogy as AI is like a dumb kid, and if one want that dumb kid to excel in trials where it is tested against multiple radiologists, then one would have to hand hold the AI to learn from multiple radiologists rather than a single radiologists. We compared our results to ground rules established by several technologists, which is one simple means of ensuring objectivity in the ground truths put into the AI system.

The method you use to compile your data or ground truth also contributes significantly to objectivity. When looking at the photos, picking out the observations is fairly objective. People can recognise the description by looking at the image, then use the description in conjunction with current medical criteria to come up with an interpretation. This also aids in the development of AI that is more understandable.

In addition to the answer to the next question, Dr. Cherian stated that the majority of AI businesses are preparing annotated photos, highlighting specific areas, and using masking technologies so that radiologists can see and comprehend the problem. They also provide radiologists with engagement, which they believe is vital as every AI outcome won’t be accurate all of the time. He went on to say that they think of AI as a junior radiologist in training who provides a report, which is then reviewed by senior radiologists who make modifications. We may learn where we are going wrong and what needs to be fixed by using feedback. The next question was answered by this.

Moving on to the following question: How does your deployment alter the care pathway, and can it be retrofitted? Yes, he replied, we can refit. While looking at the results of AI, radiologists should not switch to different systems because any or all of the efficiency gained from AI will be lost. In response to the question of changing the care pathway, he added that most AI solutions will improve the efficiency of the existing pathway and, in the next step, possibly change the overall clinical care pathway.

Moving on to the last question, Dr. Cherian explained that their AI system focuses on improving the efficiency of radiologists in reading and interpreting this type of exam, which is their main focus. They were able to achieve their goals of reducing the 15 minute time taken to 7 minutes for today’s cases by radiologists, and it involves automation of the manual processes that a radiologist will typically spend while reading and interpreting those types of exam. He also stressed the need to reduce burnout. A number of disorders may be made more sensitive with AI.

He mentioned that reaching a consensus is difficult, especially when it comes to radiologists who have been working in a certain way for a long time, and their work was done in a different way with AI. Now that they have resumed work and have worked for a long time, AI comes in and asks them to change their work behaviours, that is the most difficult part. The best part of AI is to have a documented proof of accuracy for the items, which will provide the professional the confidence in using the product. Apart from all this there is another issue to consider is for usability. With aforementioned words Dr. Cherian’s session came to a conclusion.

There are times when AI and physicians disagree, but just because one result differs from the other does not mean the AI is erroneous.

Mr. Gaur invited next and last panelist for day’s session Ms. Shraddha Mittal, Implementation Associate CARING Analytics platform(CARPL).

Ms. Mittal began by highlighting some of the hurdles that these AI solutions face on a regular basis when it comes to using them in real-world clinical workflows. She stated that CARPL is trying to become a single enabling player that provides healthcare providers global access to the greatest AI in medical imaging solutions while also ensuring that these AI solutions are seamlessly integrated into their day-to-day imaging workflow. She went on to say that they are in the process of deploying these solutions throughout their partner hospital sites around the world, resulting in CARPL being used in various locations on many continents. They are stationed at Thomas Jefferson University’s academic centres in the United States. They’re collaborating with Stanford’s Army Center, Mass General Hospitals, and other institutions in the area. She went on to say that they are highly active in Brazil at Albert Einstein Hospital and other imaging centres across the world. They are used in India at various hospitals and the Mahajan diagnostic chain.

Some of the issues, according to Shraddha initiates as healthcare providers are unaware of the existence of these AI solutions and their access alongwith with the knowledge to integrate the AI solutions into their daily workflow.

She described the lifecycle that CARPL conducts to effectively integrate AI solutions into hospital medical imaging operations with an attempt to add value to both AI developers and healthcare providers in this ecosystem. The IT infrastructure, she explained, is a key hurdle when it comes to deploying AI technologies in the healthcare ecosystem. As a result, they tend to shorten this period, and their relationship with AI partners is structured in such a way that they want them to concentrate on integrating their solutions. Then it’s up to them to spread that answer to as many hospitals as possible around the world. After that, they help with the integration of the AI technology into a hospital. 

She mentioned that CARPL allows AI engineers to concentrate on designing more robust solutions as well as the deployment side of moving those solutions from the bench to the clinic. She informed us about the projects they are presently working on, as well as how CARPL can be used as a single interface to provide feedback from all around the world to AI developers in real time. When it comes to onboarding solutions, she stated that they are always on the lookout for high-accuracy solutions, ideally with FDA and CE licences. They’ve also assisted a few businesses in obtaining FDA approval. She finished by stating that CARPL is expanding into a variety of fields.

AI is a new way of thinking that needs to go, but that it should be remembered as a tool to assist medical professionals, not as a replacement for medicine, medical personnel, or doctors.

The club meeting then progressed to Q&A sessions.

Mr. Gaur and Dr. Singh wrapped up the meeting. Conclusive note by Mr. Sachin stated that AI in science is about knowing what we don’t know, not about money or productivity.

After that, Dr. V.K. Singh thanked the panellists and participants and elucidated that AI is a new way of thinking that needs to go, but that it should be remembered as a tool to assist medical professionals, not as a replacement for medicine, medical personnel, or doctors. He stated that he has faith in our people because of the vast amount of data we have because some of our states have more people than any other country. He thanked everyone for their participation in the meeting.

Composed by: “Clarion Smith Kodamanchili.”

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The research facility will initially focus on developing AI tech for neurology. This will bring out yet another opportunity for the startups working in this field.

Aster CMI Hospital, a multispeciality hospital under the Aster DM Healthcare Group in Bangalore, India, has set up an artificial intelligence lab in collaboration with the public university Indian Institute of Science.  According to a news release, the Aster AI lab aims to build AI healthcare tools and train healthcare professionals in AI. It brings together IISc’s expertise in AI development and Aster CMI’s clinical competence to “aide, translate, and validate the developed AI algorithms for clinical use”. 

The research facility will initially focus on developing AI tools for neurology and will later expand to other clinical specialities, said Dr Phaneendra K Yalavarthy, professor of medical imaging at IISc. It will support ongoing research projects, such as the “Development of Deep Learning Methods for Automated Tracking and Segmentation of Nerves in Ultrasound Images” and “Automatic Acute Stroke Symptom Detection Using Mobile Health Technologies”.

WHY IT MATTERS

In recent years, India has been increasingly investing in AI for various industries, including healthcare. Most healthcare leaders in the country, according to a report by Royal Philips, said their health centres are ready to invest in AI technologies with the aim of optimising operations, integrating diagnostics, and predicting patient outcomes. However, adoption is impeded by difficulties in data management and a lack of systems interoperability.

Dr Azad Moopen, founder and managing director of Aster DM Healthcare, said the Aster AI lab will “open doors for healthcare professionals to undertake research and better utilise AI tools to understand their patient’s disease patterns and improve treatment outcomes significantly”. Dr Yalavarthy added that it will “enable the development of highly impactful research and technologies with a focus on translation to the bedside”.

Following the launch of the AI lab, Aster CMI and IISc will later work on exchanging personnel and conducting joint workshops to increase the cooperation in improving AI for clinicians and patients, according to Aster CMI consultant neurologist Dr Lokesh B.

THE LARGER TREND

Aside from India, other health systems in Asia are also setting up AI labs to harness large volumes of health data into improving patient outcomes. The National Heart Centre Singapore has opened the Cardiovascular Systems Imaging and Artificial Intelligence research laboratory, which aims to enhance the precision of predicting and identifying cardiovascular diseases. 

The Hong Kong government through InnoHK has provided funding to set up a health AI and robotics data research laboratory under the Laboratory of Data Discovery for Health, a joint research project by the University of Hong Kong and partner universities in London and Sydney.

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The issuance of the draft UCMDMP indicates that the government has taken cognizance of the fact that a separate code is needed which can accurately capture the ethical marketing practice required for the medical device sector.

The Department of Pharmaceuticals (DoP) on March 16, 2022, published the draft UCMDMP which is intended to be a voluntary code to regulate fair marketing practices by the Medical Device industry. The marketing practices of the Medical Device Sector are currently being voluntarily regulated by Uniform Code for Pharmaceuticals Marketing Practices (UCPMP) which is a code based on the marketing dynamics of the pharmaceuticals industry. The medical device industry is an important keystone in the healthcare delivery system and is inherently different from pharmaceuticals. The issuance of draft UCMDMP indicates that the government has taken cognisance of the fact that a separate code is needed which can accurately capture the ethical marketing practice requirements for the medical device sector. As per the order issued by DoP, stakeholder comments have been sought till April 15, 2022.

Pavan Choudary, Chairman & Director General, MTaI said, “The announcement that the govt is following through the implementation of the UCMDMP voluntarily (which is the right way to go about it) is heartening for every company which follows a high level of ethical standards. It will surely translate into more credible healthcare delivery as well as restrain the fly by night operators- who pose a great risk for patients and the reputation of the medical device industry.”

“In times to come, we hope it will separate the chaff from the grain and give the ethical players the public esteem they deserve. Its impact will hopefully also spill over and check those operators who have found a way to circumvent the price control affected on scheduled medical devices in this government’s regime.” Choudary added.

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The new buzzword is ‘Metaverse’. Metaverse may be the next big thing, and lots of implementation steps have been made in various industries but it will take another decade before it’s being used actively in the hospitals or medical practices.

The healthcare technology industry is constantly evolving due to new innovations in the medical space. From MRI scans and X-rays to robotic surgeries and virtual reality, the healthcare sector is seeing massive digital transformation. As per a study, the US healthcare spending is estimated to touch $8.3 trillion by 2040 from $4 trillion in 2020 due to the adoption of new and emerging health technologies.

The announcement of an emerging technology — the Metaverse by the CEO of Facebook, Mark Zukerberg — triggered its potential usage in the healthcare sector. Metaverse is a promising technology and holds lucrative opportunities in the medical field. This can be used to address certain issues such as mental health, access to health without geographical limitations, virtual wellness, and fitness, and just to connect with people.

Metaverse is a parallel reality or visual representation of reality, however it’s neither augmented reality (AR), virtual reality (VR), or MR (mixed reality), but an amalgamation of these technologies that also includes artificial intelligence as well. Further, this technology requires another 10 years of work before people can access it in games, shops, travel, and the healthcare sector. Currently, medical facilities are using VR and MR to assemble medical tools or to design a surgical room and operations.

The World Health Organization is utilizing AR to train COVID-19 respondents and VR is actively used by the mental health experts for treating PTS (post-traumatic stress), and medical schools use it for education. Likewise, metaverse in the healthcare sector is predicted to help healthcare professionals in numerous areas via a combination of these technologies. Hence, metaverse has the potential to be the next frontier in the healthcare sector.

Here are three areas in which the metaverse is likely to gain dominance in the healthcare industry.

Surgical operations

Currently, surgeons use technologies such as AR, VR, AI, and minimally invasive surgeries for augmenting patient outcomes. Leading hospitals and universities use these technologies for surgeries because this gives a 3-D view of a patients’ body, and helps to interpret surgeries, plan, and perform them. VR and AR simulation is used for surgical training and is widely used for efficient, safe, and measurable medical training. However, VR or AR comes with unique technical challenges in the healthcare field.

These obstacles include creation of realistic physical objects and surgical interfaces within a computer-generated space, representing interactions between items, and processing signals for complicated events during surgeries.

Furthermore, VR systems are limited to specific clinical settings and mobile VRs offer pocket size immersion. Resolution of devices and computer specifications are limiting aspects for medical facilities. These challenges can be overcome with the intervention of the metaverse in the healthcare sector as it offers space and realistic interaction between doctor — patient, including objects. Thus, making it easier to explain surgical operations to patients, along with complications if any.

Surgical operations

The metaverse raises interesting concerns regarding mental health such as the following:

• Addiction to virtual realities
• Harming people suffering from mental disorders such as psychosis, schizophrenia, depression, or anxiety
• Revolutionizing mental health treatment altogether

Metaverse in mental health can be used for the treatment of various brain issues such as phobias, PTSD, anxiety disorders, hallucinations, and delusions. Technology has changed the way we seek support from friends, family, colleagues, or healthcare professionals during mental breakdown.

Companies working in the virtual reality space are devising mental wellness applications for metaverse as this technology holds potential benefits for our wellbeing. For instance, in February 2022, Dr Lisa Cortez aka The Anxiety Dr, declared the launch of the Moody Minks, which is an NFT collection to bring mental health in the metaverse. Moody Mink Society is set to become home to the first META-tation center and NFT holders will be able to access events held by The Anxiety Dr. The visitors of metaverse can interact with each other and play games, attend virtual events, and visit galleries.

Metaverse has an interactive nature and provides an arena for online therapy, improves access to therapy for disabled people, and renders a life-like experience. Usage of psychotherapy formats in the metaverse can help individuals with phobias, stress, addiction, eating disorders, psychosis and much more.

 Medical education and training

Metaverse application in medical education and training would be to produce an augmented reality space to examine the anatomy of a human body in a laboratory setting. This technology holds potential opportunities for creating a new educational environment. This new environment offers space for communication, provisions for new experiences, freedom to share and create, and high immersion. Challenges suggested are privacy impingement, maladaptation to the students in real world, and commission of crimes.

VR is being used to train doctors and medical providers by stimulating real procedures and displaying cellular level data of the human anatomy. Currently, AR has made its way in the medical school curriculum and has shown a positive outcome to medicine. For instance, Novarad Corp., a medical imaging software company, announced the launch of its AR software for higher education.

Likewise, a combination of these two technologies in the form of metaverse will augment overall medical education in the coming years. Medical teachers can use this technology to help students solve problems, perform projects, build creativity, and create a learning space for all students. No doubt, metaverse is the next big thing in not only the medical field, but it will take another decade before it’s being used actively in the hospitals or medical practices.

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Over the years, Google has proven to be a relentless force pertaining to innovation. The company continues to find new ways to provide value to billions of people worldwide. Its latest venture is leveraging its Care Studio platform to build new partnerships and create value in the healthcare space. The platform applies the classic Google narrative to healthcare: integrating robust search features, making information digestible, and organizing data in more useful ways.

As the company describes it, Care Studio “leans on Google’s expertise in organizing information to help clinicians find health record information faster. The tool’s Clinical Search feature enables clinicians to simply type what they’re looking for and quickly find the specific information requested, and even related concepts […] Our tools give clinicians a single, centralized view that automatically brings forward a patient’s important information — including hospital visits, outpatient events, laboratory tests, medications, and treatment and progress notes. And the intuitive interface offers unique ways to visualize health data and trends in tables, graphs and other helpful formats.”

The company has painstakingly developed the platform in order to specifically address the obstacles that many current healthcare IT systems face. Now, it is slowly ready to use this system to build new parternships. At the famous Healthcare Information and Management Systems Society (HIMSS) conference last week, Google announced its intention to partner with MEDITECH, a healthcare IT giant and prominent electronic health records (EHR) systems pioneer.

The press release explains: “With MEDITECH, we’re working on a deeply integrated solution to bring some of our data harmonization, search and summarization capabilities to their web-based EHR, MEDITECH Expanse. Using Google Health’s tools, MEDITECH will form a longitudinal health data layer, bringing together data from different sources into a standard format and offering clinicians a full view of patient records. And with Google Health’s search functionality embedded into their EHR, clinicians can find salient information faster for a more frictionless experience, and the intelligent summarization can highlight critical information directly in the Expanse workflow. This will help advance healthcare data interoperability, building on MEDITECH’s vision for a more connected ecosystem. Our collaboration expands on the partnership between MEDITECH and Google Cloud and will utilize Google Cloud’s infrastructure.”

Indeed, this one-of-a-kind partnership is certainly a promising milestone for Google. Although Google has already forged strong relationships with numerous healthcare companies through various other verticals (e.g. cloud, data science, services, etc.), the company is relatively young in its EHR and health IT related ventures. Google’s partnership with a large company like MEDITECH signals a level of product maturity, indicating that the company is getting ready to scale its platform to new frontiers.

MEDITECH is a widely used platform, serving some of the largest healthcare organizations across the globe. As Google enters this realm, the possibilities are endless. Take for example Epic, another healthcare IT company that has exponentially grown its presence in the health sector over the last decade. Epic’s EHR system is used by healthcare organizations worldwide and is ripe for collaboration with partners such as Google.

Of course, the pipeline for Google’s product is still to be determined. Experts are questioning whether the company will eventually aim to launch the Care Studio product as a stand-alone EHR platform that can compete with other established systems, or rather, continue to partner with existing players as it is doing right now. Time will tell. Nevertheless, as Google has proven time and again, disruption is just around the corner. Certainly, healthcare IT is ripe for change, and Google will undoubtedly have a significant role to play in the years to come.

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We have heard somewhere or the other that the data is the new oil. But the task of leveraging data for medical innovation is something which is associated with regulatory and technical hurdles. This can be another push that can lead India to lay down a well-defined data policy and help in uplifting the power of healthcare data and its analytics. Bozeman, Montana-headquartered data company Snowflake has expanded its “data cloud” ecosystem with the launch of a dedicated offering for healthcare and life sciences industries.

Officially dubbed Healthcare & Life Sciences (HCLS) Data Cloud, the product aims to provide enterprises in these sectors with a single cross-cloud data platform to centralize, integrate and exchange critical and sensitive data at scale. It is tailor-made to solve key challenges that keep healthcare enterprises from leveraging data for innovation and continues to be backed by various technology, data, application and consulting partners, including Equifax, Dataiku, H20.ai, Cognizant, Deloitte and Strata.

Need for HCLS Data Cloud

For most healthcare or life sciences companies, the task of leveraging data for medical innovation can be described as one associated with regulatory and technical hurdles. The firms often rely on legacy architectures (that keep data in fragmented siloes) and have to follow stringent compliance rules, with no common models for data sharing with the industry. This makes downstream use difficult, affecting advanced analytics and AI projects, including those for patient care and optimizing clinical and operational decision-making.

Snowflake’s Healthcare & Life Sciences Data Cloud solves these challenges by providing an agile and interoperable solution that facilitates the storage, sharing, and use of data. It eliminates technical and institutional silos while ensuring the security, governance and compliance required to meet industry regulations. In all, Snowflake’s data cloud ecosystem facilitates six key workloads — capabilities of a data warehouse, data lake, data engineering, data sharing, data science and data app development.

“The Snowflake Healthcare & Life Sciences Data Cloud will unlock the next generation of innovation in the industry by enabling organizations to take advantage of borderless data access while ensuring strict data governance, security, and privacy compliance,” Todd Crosslin, Healthcare and Life Sciences Industry Principal at Snowflake, said. “The entire industry can benefit from this live, connected ecosystem to get access to the data they need when it’s needed.” The solution is already in use by various organizations, including Anthem, IQVIA, Komodo Health, Siemens Healthineers, Spectrum Health, Novartis and Roche.

Snowflake’s competition

The launch of Snowflake’s HCLS Data Cloud comes just a week after Databricks’ launch of lakehouse for healthcare and life sciences. The two companies are set out for the same goal – to be the one-stop shop for all thing’s enterprise data. In fact, both Snowflake and Databricks, led by Ali Ghodsi, have been expanding their product ecosystem to cover more verticals and use cases. The former recently announced the acquisition of Streamlit to simplify data app development, while the latter has debuted lakehouse for retail and financial services, as well as a solution to integrate partner data tools with ease.

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