श्री सिद्धार्थ श्रीनिवासन, सीईओ, लुपिन डिजिटल हेल्थ (LDH), तकनीक के माध्यम से रोगियों के बेहतर परिणाम सुनिश्चित करने पर केंद्रित डिजिटल थेरेप्यूटिक्स पहलों का नेतृत्व कर रहे हैं। टेक-ड्रिवन व्यवसायों को बड़े पैमाने पर आगे बढ़ाने का अनुभव रखने वाले वे इससे पहले एक दशक तक टाटा समूह से जुड़े रहे। उन्होंने एस.पी. जैन से पीजीडीएम और वीएनआईटी से बी.टेक की डिग्री प्राप्त की है। डॉ. सौम्या सिंह, क्रिएटिव एडिटर, उनसे स्वास्थ्य सेवा में डिजिटल थेरेप्यूटिक्स की भूमिका पर बातचीत कर रही हैं।

भारत में शहरी और ग्रामीण क्षेत्रों में डिजिटल थेरेप्यूटिक्स किस प्रकार स्वास्थ्य सेवाओं को बदल रहे हैं?

डिजिटल थेरेप्यूटिक्स भारत में स्वास्थ्य सेवाओं के परिदृश्य को तेजी से बदल रहे हैं।

• शहरी क्षेत्रों में टेलीहेल्थ और मोबाइल हेल्थ सॉल्यूशन्स का व्यापक रूप से उपयोग हो रहा है।
• ग्रामीण क्षेत्रों में भी धीरे-धीरे टेलीहेल्थ और डिजिटल थेरेप्यूटिक्स का अपनापन बढ़ रहा है, क्योंकि लोग विशेषज्ञ डॉक्टरों की सेवाओं तक पहुँच बनाना चाहते हैं।

अब ध्यान संक्रामक रोगों से हटकर गैर-संक्रामक रोगों (NCDs) पर केंद्रित हो गया है, जिनमें कार्डियोमेटाबोलिक बीमारियाँ (जैसे उच्च रक्तचाप और मधुमेह) शामिल हैं। इन स्थितियों से भारत की बड़ी आबादी प्रभावित है, और डिजिटल थेरेप्यूटिक्स इन्हें नियंत्रित करने में महत्वपूर्ण भूमिका निभा सकते हैं।

इसके अलावा, श्वसन रोग, महिला स्वास्थ्य, कैंसर, नवजात शिशु देखभाल और मानसिक स्वास्थ्य जैसे क्षेत्रों में भी उल्लेखनीय प्रगति हो रही है।

कार्डियोलॉजी (हृदय रोग विज्ञान) में डिजिटल थेरेप्यूटिक्स की क्या भूमिका है?

हृदय रोगों में डिजिटल थेरेप्यूटिक्स तीन प्रमुख पहलुओं में मददगार हैं:

• सक्रिय निगरानी (Active Monitoring): मरीज अपने लक्षण और महत्वपूर्ण संकेत (Vitals) वास्तविक समय में साझा कर सकते हैं, जिससे सही समय पर हस्तक्षेप संभव होता है।
• अनुपालन (Adherence): यह सुनिश्चित करता है कि मरीज नियमित रूप से दवा लें और समय-समय पर चेकअप करवाएँ।
• जागरूकता और पुनर्वास (Awareness & Rehabilitation): मरीजों को जीवनशैली सुधारने और रिकवरी में व्यापक सहायता प्रदान की जाती है।

डिजिटल थेरेप्यूटिक्स प्लेटफ़ॉर्म में डेटा गोपनीयता और सुरक्षा कैसे सुनिश्चित की जाती है?

• कानूनों का पालन: भारत में डिजिटल पर्सनल डेटा प्रोटेक्शन बिल और अमेरिका में HIPAA जैसे नियम।
• सूचना सुरक्षा प्रणाली: ISO 27001 जैसे मानकों के अनुसार।
• नियमित ऑडिट और जाँच।
• डेटा एन्क्रिप्शन और सीमित एक्सेस।
• सुरक्षित क्लाउड सेवा प्रदाताओं के साथ साझेदारी।
• मरीजों को अपने डेटा पर नियंत्रण: वे चाहें तो जानकारी एक्सेस या डिलीट कर सकते हैं।

AI एकीकरण डिजिटल थेरेप्यूटिक्स की प्रभावशीलता कैसे बढ़ा रहा है?

• प्रीडिक्टिव मॉडल्स: संभावित स्वास्थ्य समस्याओं का पहले से अनुमान।
• नेचुरल लैंग्वेज प्रोसेसिंग: चैटबॉट्स और वर्चुअल असिस्टेंट्स मरीजों के सवालों का जवाब देते हैं।
• क्लिनिकल डिसीजन सपोर्ट: डॉक्टरों को मरीज की जानकारी का सारांश और व्यक्तिगत इलाज योजनाएँ सुझाना।
• जोखिम मूल्यांकन (Risk Assessment): री-अडमिशन या जटिलताओं की संभावना का अनुमान।
• कंप्यूटर विज़न: स्मार्टफोन इमेज से बीमारियों का आकलन।

आने वाले 5–10 वर्षों में कौन-सी नई तकनीकें डिजिटल थेरेप्यूटिक्स पर सबसे बड़ा प्रभाव डालेंगी?

• वेयरेबल्स (Wearables): स्मार्टवॉच और अन्य उपकरणों का व्यापक उपयोग।
• गैर-आक्रामक मॉनिटरिंग: बिना सुई या कफ के ब्लड प्रेशर और ग्लूकोज की निगरानी।
• AI-आधारित बिहेवियरल मॉडल्स: बेहतर अनुपालन सुनिश्चित करने हेतु।
• कंप्यूटर विज़न: व्यायाम की सही तकनीक पर वास्तविक समय फीडबैक।
• ऑगमेंटेड रियलिटी (AR): मरीज शिक्षा और सर्जिकल प्लानिंग में उपयोग।

भारत और वैश्विक स्तर पर डिजिटल थेरेप्यूटिक्स बाजार का भविष्य कैसा है?

• भारत में: AI-आधारित हेल्थकेयर बाजार तेजी से विस्तार कर रहा है।
• वैश्विक स्तर पर: वेयरेबल्स का बाजार उल्लेखनीय रूप से बढ़ने की संभावना है।
• वर्तमान में स्वास्थ्य सुविधाओं में डिजिटल थेरेप्यूटिक्स का उपयोग अभी भी कम है, जिससे वृद्धि की अपार संभावना है।

हृदय रोगों की देखभाल में डिजिटल थेरेप्यूटिक्स का दृष्टिकोण क्या है?

विशेषज्ञ टीम (केयर मैनेजर, न्यूट्रिशनिस्ट, व्यायाम विशेषज्ञ)।

• मरीज, डॉक्टर और देखभालकर्ताओं के लिए ऐप इकोसिस्टम।
• मान्यता प्राप्त चिकित्सा उपकरणों का एकीकरण।
• पोस्ट-प्रोसीजर से लेकर क्रॉनिक कार्डियक कंडीशन्स तक की देखभाल।
• एविडेंस-बेस्ड केयर: नवीनतम क्लिनिकल गाइडलाइन्स पर आधारित।

डिजिटल थेरेप्यूटिक्स स्वास्थ्य सेवाओं को अधिक सुलभ और किफायती कैसे बनाते हैं?

• दूरस्थ परामर्श और मॉनिटरिंग, अस्पताल जाने की आवश्यकता कम।
• विशेषज्ञों तक पहुँच, खासकर डॉक्टरों की कमी वाले क्षेत्रों में।
• यात्रा और आवास जैसे छिपे हुए खर्चों में कमी।
• नियमित देखभाल के बीच निरंतर समर्थन, जिससे जटिलताओं से बचाव।
• AI और तकनीक के ज़रिए बड़े पैमाने पर व्यक्तिगत देखभाल।
• मरीजों को आत्म-प्रबंधन के लिए ज्ञान और उपकरण उपलब्ध।

The post स्वास्थ्य सेवा में बदलाव: भारत और वैश्विक स्तर पर डिजिटल थेरेप्यूटिक्स की भूमिका appeared first on InnoHEALTH magazine.

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.”

The post What it takes to do real world AI: lessons from deployment appeared first on InnoHEALTH magazine.

Artificial Intelligence (AI) is a hot topic, simply put – it’s a way of making a computer think intelligently, in a way human think and over a decade now it has managed to be fairly successful. It has found application in several domains, from consumer electronics like smartphones and smart home devices like Amazon’s Alexa to very niche applications in academic research. What began as a nascent academic pursuit to enable computers to think and solve problems using human-like cognitive capabilities has now invaded most aspects of human life, medicine and healthcare is no exception.

Modern medicine has discovered around 60,000 ways things can go wrong with the human body and over thousands of years have probed these illnesses and disorders to better understand and treat them, one drug, one technique at a time. In recent years, however, there has been a dramatic shift in the pace of innovation in healthcare, especially with the advent of artificial intelligence. Artificial Intelligence is an umbrella term used to cover a wide array of algorithms which mimic human cognitive functions and are self-correcting, and can ‘learn’ from a dataset.

The issues mentioned above are all pertaining to the AI system and its functioning, but there are vital concerns about AI’s effect on people involved in care. Several studies have shown that patients prefer AI chatbots and virtual nurses over humans when learning about their diagnosis as they can proceed to learn at their own pace without the embarrassment of not keeping up with the doctor’s speed. Patients are also more open to conversation with a computer than a human being, part of the reason being the diminished shame and fear associated with being vulnerable. But Allison Pugh, a Professor of Sociology at the University of Virginia and a writer for the New Yorker, thinks that virtual nurses and AI bots offer nothing more than the thinnest veil of care. She writes, “[…] automating or using AI to deliver care would be the same as relying on a “cloth monkey”—a reference to a cruel experiment, carried out in 1959, in which infant monkeys were given a choice between two surrogate mothers, one made from welded wire, the other from terry cloth. (The infants preferred the cloth mother, even when only the wire mother gave them milk.) AI-driven care was a sorry version of the real thing.”

As demonstrated by several research groups, deep learning algorithms have achieved human-level accuracy and then some more. It can look for patterns which are invisible to the human eye. Thus, sooner or later, displacing and relegating doctors from their positions, at least in certain areas of healthcare. This can lead to massive burnouts in doctors as their roles shift drastically and may even lead to a gradual attrition of their skills. But there’s more to care than just interpreting blood reports and imaging data of a patient, it has much more to do about understanding the needs of patients, their mental state, etc. The secret of healthcare is not in reading out objective reports, but in the assurance and the warmth, a doctor’s cadence can provide. “Caring is expressed in listening, in the time-honored ritual of the skilled bedside exam – reading the body – in touching and looking at where it hurts and ultimately in localizing the disease for patients not on a screen, not on an image, not on a biopsy report, but on their bodies.”, writes Abraham Verghese, an author and a physician at Stanford.

Employing AI to most healthcare activities might also have a negative effect on how knowledge is generated. Most medical knowledge generated in the past has been curiosity driven. AI systems can tell us whether the lesion is a benign mole or a tumor, but it can’t provide answers to why the tumor has a corrugated surface or white patches etc.

Artificial intelligence is going to be pervasive across the spectrum of healthcare. From routine lab tests to offering a clinical decision, AI algorithms will play a major role in the future of healthcare. As deep learning algorithms get stronger and as the workings of the black box are revealed, AI technology will make further strides in healthcare. But advancements in AI-based healthcare doesn’t mean the downfall of human doctors. Healthcare is a highly emotional and human-centric field and the “human touch” will always play a pivotal role in the delivery of healthcare. Humans, even highly skilled doctors are fallible beings with inherent limitations and artificial intelligence will not sideline these practitioners but augment their abilities, in making an objectively better yet humane decision.

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