Among all the other deadliest diseases, cancer is the most lethal cause of death, which may occur in various types and regions inside the human body. It arises due to uncontrolled cellular functionality that happens when cells start working independently and performing odd behaviors compared to their surrounding structures. When it occurs in the lung region, it creates a severe condition, as lung cancer’s survival rate is lowest compared to other types of cancer. According to the report published in 2021 by global cancer statistics, the world’s new cancer cases have reached 19.3 million, and lung cancer is the second largest cause of cancer after female breast cancer. In various research studies, it has been found that early cancer detection may increase the chances of long-term survival.

Furthermore, studies have indicated that a low dose CT scan and its automated analysis can be the best way of early cancer detection. It works without putting the human body in harm compared to other invasive procedures. Analyzing CT images to get insights into internal structure and abnormality has been vastly carried out by experts and radiologists in previous days. But due to the consistent efforts of researchers, artificial intelligence (AI) based disease detection systems have been gaining the direct attention of clinical practitioners and medical institutes for the past two decades. With increasing structured data in the medical field, more opportunities for finding precise and straightforward methods of diagnosis are opening. The modern world where we live now is just embarking on the long and exciting data science journey that can lead us towards unimaginable peaks of automated technological solutions. It is yet unclear how precise the medical technology would be in detecting and diagnosing diseases at their early stage. Perhaps, the advanced algorithm defines the upcoming health issues before their occurrence in the human body. It allows someone to take corrective feedback in an ongoing lifestyle to prevent the forthcoming critical situation. Everything we imagined today may be possible in the near future but that requires consistent efforts to develop efficient techniques and algorithms.

With the collaborative or individual efforts of various scientific and research groups, algorithms are rapidly getting advanced by adopting the most trustworthy and less time-consuming methodologies. Deep learning and fine-tuned neural networks play an essential role in that context as they open new ways to modify existing techniques to obtain promising results. Inside the deep neural network, it’s hard to know how they behave and interact, but by employing systematic hyperparameters and fine-tuning, outcomes and performance can be improved. Some key hyperparameters are learning rate, optimization algorithm, activation function, number of hidden layers, number of activation units, kernel size, pooling size, batch size, and number of epochs.

Apart from the hyperparameter, the most crucial point is to decide a number of layers for the optimal and best performance of the network. Creating balance with all these parameters is complex and requires multiple testing and experiments. It takes a lot of effort and time to maintain a balance between two different quantities for the increased performance of a deep learning network. This problem can be addressed by employing an additional algorithm responsible for performing the required analysis to choose the best hyperparameter and values to establish a fully automated and self-improving deep learning network. Maybe the better strategy is to implement the most adaptive and high-performance AI algorithm, which helps detect and diagnose diseases on the individual and societal scale.

With this article, I would like to propose a “partial naturally randomized deep learning layers (PNRDL)” for the advanced performance of an automated detection system. The procedure is not tested yet, but the inclusion of optimization with a little bit of relaxing parameter in the form of partial naturally randomized weights may provide better human-level performance for the analysis of CT images to detect lung cancer or its early signs. In general, relaxing situations are when someone takes a break from the process to achieve the desired goal and spends some time doing other activities. Many philosophical studies discuss this period as the best period for finding new innovative, astonishing ideas that revolutionize the actual path of moving towards the desired goal. Sometimes it gives better outcomes than expected results that someone never imagined. I considered this not a coincidence but a part of natural computing through which anyone and everyone are connected. Billions of neurons continuously acquire weights inside the human brain by taking insights or parametric values. These values or parameters are generated by nature that add up with the brain as essential insights for the computation of various tasks, which sometimes reveal exceptional outcomes.

I believe it is a substantial phenomenon that is seemingly random but not actually random as everything inside nature runs by natural computing. To introduce a relaxing period inside the machine, I propose a relaxed parameter-based, “partial naturally randomized deep learning layers” that takes random values for deep learning weight updation. Random values used in these experiments will be directly obtained from the naturally randomized numbers that are randomized and obtained during individual image analysis. After such procedures, it may be possible to get a deeper connection to the automated detection system through natural computing, where the randomization of parameters works as a relaxing situation in the case of the human being. By going through this procedure, I believe the development of automated disease detection systems has become more realistic and precise. Maybe the idea of making this type of system works fine, and the procedure discussed for disease detection render outstanding results if employed in other research and development purposes. Figure 1 below is showing a short description of the development of the proposed algorithm.

The experimental scope of this research focuses on taking a machine into the time-space where some of its weight functions process the very subtle event of the natural parameter to extract essential values.

However, the proposed model is still under exploration as the procedure to obtain an utterly randomized number is still in the experimental stage. The first few tries to generate random values are conducted by taking never-repeating decimal digits of “pi”. Although, they are also finite at any given period. Further trials to generate a natural randomized number to introduce relaxation sessions inside machine-learning operations are ongoing. The study aims to take insights from nature, always trying to say something to us regarding any event and situation. An American biologist, Barry Commoner once said that “everything is connected to everything else”.

It means the present case, past conditions, and future outcomes are not distinct. It is just our inability not to process the subtle insights of the surroundings and ignore them by considering them as random non-valuable information. Processing every element of information is not possible, but taking a small piece and extracting valuable insights is the way to achieve desired outcomes. The experimental scope of this research focuses on taking a machine into the time-space where some of its weight functions process the very subtle event of the natural parameter to extract essential values. The outcome of this process will generate a piece of information that helps to implement all-inclusive nature-driven algorithmic results. Unlike the existing computation system, it performs its task by combining insights of input data and instincts of current surroundings. It is an entirely radical approach to using AI techniques that can be time-consuming but taking a chance to develop and work upon this methodology may undoubtedly be helpful. After its successful implementation, it may be possible to implement a deeper bond with nature and harness the power of robust natural computing or actual computing. 

Deep learning in AI also suggests that deeper and closely connected nodes are the one who dominates the outcomes. Similarly, the more dominant one around us (nature) should be deeply and strongly connected to every possible node (eg. deep learning algorithms and layers) to obtain the best possible results today and in the near future.

Composed by: “Mr. Resham Raj Shivwanshi is pursuing PhD at the Department of Biomedical Engineering, NIT Raipur. He is currently working upon medical imaging, CT scan analysis,Machine learning and AI methodologies.”

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SOURCE: www.inshorts.com

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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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Business Models of Successful IOMT in India and Challenges

The IoT healthcare business strategy is not yet robust because it involves a set of elements with new requirements such as new operational processes and policies, new infrastructure systems, distributed target customers and transformed organizational structures. Therefore, there is a need for a new business model. The IoT technology opens a new dimension of business opportunities for healthcare companies and the IoT platform becomes a key artifact in this transformation. Early movers who are taking a proactive approach to establishing their IoT healthcare ecosystem will tap into new business models. In this part of the business model, the panel discussed the basis of pricing and the way of charging for a product or service. Besides this, the panel also discussed the ways of revenue generation.

While there are many benefits of the Internet of things in healthcare, it isn’t without its challenges. As with any new technology in healthcare, hospital and IT executives are concerned about data security and IoT device management. What are the potential barriers to IoT adoption and how healthcare IT can overcome those obstacles?

AI and IoT change the way healthcare is delivered and we obviously are on the cusp of a revolution. This definitely poses a fair amount of concerns but also huge possibilities. The panel also discussed how Medicare is 100th the cost per person when compared to Obama care. So clearly, there must be more innovations coming from IoT and AI; they are going to help us reduce the cost.

In the past, how people tried to use the data for the benefit of both patients and providers, starting from the patient side faced huge failures, as nobody was willing to pay. And that’s where the thinking came from that; how do we really make this work? How do we really turn this data into a business model? And that’s where they pivoted the model, working with providers and using their data sets. But then the question comes up, “Why the providers would share their data?”. Thus, the first pitching that started was to use the provider’s data for their patient care. And it didn’t work well. The whole narrative was, “What’s in it for providers?”.Therefore, the business model turned out to be, as we kept on talking to people, we realized that the data is going to be useful for provider’s revenue success, and then the same datasets will help the patients. A solution that they projected is to help retain the provider with patients’ data, grow their revenues and help in patient care.

The MedTech certainly seems more complex. Where they are trying to come out with a dermatology solution and oral health solution for serious diseases where a cell phone image taken from an off the shelf mobile phone camera is sent to an AI backend and it gives a diagnosis and possible treatment options for a variety of skin diseases, particularly oral cancer. Such cases are very prevalent. What the panelist found is a landscape extremely complex in terms of the business model because when someone comes out with something like this, then figuring out who would it be paying is difficult. And suddenly the AI and IoT thing comes up and say that it can perform this diagnosis at par the level and in certain cases better than certain trained specialists. This does not support their case.

One of the panelists shared her thoughts on important factors to concentrate and realize why health care has not moved digitally as compared to other industries like banking, insurance and how a lot of digital interventions can happen in healthcare. There are a whole lot of opportunities.

The main thing to focus on is What is the problem? What is the solution? And who’s going to pay you? Because that’s where the panelists believe that lots of startups have a scope.

The customer is one, the doctor is another and hospital, the other spectrum. So how to manage this change and address the change management. If you give a thought on what to overcome, it gives you a kind of value that you try and achieve success and how do we overcome the quality assurance issues, change issues, challenges that one sees in terms of adoption. Let’s say for the doctors, it took a hundred years to the doctor to adopt stethoscope. Today, it’s widely accepted. IoMT is something that is obviously very innovative, very useful. But then not every doctor will accept it right away. How does one plan to overcome that barrier? So, they might just want one to focus on change management issues.

There are three components of the IT embedment in a hospital. One is the business side of it, which gets adopted immediately. There’s just no resistance to it. The second side is the paramedical side of it; it has pathology, radiology, and other such things. They take a little time but they come on board. The learning curve is much shorter over there. The third one is the clinical side, which is the most difficult, as in a hospital system there are doctors who are most resistant to changes and the reason being their upbringing and learning of clinical practices in a certain way.

The question arises “How does one change their practices?” Changed management is already happening at the college level. The medical students who are coming out now are more hands-on and are accepting the new technological changes. We will have to wait a little while, but they will accept it. When we talk about a change in the management, the problem that arises is not specific to India only. In the USA, George Bush had said that he wanted EMR to be adopted by everyone within the next ten years. Surprisingly, he didn’t even get 15% conversion in those ten years.

It is important to secure the data and look over the cost implications of data security issues in connected health work moving forward. There is a need for appropriate interventions at the transition level of doctors and patients, as well as the importance of advocacy and awareness‐raising at community level about connected health. There is also a need for financial support for encouraging the tech enthusiasts of the country to address health issues in India with connected health solutions. One needs to understand customer value as well. How do we speak the right language in terms of what would appeal to that particular user or customer or doctor or hospital for that matter only then we will see more IoMT adoption happening. Yes, there is a need to understand the regulatory framework. Startups needs to adopt a lean structure in collaboration with the medical partners and come up with innovative solutions. India has a fantastic opportunity, but clearly it does need elements to come together and work together.

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“We are moving into a bold era, defined by advanced medical technologies and artificial intelligence. But healthcare will always remain a human business”. For Dominic Asquith, British High Commissioner to India that is the crux of the current situation, but a razor-sharp competitive business in medical devices across the world has a different narrative because of market compulsions vis-a-vis human business.

India’s strong position on heart stents’ price regime triggered debate and the price war snowballed into tough positions and international players gaped with bewilderment. Many institutions and professional bodies flew into action and debated price structures while the country’s federal government unveiled its world’s largest health coverage scheme for millions, virtually creating a colossal potential in the health sector. If the government extends healthcare services to its 1.25 billion population as part of India’s Universal Health Coverage (UHC) agenda, it will be a challenge to devise ways to reduce catastrophic Out Of Pocket (OOP) expenditure on healthcare and ensure affordable access to essential healthcare for the entire population with the limited resource envelope.

Amid ongoing trading tussle between US and China, many apprehend that ripple effect will also percolate down in India as the latter is going ahead with its price regimes and its moves to bring down stent and knee implant devices had raised many eyebrows.

A section of media has reported that AdvaMed (Advanced Medical Technology Association)in its briefing memo for US Commerce Secretary and US Trade Representative last year had conveyed that “Made in India” scheme has been used by parts of the Indian government to justify protectionist measures such as import tariff hike sand preferential market access policies.

The Economist newspaper recently carried a write-up which quoted China Digital times, a California based website, that Chinese media had been advised to play down China’s global dominance in various technological sectors, known as “Made in China 2025”.Such developments indicate that India should also dial down the hype on “Made in India”.

The 2014 World Bank Report estimated the OOP spending on healthcare in India to be as high as 89%. India is the only country, out of the major world economies, where out-of-pocket expenses are increasing, despite a concurrent increase in public spending. This effectively means that more people are availing private healthcare services due to rising incomes or are forced to spend due to inadequate public expenditure.

Official sources say big schemes can be ensured with the help of Health Technology Assessment (HTA), which is a widely used methodology internationally for optimization of resource allocation in health. HTA is a method of evidence synthesis that considers aspects pertaining to clinical effectiveness, cost-effectiveness, social, ethical and legal implications of the use of “health technology” for healthcare intervention.

The Indian healthcare industry was valued at over USD 100 billion in 2016 and is expected to reach more than USD175 billion by 2020, resulting in a CAGR (Compound Annual Growth Rate) of 20%. The medical device industry is valued at USD 6 billion. The medical device industry was accorded the status of an independent industry in 2014. The medical device sector today is clearly small and indicates low penetration in the country.

“The path to realizing healthcare goals is complex, and various fundamental issues and challenges need to be addressed and solved holistically. Long-term plans need periodic policy and regulatory interventions to ensure fair conduct within the industry while providing the support needed for profitable and sustainable growth. These activities will enable the medical device industry to accelerate rapidly and play a key role in making India healthier and stronger.” Mr. Guljit Singh, Executive Chairman of SKP Business and Abby Pratt, Vice President for Global Strategy and Analysis at AdvaMed said in their forward of the study. AdvaMed had partnered last year with SKP to publish the study entitled – Medical Device Industry in India –the evolving landscape, opportunities, and challenges.

The study also examined various policies and regulations impacting the industry and attempts to make recommendations on the way forward from the perspective of different stakeholders.

The study made a slew of recommendations like cap trade margins and not price to the dealers, should be capped after a detailed evaluation of each medical device segment and the role of trade. Trade margins should be fixed differentially for different categories of devices, based on service requirements and role of distributors.

It says allow the dealers to compete with each other and provide a fair price to the hospitals based on the terms and conditions of services and payments. There is a need to work towards bundled payment models as used in many other countries to better align incentives for hospitals and their business models. Increase government healthcare spending as a percentage of GDP; India lags behind the other BRICS in this regard.

Adequate utilization of government infrastructure to reduce the cost to private players – PPP model could be an attractive alternative to explore. Specify certain quantities/ proportions of supply of different stents at lower prices to specified government agencies for use with underprivileged sections. It advocated to empower the Medical Technology Assessment Board (MTAB) to evaluate the model of tiered pricing as observed in the French healthcare ecosystem, wherein the NPPA caps the generic products and leaves the latest generation products with incremental value out of the ‘essentiality purview’.

The incremental value could be on account of efficacy, material used, ease of delivery and shortened recovery time. Various combinations are possible here such as: an increase in the number of tiers, allowing new introductions to be free of price intervention for a certain number of years. Identify priority medical devices and procedures that demonstrate the greatest need stemming from disease burden. MTAB, alongwith other regulators for medical devices, should ensure that there are minimum quality parameters – in terms of safety, clinical efficacy, and cost-effectiveness – for medical devices that get used in the public and private health system, such that long-term costs are lowered over a patient’s lifespan, with need for fewer hospital readmissions, lowered need for medication, and overall better health outcomes.

The report said factors such as changing demographics, rising life expectancy, growing incomes and public awareness have contributed to a higher demand for medical care. A more focused approach from the government, with increased public expenditure on health, greater utilization of technology, vibrant private sector participation, and continued innovation can transform the sector and move India closer to its goal of providing quality universal healthcare.

Some of the key issues faced by the Indian healthcare industry are evident: with India’s disease burden shifting from acute to chronic diseases, large number of the population continue to not have access to basic healthcare services. Public health infrastructures are poorly equipped to deal with this shift towards NCDs.

While the government and value chain participants are undertaking several steps to address these issues, they have been executed in silos. Medical service providers are not only inadequate but are also not evenly distributed across rural and urban areas. This shortfall occurs despite an increase in the number of medical colleges from 23 in 1947 to 398 in 2014. The quality and availability of healthcare deteriorate as one moves away from large urban centers to lower-tier towns and rural areas. The Indian medical device industry, the report found, is highly fragmented. Currently, these sectors are dominated by MNCs with 70-75% of the demand being met through imports.

Approximately 30% of the domestically manufactured devices are exported, in which the consumables and disposables segment has the largest share.

On a mix of technologies such as engineering, electronics, material sciences and information technology – innovation, capital and technology drive the industry. However, India has not been able to bridge the gap between investments, skilled resources, and innovation to fully capitalize on these opportunities. Numerous factors underlie the prevalence of higher imports in the country.

Some of these are: no clear comparative cost advantage in view of other emerging markets; policy issues like inverted duty structured do not help in creating a positive environment; lack of favorable policy and regulatory framework, the report, compiled last year said.

On segments, the study said, hearing aids and pacemakers form major part of patient aid segment and constitute 70% of the segment collectively. Most of the products are sourced from Australia, China, Ireland, Singapore, South Korea and US.

The medical disposables and consumables sector consists of products such as plastic syringes, blood bags and many others. This segment is dominated by domestic players in India due to its low technology requirements. Needles and syringes constitute majority of the sales. However, wound management products and medical apparels are the fastest growing products in this segment. The Indian implants segment has witnessed an encouraging growth rate of CAGR 25%.

With a healthy mix of both domestic companies and MNCs, this segment has witnessed intense competition between players due to strong pricing pressure. The domestic players have realized the market potential which demands customization and differentiated product quality. The equipment and instruments section is the largest segment of the medical device industry constituting nearly 54% of the segment and is dependent on imports.

MRI machines, CT scanners, ultrasound machines, dental drills, dental chairs, dental x-ray machines are some of the key products of this segment. It is dominated by MNCs like GE Healthcare, Philips Healthcare, Schiller Healthcare, Danaher Corporation, and Roche.

Drug-eluting stents and bare metal stents form a major part of the stents segment and constitute more than 70% of the segment collectively. Most products are sourced from US and Europe. While domestic companies are manufacturing cost-competitive products, they still face competition from international players on account of quality. Diagnostics segment is growing due to advanced technology being increasingly applied in medical procedures.

The report had said government initiatives control regulation and the new Public Procurement Policy- the Preferential Market Access – do not fully reflect this and has alarmed many industry participants.

A clear long-term vision and roadmap for the industry and predictability of policy would excite the industry. The government needs to provide policy support for both the supply and demand side of the medical device industry to successfully accelerate growth. Industry and government need to work together to improve awareness, access has made rapid progress in the last decade, but significantly lags behind other nations in availability and quality of equitable medical care and services for citizens.

This, in turn, presents an enormous opportunity given the large population, growing economic prosperity, and the disease burden. Major issues such as availability of adequate infrastructure, trained human resources, geographic spread, rapidly changing disease burden, and high/often catastrophic out of pocket expenditures are challenges that the government is keen to address, the report said.

The Indian government is committed to raise public expenditure on health to 2.5% of the GDP. Public and private sectors need to play equally important but different roles in bringing rapid change to the healthcare scenario in the coming decade.

Around opening up, FDI and infrastructure development are welcome initiatives that will enhance the ecosystem for investment. The Indian medical device industry appreciates government’s efforts to remove bureaucratic hurdles and improve the ease of doing business, but believes that additional steps can be taken to strengthen its approach.

The study identified challenges and said growth in the healthcare industry has been attractive. However, much more needs to be done. Despite the advent of private players and better government spending, most Indians can only afford and/or have access to basic healthcare. Hence, the path to universal healthcare coverage is challenging.

Some of the obstacles faced are – real estate prices and high capital costs limit the growth of delivery infrastructure; insufficient attention by policymakers and a complex tax regime are also responsible for the sector’s underdevelopment; the lack of a comprehensive policy and focus to develop the healthcare ecosystem; low penetration: the per capital medical device spending of USD 3 compared to USD 7 in China and USD 42 in Russia is significantly low.

Another study early this year – “Medical devices in India – an agenda to effective healthcare delivery” said to attract world’s top med-tech players to establish their research and development (R&D) operations in the country and to establish a firm footing in the global market, the Government needs to adopt a robust policy and regulatory framework. India can replicate some of the models being adopted by other nations that have succeeded in attracting leading medical players. The contents of this report are based on a study commissioned by AdvaMed and conducted by IQVIA on behalf of AdvaMed.

It said that the government can provide financial incentives in form of extended tax holidays or weighted tax reduction for R&D investments; boost local demand for medical devices by stepping up public healthcare spending which in-turn could catalyze investments in this sector. Further, the government should focus on creating an enabling regulatory landscape by creating a separate legislation system for medical devices.

The report said while, Medical Devices Rules 2017 is a welcome step towards regulating the medical devices sector, the setting up separate notified body and distinct legislation system for devices with a focus on the following could further boost growth of the industry like Grant product approvals; Instituting quality standards; Setting up monitoring mechanisms for devices; Expedite patent approval process for medical devices.

Medical devices form more than USD 200-billion global industry, which develops and manufactures essential healthcare equipment. The Indian industry is currently valued at USD 4.4 billion with about 700 medical device makers, India’s medical device market is currently the fourth-largest in Asia (after Japan, China and South Korea) and ranks among the world’s top 20.

Imports constitute a substantial part of the medical device market in India. An estimated 80% of India’s demand for medical devices is currently met by imports, nearly 30% of which are supplied by the US. Imported medical devices are often those that are critical, innovative and high-risk in nature – either life-saving or life-enabling, and therefore undergo rigorous testing. These complex, innovative devices are designed to address the growing expectations of India’s population in the country’s rapidly evolving healthcare system, this report said.

The medical device industry has been very encouraged by the Prime Minister’s recognition of the critical role of medical devices in addressing India’s healthcare challenges. The Adva Medwebsite says in general, we have a received a strong message from the government that they are interested in promoting the medical devices sector and creating an environment that fosters innovation. This is great news not only for manufacturers of medical devices but more importantly for patients who desperately need access to high quality yet affordable lifesaving and life-enhancing medical technologies.

The PM’s call to medical device manufacturers is a welcome recognition of this separate and important part of the healthcare system. Several of AdvaMed’s member companies have already established manufacturing units in India, and India is being increasingly viewed as an R&D base.

The website says, the medical device industry in India has grappled with challenges for several years around recognition and regulation. While the Global Medical Device Nomenclature (GMDN) lists more than 14,000 different product types, the current regime only regulates a relatively modest portion of these products.

Moreover, these devices/products are regulated as “drugs” under the Drugs and Cosmetics Act of 1940. This is problematic because medical devices are very different from drugs in terms of diversity, product development, patent structures, types of failures, scientific disciplines involved in assessing performance/efficacy.

In addition to the arbitrary application of the rules for drugs to medical devices, which hinders the development, quality of and access to medical devices, there is also a lack of predictability in the regulatory system. The industry is also concerned about the lack of standardization in line with global best practices.

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