The Current State of AI in Healthcare

Lavanya Nandan opened the discussion by highlighting the urgent need to incorporate AI into healthcare systems. AI, when integrated with health technologies, can predict outbreaks, improve patient outcomes, and even administer patient care. She mentioned the significance of creating AI-based systems that can engage with patients, providing them with immediate responses.

The Potential of AI during Pandemics

One of the pivotal moments was when Lavanya emphasized the time-saving potential of AI during global health crises like pandemics. She poignantly noted that if AI had been more advanced during the initial stages of the recent pandemic, many healthcare professionals might have been saved from exposure. AI could have enabled remote assessment and understanding of patient conditions, leading to more effective treatment without direct exposure to infection.

AI’s Role in Streamlining Administrative Tasks

Sachin Gaur discussed the administrative burdens that healthcare professionals often grapple with. Emphasizing the importance of time in the healthcare sector, he suggested that AI could handle administrative tasks. This would allow healthcare workers to focus more on patient care and showing empathy – a crucial aspect that machines cannot replicate.

Gaur also mentioned Eric Topol’s book, “Deep Medicine”, underscoring that a significant part of healthcare revolves around empathy. He argued that by freeing up medical professionals from mundane tasks, they can focus more on building patient relationships.

AI-Powered Robotics in Healthcare

The discussion took a futuristic turn when Sachin touched upon AI-powered robotics in healthcare settings. He acknowledged the presence of such robots in certain overseas medical settings and restaurants. However, he brought attention to the socio-economic implications of these robots. In countries like India, where employment opportunities are crucial, the large-scale replacement of jobs with robots may not be feasible.

Yet, the role of robotics, especially in infection control, was recognized. Robotics can mitigate human contact, which is crucial in managing the spread of infectious diseases.

Government Initiatives and Challenges

Dr. V. K. Singh brought a unique perspective, referencing governmental viewpoints on integrating AI into medical curricula. He acknowledged the supremacy of human intelligence but also cautioned against excessive dependence on AI. By sharing a real-life anecdote, Dr. Singh highlighted the potential challenges AI can bring to the academic domain, where AI-generated content might flood academic journals.

However, he also shared a personal experience where he used a tool similar to “Chat GPT” to aid in writing, signifying the immense potential of AI in simplifying complex tasks. Dr. Singh encouraged more people to learn about AI, suggesting the possibility of institutional-level AI training programs in the future.

Concluding Remarks and Future Prospects

Lavanya wrapped up the webinar with gratitude, noting the active participation, which was higher than in previous seminars. The topic’s relevance was evident as a large number of participants stayed engaged until the end. She expressed hope for another in-depth session in the future, particularly when AI in healthcare sees more advancements in India.

Lavanya’s closing remarks captured the essence of the seminar: while AI has vast potential in revolutionizing healthcare, the human touch remains irreplaceable. The challenge lies in harmoniously blending the two to create a more efficient, empathetic, and effective healthcare system.

Summary

The webinar provided a comprehensive overview of the myriad ways AI can and has transformed the healthcare sector. From predicting outbreaks to assisting in administrative tasks, the capabilities of AI are vast. Yet, as with any technological advancement, it comes with its own set of challenges. The key lies in harnessing AI’s potential without compromising the human touch that remains at the core of healthcare.

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The field of artificial intelligence is booming, thanks in part, to the hype surrounding new tools like ChatGPT. The chatbot, owned by Microsoft-backed company OpenAI, has captured the public imagination with its ability to converse, write code, and compose poetry and essays in a surprisingly human way. It’s also spurred a race among tech giants to release similar, more sophisticated products.

Investment in artificial intelligence is growing rapidly. The global AI market is currently valued at $142.3 billion (€129.6 billion), and is expected to grow to nearly $2 trillion by 2030. AI systems are already a big part of our lives, helping governments, industries and regular people be more efficient and make data-driven decisions. But there are some significant downsides to this technology.

AI has a big carbon footprint

In order to carry out the tasks they’re supposed to, AI models need to process — or be “trained” on — mountains of data. To learn to recognize an image of a car, for example, an algorithm will need to churn through millions of pictures of cars. Or in the case of ChatGPT, it’s fed colossal text databases from the internet to learn to handle human language. This data crunching happens in data centers. It requires a lot of computing power and is energy-intensive.

“The entire data center infrastructure and data submission networks account for 2-4% of global CO2 emissions,” says Anne Mollen, researcher at the Berlin-based NGO Algorithmwatch. “This is not only AI, but AI is a large part of that.” That’s on a par with aviation industry emissions.

“The first time I read this data, I was really shocked,” said Benedetta Brevini, associate professor of political economy of communication at the University of Sydney, Australia, and author of the book, “Is AI good for the planet?”

“If you jump on a plane from London to New York, your carbon emissions will be 986 kilos. But to train one algorithm, we emit 284,000 kilos,” she said. “Why are we not having a conversation about how to reduce this carbon footprint?”

It’s important to note that the Massachusetts study’s estimate was for an especially energy-intensive AI model. Smaller models can run on a laptop and use less energy. But those that use deep learning, such as algorithms that curate social media content, or ChatGPT, need a significant amount of computing power.

Beyond the “training” phase, more emissions are created when the model is applied in the real world, something that can happen billions of times a day, such as every time an online translator translates a word, or a chatbot answers a question. Mollen from Algorithm watch says this application phase can potentially account for up to 90% of the emissions in the life cycle of an AI.

So what can be done to tackle AI’s footprint?

Brevini says environmental concerns need to be taken into account right from the start — in the algorithm design and training phases. “We need to consider the entire production chain and all the environmental problems that are connected to this chain… most notably energy consumption and emissions, but also material toxicity and electronic waste,” says Brevini.

Rather than building bigger and bigger AI models, as is the current trend, Mollen suggests companies could scale them down, use smaller data sets, and ensure the AI is trained on the most efficient hardware available.

Using data centers in regions that rely on renewable energy and don’t require huge amounts of water for cooling could also make a difference. Huge facilities in parts of the US or Australia, where fossil fuels make up a significant chunk of the energy mix, will produce more emissions than in Iceland, where geothermal power is a main source of energy and lower temperatures make cooling servers easier.

Mollen notes that tech giants have a fairly good record when it comes to using renewable energy to power their operations. Google says its carbon footprint is zero, thanks to investment in offsets. It aims to be operating exclusively on carbon-free energy by 2030. Microsoft has pledged to be carbon negative by 2030, using carbon capture and storage technologies, and Meta plans to reach net-zero across its value chain by 2030.

But energy isn’t the only consideration. The huge amount of water data centers need to prevent their facilities from overheating has raised concerns in some water-stressed regions, such as Santiago, Chile. Google’s data center there is “aggravating a drought in the area and local communities are actually revolting against the data center and against the construction of new data centers,” says Mollen.

Emissions aside, how is AI being used?

But even if big tech companies shrink AI’s energy use, there’s another issue that is potentially more damaging to the environment, according to David Rolnick, assistant professor in the school of computer science at McGill University, Canada, and cofounder of the non-profit Climate Change AI.

He says there should be more focus on the way AI is being used to speed up activities that contribute to climate change. One example he points to is the use of algorithms for advertising. These are deliberately “designed to increase consumption, which assuredly comes with a very significant climate cost,” he says.

Rolnick also draws on a report by tech consultancy Accenture and the World Economic Forum, which predicts AI and advanced analytics will help the oil and gas industry make $425 billion in additional profit by 2025.

Greenpeace has heavily criticized AI contracts between fossil fuel companies and Amazon, Microsoft and Google. In a report, the environmental organization said Shell, BP and ExxonMobil were using AI tools to expand their oil and gas operations, reduce costs, and in some cases boost production. It said such contracts were “significantly undermining the climate commitments” made by the tech giants. Google has since said it will no longer build customized AI tools to help companies extract fossil fuels.

Playing catchup to the technology

The role of artificial intelligence is only likely to become more significant in the future. And keeping up with such rapidly advancing technology will be a challenge. That’s why Rolnick says regulation is crucial to ensuring AI development is sustainable and doesn’t make emissions targets harder to reach.

“It’s really a question of what we’re prioritizing and getting in early and shaping those choices that are being made,” he says. In the EU, lawmakers have for the past two years been working on the AI Act, expected to be a landmark piece of legislation, to govern AI and classify tools according to perceived risk. It’s unclear whether environmental concerns will feature in the bill.

Meanwhile, other governments are also working out how to deal with AI — to encourage innovation in the field and reap the benefits this new technology brings, while avoiding the potential dangers and protecting citizens.

Source: www.indianexpress.com 

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The Covid-19 pandemic was a major catalyst for the pharma industry to redefine its operations and embrace digital transformation at a faster pace. For example, the impact of artificial intelligence (AI) has been profoundly felt in the drug discovery space.

Over the past three to four years, there has been increased interest in the use of the technology, as witnessed by the emergence of an ever-growing number of start-ups, record levels of investment, and an increase in deals between pharma and AI vendors, including those using generative AI.

Drug discovery is known to be an ever-growing complex process that is incredibly expensive and time-consuming, and involves multiple processes including target identification and validation, hit identification, and lead optimisation. The time needed for a drug to reach the market ranges from 12 to 18 years, with an average cost of $2.6bn. Success rates are also low, with just 10% of candidates making it into clinical trials. Generative AI has shown great promise in accelerating and improving drug discovery processes. Most use cases of generative AI have been in the creation of new drug molecules (de novo drug design), but there is also the potential for the technology in target identification and drug repurposing. Companies across various industries have been quick to recognise the potential of generative AI and have been actively exploiting its applications to gain a competitive edge and improve their products in the drug discovery space. Examples of companies with generative AI platforms for drug discovery include Insilico Medicine, Exscientia, Iktos, Adaptyv Bio, and AbSci.

Insilico Medicine, a pioneer in the use of generative AI in drug discovery, has developed and validated a generative adversarial network-based AI platform and integrated this into its now commercially available Pharma.AI platform. This platform aims to accelerate the generation of novel molecules, disease target identification, and prediction of clinical trial outcomes. Insilico discovered and designed the world’s first AI-generated anti-fibrotic small molecule inhibitor (INS018_055) on lung function in patients with idiopathic pulmonary fibrosis, which has completed Phase 0 and Phase I trials. In June 2023, it entered Phase II clinical trials.

Another example includes Switzerland-based biotechnology startup Adaptyv Bio, which announced in March 2023 that it had created a platform that utilises generative AI for protein engineering. Its protein engineering platform utilises generative Algorithms, advanced robotics, microfluidics, and synthetic biology technique to generate and optimise protein sequences. In June 2023, Iktos and Curreio, a Japanese cryo-electron microscopy (EM) expert, entered a partnership that aims to leverage the power of AI and cryo-EM technology to expedite the discovery and design of novel preclinical drug candidates. Iktos’s generative AI technology, Makya, will benefit from the insights gained through cryo-EM analysis, enabling the design of molecular structures that maximise protein-ligand interactions. This is expected to improve the accuracy of predicting molecules that meet the project’s target product profile, accelerating the drug discovery process.

As the pharmaceutical industry shifts towards developing more complex and targeted therapies, the role of generative AI will become even more critical. The adoption of the technology in drug discovery is growing, but it is still limited in many settings. Barriers to the adoption of generative AI include requiring large and high-quality datasets for training, a requirement for specialised expertise in machine learning and data science, and challenging ethical and regulatory considerations.

Source: www.pharmaceutical-technology.com 

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Due to COVID-19, the demographic changed towards an older population, and the rise in medical difficulties, a growing number of remote patient monitoring systems are becoming popular.RPMS is also being used more frequently due to technical advancements

What is RFID technology?

The main objective of healthcare is to keep patients at ease, possible during their daily lives. In the past, patient monitoring systems in hospitals relied on wired sensors connected to computers. These systems’ drawback was that they limited the mobility of the patients. When medical establishments began to offer home-based treatment, RPMS were developed. As more researchers and businesses employ remote patient monitoring technologies to enhance treatment, the health sector is considerably improving, and the industry is expanding quickly.

Radio frequency identification technology, or RFID for short, is a catchall term for the use of radio frequency wireless communications for broadcasting, labelling, and automatically identifying people or objects.

Readers, middleware, and tags are the common components of an RFID system. A CPU or processor, memory, a power supply, and a radio antenna and transmission system that can send and receive data are the four main parts of an RFID tag. The middleware that links RFID to corporate information systems often collects gathered data and integrates it into enterprise systems in a way that is most beneficial for decision-making. RFID makes data processing possible for commercial operations.

What are the latest trends in RFID?

In the coming years, it is anticipated that the markets for healthcare, retail, food safety, and other industries will expand faster. This will drive developments in RFID technology. The potential of RFID is growing and developing as more companies and sectors adopt the technology. RFID technology is useful for tracking hospital assets, drugs, patients, and employees. This reduces operational costs, speeds up hospital processes, maximises the use of available resources, reduces medical errors, and improves patient safety. 

What RFID tracking in medicine entails?

Human traceability & access control

RFID is becoming a more widely used method to track and restrict access. For instance, embedding a distinctive RFID tag inside a wristband, card, or other item can grant a medical employee exclusive access to particular regions.

RFID technology has shown to be quite beneficial for a number of aspects of the COVID-19 outbreak.

Quality & Authenticity control

Since RFID tags provide your organisation a unique identity for every product, industries like pharmaceuticals and medical equipment are particularly concerned about this problem. Better quality control is possible as a result. Additionally, firms can employ real-time traceability to combat fake goods.

File and Archive Recordkeeping

RFID aids in keeping healthcare records and files current. In contrast to individually scanning each one, an RFID reader can instantly update the locations of documents and books.

Vaccine Safety

RFID technology has shown to be quite beneficial for a number of aspects of the COVID-19 outbreak. To improve the tracking and security of the many virus vaccines, for instance, the healthcare sector adopted RFID. To track vaccine doses and stop the use of stale or false vaccines, vaccine makers, hospitals, and clinics utilise RFID tags.

Touchless Interactions

Due to the increased acceptance of contactless payment methods, many stores are using RFID to track merchandise at the point of sale. As a result, fewer interactions with people are necessary, and inventory management as a whole is enhanced.

Protect Your Data with New Cloud-Based Features

Because cloud-based apps and services relocate the components of IT support away from the point of operation, businesses can now install centrally managed and available solutions without the usual support and implementation costs.

Cloud-based solutions allow for coordination to occur anywhere. Get real-time updates on inventory across global supply networks to enable just-in-time production across borders.

GIVEAWAY

Despite the challenges created by the pandemic, global shortages and economic losses, the RFID sector has expanded and is expected to keep developing in the years to come. To better understand the people driving this expansion, it’s important to identify critical applications and high-potential untapped areas

“Composed by: Anushka Saxena is a highly accomplished healthcare professional with a background in physiotherapy, & presently pursuing Masters in Hospital & healthcare management. Her experience lies in computer software, databases, healthcare terminologies, documents processing.”

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‘The notion of the possible is precisely that we should not be limited by what we think is possible today what you might think of as a sunrise industry turns into a sunset experience, but be prepared to embrace that and react in a positive way there will be some folks you will need to retrain, and there will be some folks you can’t retrain. like triage; do it humanely’, said Dr Shashi Tharoor, Author and Public Intellectual in a session on ‘Leading The Way Towards A Sustainable & Equitable Future at the TechHR India 2023’. 

In a conversation with Ester Martinez, CEO and Editor-in-Chief at People Matters on how can we make the transition more humane with rapid advancements in technology and AI, Dr Tharoor said, “The ability to think beyond conventional boundaries, to transcend the limitations of today’s possibilities and envision what could be achievable tomorrow, stands as one of the most valuable pursuits in the 21st century, spanning across various disciplines. This importance becomes even more pronounced due to the rapid, whirlwind pace of technological transformation with which all of you are well-acquainted. Consider this: I recently came across a noteworthy study conducted by the Oxford Martin School, which projects that by 2030, a staggering 30% of jobs worldwide will be roles that do not currently exist. The daunting question arises:

How do we prepare individuals for roles that are yet undefined? This dilemma stems from the unpredictable nature of future job requirements. Hence, possessing the capacity and willingness to embrace novel approaches, new undertakings, and unexplored territories—one’s previous experience might never have contemplated—is becoming an indispensable trait for prospective hires, trainees, and learners. This need to adapt, however, often eludes many of us who have not accustomed ourselves to such thinking.”

Embracing change while navigating the unpredictable future of innovation 

Furthermore, Dr Tharoor explained how the world is ever-evolving and new job roles are being defined out of necessity, and with AI the future is unpredictable by saying, “Another significant consideration is the dynamic nature of innovation. The phenomena that seem to be on the ascendant might, in an unforeseen twist, descend into obscurity. An illustrative instance is the emergence and eventual decline of medical transcription during the early years of the century. This practice involved American doctors recording notes, which were then transmitted overnight to qualified Indian professionals to transcribe while the doctors slept, yielding substantial cost savings and efficiency.

This once-promising field experienced a sudden demise due to the proliferation of AI-driven voice recognition software. Now, these doctors, instead of depending on external transcription services, can employ software that converts their speech into text as they speak. This transformative change effectively rendered the medical transcription sector obsolete, leading to its collapse. Similarly, even essential domains like radiology faced paradigm shifts. Qualified Indian radiologists remotely interpreting MRIs for American hospitals had been a lucrative business. However, AI advancements have enabled direct computer-based analysis, circumventing the need for radiologists to review each scan.

Such instances underline the volatile nature of our times, where even established practices cannot be taken for granted. Something that seems possible today may not be possible tomorrow, and something that may be possible tomorrow hasn’t yet been thought of today but could be that keeps you on your toes. This awareness keeps us vigilant, albeit sometimes taxing and nerve-wracking. Nevertheless, it also exudes a sense of exhilaration—every operation within your enterprise presents an opportunity to explore how it could be streamlined, optimized, or made more lucrative through imaginative, even fantastical means.” 

Strategize to make AI an advantage 

Illustrating his own profession as an example, Dr Tharoor shared how with AI adoption even the traditional mechanisms will change. He said, “A lot of things will become possible in the future that isn’t possible today, be prepared to embrace that and to react in an accommodative way for you folks remember part of the answer is retraining but it’s not the whole answer.”

• Individuals and organisations should focus on upskilling and reskilling.
• Embrace a growth mindset and be willing to learn new skills and technologies. 
• Continuous learning and development are crucial to staying relevant in a dynamic job market.
• HR should ensure that people who can’t keep up with this change are given a soft landing, and employ by retraining them, but those who cannot adapt or retrain, deploy them with different roles. 

The World of Work Today – People, Profit and Planet 

Dr Tharoor shares, “One of my favourite concepts in the world of work today is the James Elkington theory called the triple bottom line. Profit is essentially you wouldn’t stay in business but once you’ve taken care of that the other two bottom lines also matter. Planning, the obvious environmental responsibilities we all have that what you do in your business doesn’t actually harm others and harm the environment, you don’t throw toxic sludge into our rivers, you don’t screw dangerous chemicals into the air, all of that stuff that you know you can now assume that everybody is sufficiently conscious of and you can do that. But then what about people?

Now those include two kinds of people, the people who work for you and the people who live around you but don’t work for you and companies need to get used to the idea in the 21st century that those people, both sets of people really matter because ultimately all development, all corporate activity, all political activity is at the bottom above people and if what you do is making you money but arming people in the process you should stop doing it and if you won’t there will be many of us outside who want you to stop doing it. If you can do it in a way that helps people we will encourage and support you and you can legitimately lobby for tax incentives and other things because what you’re doing is also giving you a social good.” 

Source: www.peoplematters.in 

Credit: www.peoplematters.in

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Introduction

The integration of AI in Healthcare has started a great revolution, reshaping every aspect of medical care. From simplifying administrative tasks to revolutionizing diagnostics and treatments, AI is driving health tech innovation worldwide. This transformative synergy between intelligent algorithms and human expertise is starting in a new era of precision medicine, where early disease detection and innovative treatment approaches take center stage. Let’s delve into how AI is being used in healthcare and the significance of Intellectual Property Rights.

Overview

Historically, the healthcare industry has displayed a cautious approach toward integrating technology, which can be attributed in part to earlier setbacks in digitization efforts. A study has underscored that healthcare professionals have dedicated a substantial 49% of their time to administrative duties, leaving only a mere 27% for direct patient care. This imbalance, stemming from outdated systems, not only results in inefficiencies but also contributes to burnout and hasty diagnoses among clinicians. At the same time, the pharmaceutical industry faces its own challenges. Developing a single product takes about ten years and costs around a huge USD 2.5 billion.

However, the progress made in automation and artificial intelligence (AI) offers hope for solving these problems. AI in Healthcare possesses the capability to automate approximately 33% of the manual tasks undertaken by healthcare professionals, thereby ushering in potential cost reductions. The incorporation of AI-powered technologies has the potential to generate savings exceeding USD 150 billion for the US healthcare sector by the year 2026. This encouraging projection has spurred substantial global investments in AI research tailored specifically to healthcare applications. Presently, AI’s self-learning algorithms have demonstrated their capacity to automate tasks and contribute to informed decision-making processes. (source)

AI-Powered HealthTech

1. Robots: In healthcare, robots have transformed from optimizing operational tasks to enabling precise, minimally invasive surgeries. Companies like Diligent Robotics, Aethon, and InTouch Health streamline hospital operations, while Intuitive Surgical, Medtronic, Verb Surgical, and Auris Health enhance surgical precision, benefiting patients and providers worldwide.

2. Medical Imaging Systems: Medical imaging has evolved from X-rays to advanced modalities like MRI, CT, and ultrasound, aiding precise diagnoses. AI-driven systems like GE Healthcare’s Edison and Siemens Healthineers’ AI-Rad Companion enhance image analysis, while companies like Arterys, Enlitic, and Zebra Medical Vision advance AI-powered medical imaging for improved healthcare outcomes.

3. Personalized Care: AI-driven health assistants like Your.MD and Suki.AI offer personalized medical advice, while Babylon Health, CureMe, Oscar Health, and Welltok develop their versions. AI-powered EHR systems from GE Healthcare, Oracle, Cerner, Allscripts, and Epic analyze patient data for informed decision-making, treatment recommendations, and improved healthcare delivery.

4. Analysis and Discovery: AI revolutionizes healthcare through predictive analytics like HealtheIntent, Caboodle for disease prediction and drug discovery platforms like BenevolentAI, and Atomwise that accelerate candidate identification. These innovations hold the potential to transform patient care and reshape medicine’s future.

5. Virtual Reality Therapy: VR therapy platforms like MindMotionPRO, AppliedVR, etc. with AI integration provide immersive rehab for patients. As AI improves, healthcare will see new and creative uses that will greatly change how services are provided.

What does the future hold?

The utilization of AI in the healthcare sector is experiencing a significant expansion. Presently, the worldwide market for AI in healthcare has exceeded a value of $10 billion. Furthermore, the count of startups operating at the intersection of healthcare and AI has surged by 75% within the last two years. AI is finding its application in an increasingly diverse range of healthcare domains, encompassing mental health support, telehealth services, precision medicine, and the detection of healthcare fraud. As AI advancements persist, it is foreseeable that the future will bring forth even more groundbreaking and inventive applications of AI in this field.

The forthcoming trends in AI-powered health tech are compelling. AI-driven virtual assistants will enhance patient engagement and personalized care. Robotic surgery, gene editing, and personalized medicine will revolutionize treatment approaches, ensuring precision and effectiveness. Healthcare fraud detection powered by AI will safeguard financial integrity. AI-powered clinical decision support, drug discovery, remote patient monitoring, and public health initiatives will optimize diagnostics, therapies, and preventive strategies. These trends collectively exemplify the remarkable potential of AI to improve patient outcomes, streamline operations, and foster innovation across the healthcare ecosystem. (source)

As AI’s capabilities evolve, it will catalyze transformative advancements. The integration of big data analytics, predictive algorithms, and machine learning will empower clinicians with actionable insights, revolutionizing disease prevention, diagnosis, and treatment. AI-driven telehealth and remote monitoring will enable greater accessibility, especially in underserved areas. Ethical considerations and data privacy will remain pivotal as AI reshapes patient care and healthcare management.

Ultimately, AI’s future in health tech holds the promise of more accurate diagnoses, efficient treatment pathways, reduced costs, and improved patient experiences. By collaborating across disciplines and addressing challenges, AI has the potential to realize a future where healthcare is increasingly patient-centric, data-driven, and technologically empowered. (source)

How can patents help?

Patents are instrumental in fostering the growth and development of AI in the healthcare technology sector. They play a pivotal role in encouraging innovation, protecting intellectual property, and facilitating collaboration among stakeholders. By offering inventors and companies exclusive rights to their AI-driven health tech inventions, patents incentivize investments in research and development, driving continuous advancements in healthcare technologies.

Moreover, patents safeguard novel ideas and technologies, preventing unauthorized use or copying, and ensuring that inventors and companies can benefit from their creations. The disclosure of patented technologies in public documents enables knowledge sharing, enabling other researchers and companies to learn from and build upon the innovation. As a result, patents contribute to the acceleration of technology transfer and the integration of AI health tech solutions into real-world healthcare settings.

Furthermore, patents can increase the value of a company’s assets, attracting investment and fostering partnerships with other players in the healthcare industry. Ultimately, by promoting competition and driving improvements in the quality and standards of AI health tech products, patents play a significant role in advancing healthcare and expanding access to medical innovations worldwide. (source)

AI Innovations and Patents

1. In 2017, IBM was granted a patent for an AI-based system that can diagnose cancer from medical images which helped to protect its investment in research and development and to promote the adoption of its AI-based cancer detection system.

2. In 2018, Google was granted a patent for an AI-based system that can personalize treatment plans for patients with diabetes and has helped Google to protect its intellectual property and to promote the development of new AI-based diabetes treatments.

3. In 2019, Johnson & Johnson was granted a patent for an AI-based system that can detect and prevent healthcare fraud and protect its intellectual property and promote the adoption of its AI-based healthcare fraud prevention system.

4. In 2020, Stanford University got a patent for an AI system that can foresee the chances of sepsis in patients. It has helped Stanford University to protect its intellectual property and to promote the development of new AI-based sepsis prediction systems.

5. In 2021, Mayo Clinic received a patent for an AI system that detects Alzheimer’s disease from brain scans. This patent safeguards their ideas and encourages the creation of more AI-based tools for diagnosing Alzheimer’s.

6. In 2022, Massachusetts General Hospital was granted a patent for an AI-based system that can personalize cancer treatment plans for patients. This patent has helped Massachusetts General Hospital to protect its intellectual property and to promote the development of new AI-based cancer treatments.

Source: www.mondaq.com

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Over the last two decades, cognitive technologies like Artificial Intelligence (AI) and Machine Learning (ML) have been evolving at a rapid pace. But it is in recent years that they have taken groundbreaking strides in terms of investments to propel growth. First emerging as obscure tools meant for a distant future, the technologies are now widespread and available to the masses for a variety of use cases. There have been two pivotal moments that have accelerated the adoption and investments into AI. The first is the self-driving car movement, and the other is the launch of Chat GPT by OpenAI. These two events have led to the belief that AI is now primed for everyday production, and no industry can afford to ignore investing in it. In this article, we will explore the investor opportunity for ventures building upon AI and the macro challenges that may impact the sector.

Startups are catching the AI wave

Unlike the internet boom of the 90s and the 2000s, where multiple new entrants came into the fold, the AI wave is being pioneered by established tech giants, as significant upfront investment is required to develop the technology. Yet, while the chunk of spearheading AI lies with the behemoths, startups can also find niches to operate in.

Broadly speaking, these startups can support two important business needs: first, many companies require third-party infrastructure providers to implement AI in their products; while the second opportunity lies in application work involving automating business processes. These are typically back-office administrative and financial activities, which help marketing and support departments within enterprises.

There are numerous examples of AI-based applications in the global market and many businesses are capitalizing on their capabilities to automate support queries or generate marketing content through AI. Closer home, in India too, one can witness the growing adoption of AI in various industries — from finance to healthcare, and education to e-commerce. Examples include chatbots for customer support, medical image analysis for healthcare, and personalized learning tools for education.

Investor interest in AI startups

Given their potential to transform various industries, there is growing investor interest in ventures building upon the capabilities of AI. According to Tracxn data, funding into these startups in the Indian market has increased threefold from 2020 to 2022, growing from $1.76 billion to $5.28 billion. Compared to the global context, Indian startups in the sector are becoming more prominent to investors, ranking the 3rd highest in terms of funds raised in 2022, according to Tracxn data. This places India ahead of the United Kingdom, Israel and France, pushing the country closer to becoming a global hub for AI applications development. Investors are eager to capitalize on the opportunities presented by AI and democratizing services that have hitherto been inaccessible, especially for smaller businesses.

Despite the contraction in private market funding globally, as well as in India, investment into AI-based ventures continues to flow. In Q1 CY23, there have been 50 funding rounds which have deployed capital to these startups. Online investing platform, Mensa Brands raised $36.3 million in debt funding, while machine vision player, Mad Street Den raised the largest equity deal in the period through a $30 million Series C round. However, the activity in funding is far less pronounced compared to Q1 CY22, where 133 funding rounds took place and the largest deal size was $400 million raised by conversational AI player Uniphore in a Series E round.

Inflows from private entities have been decreasing due to global macroeconomic pressures, but startups in this sector are continuing to receive significant support through government-led initiatives falling under Prime Minister Narendra Modi’s Startup India program. This fact becomes more salient when we see that leading investors in AI-based ventures in terms of portfolio count are T-Hub, a startup incubator created by a Public-Private-Partnership (PPP) fostered by the Government of Telangana, and Startup Karnataka, another state-level incubation program. Furthermore, initiatives like the National AI Strategy, the National Programme on AI, and the AI for All program, along with tax incentives for startups and investors, are expected to create a favourable environment for AI startups in India.

Challenges

Growing investor interest notwithstanding, a challenge for AI-based startups lies in the fact that India’s tech ecosystem is still relatively new, resulting in a shortage of senior technical and PhD-level talent compared to more established tech hubs. This limits the number of startups founded by highly educated candidates. However, with the maturing of the tech ecosystem in the country, more senior technical talent is expected to stay, which will foster the emergence of more such companies in the future.

Private market investments will always go through cycles of extreme highs and lows and we are currently in the midst of a bear market. As highlighted earlier, Q1 CY2022 did see 133 funding rounds occur in the Artificial Intelligence space in India, receiving a total of $2.36 billion in funding. The sector was burgeoning with many new ventures, as the majority of funding rounds were dedicated to seed capital (75), followed by series A (15) and angel (13) rounds. This is not surprising as although the global startup ecosystem is experiencing a funding crunch that began in Q4 CY2021, early-stage deals did not lose momentum in 2022 and actually grew by 5.8% compared to the previous year. Investors are lowering their valuation metrics and are taking a more patient approach with late-stage deals, looking to fund ventures that portray clear paths to profitability.

Across sectors, startups in the early phases of growth have fared better in comparison to those raising late-stage capital. However, the current scenario for these nascent ventures Yet, as the development of AI-based applications is seeing phenomenal growth in India, supported by the launch of GPT and other large language models (LLMs), the verdict is still out on the sector’s resilience as compared to others.may not be as promising as it was in 2022. According to Tracxn data, total funding in Indian early-stage deals fell by 72% in Q1 CY2023 compared to Q1 CY2022. Moreover, AI startups’ total funding across early and late-stage rounds has reached a total of $308 million Q1 CY2023, which means they have gone through a steep decline of 87% YoY, where average funding amounts have fallen from $20.5 million to $8.34 million. With the recent collapse of SVB, Credit Suisse’s rescue by UBS, and proposed changes to the Angel Tax provision, the funding winter may be further exacerbated.

Source: indiaai.gov.in

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After expressing gratitude to Mr. Sachin, Dr. V K Singh, Mr. Sanjay, and the team, Dr. Sangeeta embarked on the continuation of the discussion. She emphasized the contemporary significance of the topic and swiftly provided a concise overview of the fundamental aspects of ‘one health’ and antimicrobial resistance. Dr. Sangeeta delved into the historical context, touching upon the foundational phase of the antibiotic era and its subsequent prosperous period. However, she noted a shift in momentum during the 1980s and 1990s when the pace of new antimicrobial discoveries began to decelerate, marking an era characterized by the rise and fall of antibiotics.

Highlighting the global scope of the issue, she underlined that antimicrobial resistance (AMR) has evolved into a worldwide health concern, further exacerbated by the context of the COVID-19 pandemic. Dr. Sangeeta pinpointed Asia and Africa as the regions most severely impacted by AMR, where antibiotic effectiveness has diminished significantly, posing a tangible threat to development. A concerning prediction was shared, projecting a staggering annual death toll of 10 million by 2050, accompanied by a substantial global economic burden resulting from inaction.

Moving forward, Dr. Sangeeta delved into the catalysts driving the escalation of this problem. She elucidated the reasons for the influence of AMR on sustainable development goals. One key factor lies in the dual usage of antibiotics for both human and non-human purposes, with veterinary applications equally crucial for preserving animal health. This holistic approach is pivotal for safeguarding human well-being. The discourse turned to the existing challenge of irrational antibiotic usage in human healthcare, creating a complex dilemma regarding which sectors contribute disproportionately to antibiotic consumption.

Furthermore, Dr. Sangeeta highlighted the interconnectedness of bacterial propagation across borders and supply chains, facilitated by extensive air networks. She transitioned to emphasize that the contemporary surge in antimicrobial usage has become the principal driver behind the development and dissemination of AMR. The misuse of antibiotics by medical practitioners, patients, pharmacists, and also their application in animal husbandry and agriculture, emerged as significant contributors to this dilemma.

Today these antibiotics are used as shortcut for poor sanitization techniques and shortage for poor patient education and that’s how overuse is done, nearly 50 % patient admit in IPD get antibiotics, now match it with morbidity, it is happening because these antibiotics in injection used for nonbacterial trivial infection like common cold & diarrhoea, Copious prescription is major component for resistant problems. Consequences of AMR, are where the post antibiotic era is imminent & threatens to reverse medical progress, like simple infections, higher chances of treatment failure and deaths, longer stay & uncontrollable epidemics. Now the horror & the importance coming from the running out of antibiotics. Losing antibiotics will be disastrous for humanity as many lives have been saved, even careful use of antibiotics can lead to emergence of AMR. It raises a number of ethical problems like issues regarding overuse and lack of access to antibiotics, intergenerational problems like; they will be able to see MDR only not antibiotics, paradoxes influencing AMR, like brand & positioning.

Dr. Sangeeta further summarised the AMR as Multisectoral, Multidimensional & Multifaceted Problem, as it is biological containment, technical and economic issue as we use it for cost economy of healthcare and regulations are need to control availability and implementation, educational issue as most of the people are not aware of this & behavioural issues. ONE HEALTH is an complex issue which includes human, environment & animal food, legal enforcements of laws to restraint the use and control infection and how can we improve antibiotic prescribing through common areas of improvement  like wrong choice, overprescribing and many more, effective stewardship is much more complex now and also the diagnostic errors but the problem is pathogens are now originating from plants and animals which are coming back to us through different modes, as there is a widespread use of antimicrobials in livestock production, agriculture & not only for therapeutic purposes.

She further added India has one-fourth incidence of MDR TB & also India is one of the largest producer and consumer of antibiotics for human use and has a high level of mortality due to drug resistant pathogens so strengthening and engagement with sector specific regulatory organizations and state governments is required. Here we come with approach ONE HEALTH where we take care of human health, animal health, plant health, environment all together, in different sectors, it is nothing but working together and taking actions and is all about 5 Cs’ Coordination, Collaboration, Cooperation, Communication & Commitment .National action plan was made by WHO, it identify critically ill antibiotics in different sectors which we need to save and India also adopted this frame and we look into regulation and innovative projects which enhance financing & for that leadership is very important. NAP-AMR strategies are implemented in states of India which are; awareness and understanding of AMR, knowledge & awareness, infection prevention & control, optimistic use, innovations, and leadership and focusing on one health approach through mass campaigns for school children, strengthening laboratory capacities, surveillance & AI based systems are also important. She concludes over the years research is not happening in this area so people are losing expertise in this area, if we need to handle  this problem, we need an end to end approach for newer & older products.

Mr. Sachin expressed his gratitude to Dr. Sangeeta for imparting her valuable knowledge and she was further engaged in a Q&A session with questions. During this interaction, he presented a query to Dr. Sangeeta regarding the concept of AMR being a time bomb with a lingering impact. He inquired about her overall viewpoint on this matter. In response, Dr. Sangeeta conveyed that we are presently in the phase beyond the effectiveness of antibiotics, and she expressed a lack of optimism unless prompt actions are taken and various sectors undergo enhancement. She emphasized that addressing this issue necessitates substantial financial resources and a considerable investment of time.

Mr. Sachin continued with Q & A Round; He asked about the Case of Calcutta where the first patient in the world got plant fungus, he queried; Do u foresee more such zoonotic cases, new viruses which jump into species like covid in future? Dr. Sangeeta responded, we are destroying our environment like deforestation and it was distinct priorly but now we are more in exposure to animal so the chances of increasing in the cases will be more in future. 

Further he questioned about in general drug discovery side methods, like E.coli pathway blockage method by AI molecule approach; to which she replied, our 5^th priority is research we need to find alternatives from the research & for this we need to work on resistance mechanism as well, it is the easiest part to work on and also preserve the present one and AI can predict chance of appropriate antibiotics and we should use these technologies here to full action. We can use system surveillance and AI, they have a lot of potential.

And advice for young generation, it is a part of curriculum, antibiotics studies on microbiology integrated curriculum is provided some changes, & in Post graduate teaching also, earlier pharmacology was taught now integrated and competency curriculum changes are there , then National Medical Commissions brought advisory that all medical colleges should have antimicrobial stewardship program and actions, so now major focus added to education and induction training and job training and most important to know about when not to prescribe this if focus goes on that perhaps things will change. 

At last, Mr. Sachin thanks for sharing knowledge and heartfelt gratitude towards her and He also made an important announcement about the conference that will be held physically on 13^th & 14^th of October in partnership with IIIT Delhi, the topic of the conference is “CLINICAL VALIDATION OF EMERGING TECHNOLOGIES”. He again thanked all the participants for joining & this concluded the meeting.

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