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AI – Reaping the benefits of technology

Artificial intelligence is a powerful and disruptive area of computer science with the potential to fundamentally transform the practice of medicine and the delivery of healthcare.

Healthcare systems around the world face significant challenges in achieving the quadruple aim for healthcare: improve population health, improve the patient’s experience of care, enhance caregiver experience, and reduce the rising cost of care. Aging populations, growing burden of chronic diseases, and rising costs of healthcare globally are challenging governments, payers, regulators, and providers to innovate and transform models of healthcare delivery. Moreover, against a backdrop now catalyzed by the global pandemic, healthcare systems find themselves challenged to perform (deliver effective, high-quality care) and transform care at scale by leveraging real-world data-driven insights directly into patient care. The period after March 2020 saw an unprecedented shift to virtual health, fueled by necessity and regulatory flexibility. The pandemic opened the aperture for digital technologies, such as artificial intelligence (AI) to solve problems, and highlighted the importance of AI. The pandemic has also highlighted the shortages in healthcare workforce and inequities in the access to care.

The application of technology and AI in healthcare has the potential to address some of these supply-and-demand challenges. The increasing availability of multi-modal data (genomics, economic, demographic, clinical, and phenotypic), coupled with technology innovations in mobile, internet of things (IoT), computing power, and data security, herald a moment of convergence between healthcare and technology to fundamentally transform models of healthcare delivery through AI-augmented healthcare systems.

In particular, cloud computing is enabling the transition of effective and safe AI systems into mainstream healthcare delivery. Cloud computing is providing the computing capacity for the analysis of considerably large amounts of data, at higher speeds and lower costs compared with historic on premises infrastructure of healthcare organizations. Indeed, many technology providers are increasingly seeking to partner with healthcare organizations to drive AI-driven medical innovation, enabled by cloud computing and technology-related transformation.

India’s think tank NITI Aayog, in its recent report, has put healthcare on top priority in the list of domains that need an AI push. Riding on latest technology, the Indian healthcare sector with a compound annual growth rate of around 22 percent since 2016 is likely to breach the USD 372 billion mark by 2022. According to a NITI Aayog report, the latest technology adoption including that of AI has opened new avenues of growth in the healthcare sector. India is the perfect petri dish for enterprises and institutions globally to develop scalable solutions, which can be easily implemented in the rest of the developing and emerging economies. Simply put, solve for India means to solve for 40 percent or more of the world. The report also anticipates AI application in healthcare can help overcome high barriers within the healthcare domain, particularly in rural areas that suffer from poor connectivity and limited supply of healthcare personnel. This is where AI-driven diagnostics, personalized treatment, early identification of potential pandemics, and imaging diagnostics come handy.

Machine learning models are already getting good at helping radiologists. But how legit are these algorithms? Have they been thoroughly vetted by government bodies? For example, when Google AI, in partnership with the Ministry of Public Health in Thailand, conducted deep learning experiments in a handful of clinics, they found fundamental issues in the way the deep learning systems were deployed. Though the model improved regularly, the challenges came from factors external to the model. Software-as-a-medical-device or SaMD comes with lots of challenges.

When it comes to India, AI incorporation has to work around challenges, such as shortage of qualified healthcare professionals and services and non-uniform access to healthcare across the country. India’s eHealth ambitions are yet to gain significant traction despite promising starts with initiatives, such as National eHealth Authority (NeHA), Integrated Health Information Program (IHIP), and Electronic Health Record Standards for India.

According to NITI Aayog, in India, AI adoption for healthcare applications is expected to see an exponential increase in the next few years. The advances in technology, and interest and activity from innovators will allow India to solve some of its long-existing challenges in providing appropriate healthcare to a large section of its population. AI combined with robotics and the Internet of Medical Things (IoMT) could potentially be the new nervous system for healthcare, presenting solutions to address healthcare problems and help the government meet mission-critical objectives.

The deployment of AI is still at a very early stage, particularly in the form of clinical interventions. A number of identified use-cases are still at a development and testing stage. Most of the current use-cases take the form of decision support systems, followed by process optimization and virtual assistants. Computer vision – one of the more advanced applications of AI – is being used to train AI algorithms to read X-rays and scans to support the processes of disease detection and diagnosis. Only a small handful of companies are developing surgical simulators, personalized health solutions, and patient monitoring systems.

Moreover, only a small number of interventions use NLP and speech recognition, both of which are critical for meeting diverse linguistic and literacy needs in the country. This is likely to change, however, with growing investments by big tech actors, such as Google and Microsoft, in the development of these capabilities. Google, Microsoft, and IBM have multiple partnerships with private hospital groups such as Narayana, Apollo, and Fortis, as well as partnerships with state governments in India. These are working on a range of solutions, including AI systems for hospital management, disease detection, and prediction, as well as AI service delivery in remote areas.

The four areas in which interventions are being developed include:
Disease detection and diagnostics. ML is being used to build decision-support systems for diagnostics, as well as in predictive systems for prognostication. Computer vision and DL models are being used to read medical scans, such as X-rays, CT scans, PET scans, and ultrasound scans. AI-based systems are being used for early detection of tumors, e.g., non-invasive, non-touch, and non-radiation approaches to detect breast cancer – as well as predicting cancer recurrence through a risk score. AI-based applications are also being developed and used to build systems that can analyze images of blood. SigTuple, for example, is using an AI platform called Manthana for automated analysis of blood smears as well as for digitization of blood, urine, and semen samples. Researchers at one of India’s leading government hospitals have developed a tool that leverages thermal imaging and AI-based tests to help predict the onset of hemodynamic shock. AI systems for tuberculosis diagnosis and DR systems are also being developed. Platforms, such as OnliDoc and Lybrate, are also using AI methods to provide virtual assistance and diagnostics remotely. OnliDoc uses AI for symptom checking and treatment selection.

Process optimization. ML processes are being developed to create new efficiencies in areas, such as hospital bed management and processing of insurance claims. A few online platforms that assist with helping find a doctor, storing health records, or procuring medicine are using ML to improve efficiencies in these processes. Others are automating the first-level screening of symptoms, finding doctors, and booking appointments. Optical character-reading systems are also being used to scan prescriptions and check prescribed medications against the inventory. ML is also being developed for bed management and planning, to predict rates of patient churn (turnover of beds), in order to optimize the use of beds in hospitals.

Patient-facing applications. Chatbots are increasingly being used as conversational agents for interaction with patients. The online platform mfine, for example, handles more than 15,000 cases per month – approximately the number of patients handled by Manipal Hospitals. Several large hospitals now use chatbots to schedule appointments, converse, and collect basic details and symptoms, before handing over a case to a doctor. In the case of mental health, chatbots are being used as the first level of intervention in behavioral coaching and as a means of addressing loneliness. Systems to monitor or track patient progress are also under development. AI-driven analysis of camera feeds was found to have been used in one case to detect emotional responses and patient fatigue in order both to help monitor patients during the treatment process and to alert medical staff. Sensor data is also being used to monitor patient recovery and response to medication after surgery or treatment. Wearable sensors and AI-based solutions are being developed to measure vital signs and provide doctors with actionable insights.

While at a much earlier stage of development, deep learning techniques are being developed to derive molecular insights for drug discovery. Surgery simulators that are continually updated are also being developed to train doctors for spine and knee surgery. A surgery simulator center was recently opened in Delhi.

Additionally, a few start-ups and initiatives have begun to provide personalized health solutions. Healthi, a digital health and wellness start-up in Bangalore, uses predictive analytics, personalization algorithms, and ML to deliver personalized health suggestions. Similarly, Manipal Hospitals is using IBM Watson for oncology, a cognitive-computing platform, to help physicians discover personalized cancer care options.

Growth opportunities may be hard to come by without significant investment from companies, but a major opportunity exists in the self-running engine for growth within the AI sector of healthcare.

Unlike legacy technologies that are only algorithms/tools that complement a human, health AI today can truly augment human activity. With immense power to unleash improvements in cost, quality, and access, AI is exploding in popularity. Growth in the AI health market is expected to reach USD 6.6 billion by 2021 – that is a compound annual growth rate of 40 percent. In just the next 5 years, the health AI market will grow more than 10×2. The increase in the inflow of patient health-related digital data, growing pressure for cutting down healthcare spending, and rising demand for personalized medicine are some of the key factors aiding revenue growth in the market.

The increase in the prevalence of chronic diseases in an aging population is another key factor that has raised the need to understand and diagnose diseases in their early stages. With deep learning technologies, it would become easy to predict diseases based on their historic health data.

Moreover, content analytics, natural language processing (NLP) tools, and AI can help in speedy diagnosis of the patient’s condition.

In 2020, the software solutions segment dominated the market for AI in healthcare, and accounted for the largest revenue share of 40.6 percent. An increase in the adoption of AI software solutions among healthcare payers and providers is one of the key factors driving the AI software segment.

Moreover, AI technology has played a pivotal role in the ongoing COVID-19 pandemic, and has positively influenced related markets. It is being implemented in rapid detection and diagnosis of the virus strains and in managing the outbreak through personalized information. AI algorithms can be trained through chest CT images, exposure history, symptoms, and laboratory findings to rapidly diagnose COVID-19-positive patients. In an NCBI study in April 2020, it was found that an AI system detected 17 out of 25 COVID-19-positive patients, based on normal CT images, whereas professionals diagnosed all patients as COVID-19-negative.

Therefore, AI-based diagnosis can be used to accurately detect a disease even before evident symptoms appear. These systems can be trained to analyze images and patterns to create algorithms to aid professionals in diagnosing a disease accurately and quickly, thereby increasing the adoption of AI technology in the healthcare space.

Additionally, the ongoing COVID-19 pandemic, coupled with growing government initiatives, increasing mergers and acquisitions, portfolio expansion, and collaborations to promote the implementation of AI in healthcare, is further accelerating its adoption, thereby contributing to market growth.

In August 2020, Digital Diagnostics Inc., formerly known as IDx, acquired 3Derm Systems Inc. The acquisition aimed at including telemedicine capabilities for dermatology, 3DermTriage, and to prepare its autonomous AI skin cancer diagnostic system, 3DermSpot, for FDA authorization.

AI technology is expected to experience a separate growth trajectory owing to this pandemic with surged market growth in a few AI applications pertaining to the healthcare sector. Key participants are also expanding their portfolio to meet the demands in the current pandemic situation, which is also one factor depicting the surge in AI penetration in healthcare applications. Qventus installed AI-based patient flow automation systems to help the healthcare facilities during this pandemic situation. The solution includes staying length optimization, ICU and med-surge capacity creation, COVID scenario planner, and critical resource control. Also, in May 2020, MIT-IBM Watson, AI Lab pushed AI-based technology by funding 10 research projects that are aimed at addressing the economic and health consequences of the COVID pandemics.

Furthermore, the companies are increasingly focusing on expanding their geographical reach and introducing newer, innovative solutions through various strategies, including partnerships, product launches, and collaborations, to support the end-users in overcoming the shortage of radiologists, deliver value-based care, combat the COVID-19 pandemic by early disease detection and diagnosis, and maintain a competitive edge in the market.

In June 2020, GE Healthcare launched Thoracic Care Suite to detect chest X-Ray abnormalities, such as pneumonia and tuberculosis, caused by COVID-19 as well as support clinicians and health systems to ensure quick diagnosis and effective treatment of patients.

In April 2020, Microsoft invested USD 20 million to help in COVID-19 research with the use of AI technology and data sciences, mainly focusing on hospital resources, diagnostics, and other critical areas.

North America dominated the market for AI in healthcare and accounted for the largest revenue share of 58.9 percent in 2020. However, in Asia-Pacific, the market for AI in healthcare is expected to grow fast over the next 5 years. This growth is seen through development and innovations in entrepreneurship startups established in Asia-Pacific countries.

Service differentiation, increasing investments in R&D, and collaborations with key industry participants are the key strategies adopted by key competitors to gain a competitive edge in the industry. Moreover, rising investments in AI by startups are also propelling the market growth.

What are the current and future use-cases of AI in healthcare? AI can enable healthcare systems to achieve their quadruple aim by democratizing and standardizing a future of connected and AI-augmented care, precision diagnostics, precision therapeutics and, ultimately, precision medicine. Research in the application of AI healthcare continues to accelerate rapidly, with potential use-cases being demonstrated across the healthcare sector (both physical and mental health), including drug discovery, virtual clinical consultation, disease diagnosis, prognosis, medication management, and health monitoring.

Currently, AI systems are not reasoning engines, i.e., cannot reason the same way as human physicians, who can draw upon common sense or clinical intuition and experience. Instead, AI resembles a signal translator, translating patterns from datasets.

AI systems today are beginning to be adopted by healthcare organizations to automate time consuming, high volume repetitive tasks. Moreover, there is considerable progress in demonstrating the use of AI in precision diagnostics (diabetic retinopathy and radiotherapy planning).

In the medium term, there will be significant progress in the development of powerful algorithms that are efficient (e.g., require less data to train), able to use unlabeled data, and can combine disparate structured and unstructured data, including imaging, electronic health data, multi-omic, behavioral, and pharmacological data.

In addition, healthcare organizations and medical practices will evolve from being adopters of AI platforms to becoming co-innovators with technology partners in the development of novel AI systems for precision therapeutics.

In the long term, AI systems will become more intelligent, enabling AI healthcare systems achieve a state of precision medicine through AI-augmented healthcare and connected care. Healthcare will shift from the traditional one-size-fits-all form of medicine to a preventative, personalized, data-driven disease management model that achieves improved patient outcomes (improved patient and clinical experiences of care) in a more cost-effective delivery system.

AI could significantly reduce inefficiency in healthcare, improve patient flow and experience, and enhance caregiver experience and patient safety through the care pathway; for example, AI could be applied to the remote monitoring of patients (intelligent telehealth through wearables/sensors) to identify and provide timely care of patients at risk of deterioration.

In the long term, it is expected that healthcare clinics, hospitals, social care services, patients, and caregivers to be all connected to a single, interoperable digital infrastructure using passive sensors in combination with ambient intelligence.

Although AI in healthcare has huge potential, as with most developments in the technological space, there are a number of known current limitations.

Initial adoption issues. Experiencing teething problems with the introduction of any new technology is not rare, but must be overcome for large-scale adoption of AI to occur in the healthcare market. Ultimately, the adoption of AI will attract stakeholders who will invest in AI, and successful case studies need to be highlighted and presented for future encouragement. These case studies will require some early adopters of healthcare companies to kick start the process.

Data privacy concerns. Privacy within healthcare is, by nature, extremely sensitive and thus confidential. For utmost confidence in technology, systems should be put in place to ensure data privacy and protection from hackers. Unfortunately, data breaches continue to be a common occurrence as reported before when UW Medicine exposed one million patient records or with Missouri Medicaid. But privacy concerns should not be a deterrent from adopting AI in the healthcare space.

Compliance to regulations. Health Insurance Portability and Accountability Act (HIPAA) and a number of other patient-data laws in USA are subject to the approval of governing organizations, e.g., regulatory bodies to ensure that federal standards are maintained. The sharing of data among a variety of databases poses challenges to the HIPAA compliance, and care must be taken around these areas if future developments are to succeed. As companies developing software, therefore AI, are also required to comply with Hitrust rules, current rules and regulations are definitely known to be a barrier to AI. adoption.

Black box difficulty. Deep learning, AI, and machine learning do not have the ability to ask the question why? As a result, the logic behind decisions is not justified, meaning mostly guesswork is required to how the decision was made. How and why the decision has been made is key to the information within the treatment plan. With a lack of reasoning can come a lack of confidence within the decision, potentially rendering the technology as unreliable or untrustworthy by both patients and professionals.

Stakeholder complexities. When it comes to the stakeholders within the adoption of AI in healthcare, everyone, including patients, insurance companies, pharma companies, healthcare workers, etc., are key. Resistance to pursue the technology at any of the aforementioned levels would result in issues and potential failure to the incorporation of the technology in the macro. Stakeholdering is one of the top ten reasons why the healthcare industry as a whole is not innovating enough in 2019.

Clinical decision support. Diagnostic errors account for 60 percent of all medical errors, and an estimated 40,000 to 80,000 deaths each year. As a result, AI has been employed in a variety of different areas in a bid to reduce the toll and number of errors made by human judgement. That said, there continues to be significant pushback when it comes to AI adoption in the clinical decision support process as scientists and medical personnel continue to approach the topic of AI with incredible caution.

Easy to use with a clear output. With minimal operator training needed and design with common output formats that directly interface with other medical software and health record systems, the system is incredibly easy to use and simple to implement. A clear output from the system allows 60 seconds to identify whether the exam quality was of sufficient quality, the patient is negative for referable DR or the patient has signs of referable DR. Following signs of referable DR, further action in the form of a human grader over-reading, teleconsultation, and/or referral to an ophthalmologist may be suggested.

AI can undoubtedly bring new efficiencies and quality to healthcare outcomes in India. However, gaps and challenges in the healthcare sector reflect deep-rooted issues around inadequate funding, weak regulation, insufficient healthcare infrastructure, and deeply embedded socio-cultural practices. These cannot be addressed by AI solutions alone.

Moreover, technological possibility cannot be equated to adoption. In India, poor digital infrastructure, a large, diverse and unregulated private sector, and variable capacity among states and medical professionals alike, mean that the adoption of AI is likely to be slow and deeply heterogeneous.

The same factors also make it quite likely that well-established private hospitals will be the main adopters. This in turn would imply that much of the dominant narrative or rationale for the development of AI in healthcare, in terms of improving equity and quality, is unlikely to be addressed through market forces alone: these solutions are more likely to serve populations who already have access to high-quality care, typically in cities with well-developed digital infrastructure.

In many small hospitals and single-provider practices in India, administrative systems have barely moved beyond rudimentary ICT solutions, such as invoicing and billing platforms.

The effectiveness of these systems will depend on accurate identification of problems and their matching to appropriate solutions. Currently, there is a risk that solutions are technology-led rather than problem-led, and they are as a result often blind to specific contextual needs or constraints.

For example, it might not be the best approach to design real-time or synchronous solutions for digital products meant to be used in remote areas, where basic internet infrastructure is lacking. Designing the right digital interventions is often challenging because of the digital divide between the user and the technology developers, who are typically more adept at using technology than the user is.

Finally, issues around privacy, misuse, and accountability are only slowly being understood, and require much more far-reaching consideration before AI can deliver safe and fair healthcare solutions.

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