The future with AI is unwritten – and rife with possibilities

While AI in healthcare holds tremendous promise, it will be some time before AI-dominated diagnostics become the norm across the board.

There is no question that artificial intelligence (AI) is moving quickly in the healthcare industry. Even just a few months ago, AI was still a dream for the next generation – something that would start to enter regular care delivery in a couple of decades – maybe 10 or 15 years for the most advanced health systems. AI can improve healthcare by fostering preventative medicine and new drug discovery. Two examples of how AI is impacting healthcare include IBM Watson’s ability to pinpoint treatments for cancer patients, and Google Cloud’s Healthcare app that makes it easier for health organizations to collect, store, and access data. AI has helped improve patient outcomes at reduced costs. Besides, the introduction of this technology in healthcare promises easy access, affordability, and effectiveness.
Recent advancements in AI have fueled discussion of whether AI doctors will replace human doctors in the coming future. While the idea of replacing human doctors, may sound absurd, AI can help human physicians to make better decisions. In certain areas of healthcare like radiology, it can replace human judgment entirely.
The ability of AI to use sophisticated algorithms and learn features from a massive amount of data is truly commendable. With the help of these algorithms, insights for assisting clinical practice can be obtained. AI can be equipped with self-correcting and learning abilities, which help the system get better accuracy based on the feedback it receives. Therefore, it gets better with time. AI systems can help physicians in many ways. Since they are armed with a lot of information, they can assist in clinical decision making. Also, diagnostic errors and therapeutic errors can be minimized.

Indian market
The applications of AI in the healthcare space will be worth `431.97 billion by 2021, expanding at a rate of ~40 percent, predicts Market Watch. Based on this growth of AI applications in healthcare, the doctor-patient ratio in India is expected to reach ~6.9:1000 by 2023. The capability of AI applications to improve doctors’ efficiency will help in tackling challenges like uneven doctor-patient ratio, by providing rural populations high-quality healthcare, and training doctors and nurses to handle complex medical procedures.
The adoption of AI is reshaping the Indian healthcare market significantly. AI-enabled healthcare services like automated analysis of medical tests, predictive healthcare diagnosis, automation of healthcare diagnosis with the help of monitoring equipment, and wearable sensor-based medical devices, are expected to revolutionize medical treatment processes in the country.
The grim doctor-patient ratio adds to the woes of the Indian medical industry. The healthcare delivery system will require an investment of around USD 245 billion by 2034 and will need to pump in around 3.6 million doctors and 6 million nurses in the next 20 years, predicts PricewaterhouseCoopers. At present, there are only 1.1 hospital beds, 0.7 doctor, and 1.3 nurses per 1000 people.
To address this, new age companies are using AI to transform healthcare experience and to enable all stakeholders in the healthcare ecosystem to collaborate and provide real-time solutions. The technology infrastructure present has shown some promise and relief, but there remains a broader need for a comprehensive solution that caters to both aspects of the spectrum – patients and care providers.
There are many factors, such as absence of AI designing guidelines for healthcare system that inhibit strategic growth of AI applications in healthcare industry in India. Also, significant delays and hurdles are observed in certification of any AI application, thereby delaying the adoption of AI technology and applications.
Major players operating in Indian AI market include Aindra Systems Private Limited, Nirmai Health Analytix Private Limited, IBM India Private Limited, and Microsoft Corporation (India) Private Limited.

Global market
The global AI in healthcare market is expected to reach USD 27.6 billion by 2025 with CAGR of 43.5 percent, predicts Meticulous Research. The growth of AI in healthcare market is mainly driven by growing demand for precision medicines, effective cost-reduction in the healthcare expenditure, and rising funding in healthcare AI. In addition, factors such as push for digitization in healthcare, growing demand to reduce healthcare costs, and growing number of partnerships and collaborations are driving the global AI in healthcare market. However, physician and provider skepticism, regulatory challenges, and lack of skilled AI workforce may hamper the growth of the AI in healthcare industry.
Digitized healthcare not only simplifies the delivery of healthcare services but also helps in easy and secure management of patient data. It creates new streams of revenue generation for stakeholders. AI and advanced analytics enable healthcare providers to offer personalized medicines and diagnostics by extracting patient-specific information. The increase in digitization of healthcare has also led to medical tourism with the help of telemedicine, precision medicine, and digitized record handling.
Healthcare providers have started using AI widely in intelligent tracking of biometric information and early diagnosis of diseases. AI is enhancing the treatments and increasing the satisfaction of both patients and clinicians. The applications of AI primarily include smart devices, assisted living, fall detection, home health monitoring, and virtual companions such as elderly care robotics. This development is expected to have a positive impact on the overall market growth.

Natural language processing (NLP) technology is estimated to account for the largest share of the overall AI in healthcare market in 2019, mainly attributed to the rising adoption of NLP in clinical documentation and automated coding in claim submission. Currently, the market for NLP technology in healthcare is in a nascent stage, dominated by legacy vendors such as IBM Corporation and Google Inc. focusing on front-end speech recognition for computer-assisted physician documentation and back-end coding to optimize billing.
The AI in healthcare market is into patient data and risk analytics, medical imaging and diagnosis, drug discovery, precision medicine, hospital workflow, patient management, and other applications. Hospital workflow management application segment is estimated to account for the largest share of the overall AI market in healthcare. The large share of this segment can be attributed to increasing implementation of machine learning, deep learning, and other detailed pattern-recognition algorithms that provide clinical-decision support while improving the efficiency of radiologists, pathologists, and other image-based diagnostics. Moreover, rising adoption of the AI solutions in hospitals and clinics to manage the complicated work flow and customer service is also expected to support the growth of this market segment.
North America will continue to dominate the global AI market in healthcare; the Asia-Pacific region is expected to grow at the highest CGR by 2025. The growing technological innovation and demand from China is the primary driver for AI in healthcare market in the APAC region. Moreover, Japan, Korea, India, Taiwan, and Singapore have also announced national AI strategies, focusing on their own niche areas of strength whether it is in software, hardware, or services, which is further expected to support the growth of the AI in healthcare market in the APAC region in the coming years.
The companies that hold the majority share of AI in the global healthcare market are Nvidia Corporation, Intel Corporation, IBM Corporation, Alphabet Inc., and Microsoft Corporation. Other major companies operating in the global AI market in healthcare are AntWorks, Atomwise, Babylon Health, Carre Technologies Inc., Deep Genomics, Enlitic Inc., General Electric Company, General Vision Inc., iCarbonX, Johnson & Johnson Services, Inc., Koninklijke Philips N.V., Medtronic Plc., Next IT Corp., Siemens Healthineers, Butterfly Network, Welltok Inc., and Zebra Medical Vision.

Challenges
AI, in its current iterations, is not a flawless tool for any application, medical or otherwise. As such, ensuring that certain industry-wide standards are put in place for those looking to create AI solutions for healthcare applications, to help overcome such limitations, is among FG-AI4H’s most notable goals for the near future. The Focus Group on Artificial Intelligence for Health (FG-AI4H), which works in partnership with the World Health Organization (WHO), was established by ITU-T Study Group 16 at its meeting in Ljubljana, Slovenia, in July 2018. It aims to establish a standardized assessment framework for the evaluation of AI-based methods for health, diagnosis, triage, or treatment decisions. Thomas Wiegand, a professor at the Technical University of Berlin, the executive director of the Fraunhofer Heinrich Hertz Institute and chairperson of FG-AI4H, addressed this in a video interview when asked about challenges to worldwide AI healthcare implementations: “We are seeing mostly limited projects where you have a given dataset and you develop your algorithm for it, and then you report on what comes out,” Wiegand said. “The problem with that is, how does this generalize to worldwide data that are coming in for this algorithm? We don’t know. And how does a regulator…make a recommendation on a particular algorithm when they don’t know how well it performs in a general sense?” Wiegand went on to explain FG-AI4H’s intent to create global guidelines for approaching health problems using AI – similar to those developed by ISO and other standardization groups – as well as a broadly applicable rubric for assessing AI projects’ performances.

Still, even with all of its promising applications, there are some general concerns with AI as it applies to the medical industry.
Privacy and ethics. First, and foremost, is the concern about patient privacy and ethics. A question on the minds of most is who owns the patient data needed for the AI technology? While Health Insurance Portability and Accountability Act (HIPPA) protects patient health data when it comes from organizations that provide healthcare services, the law does not carry over to the tech companies building and using AI technologies. For example, companies such as 23andMe and Ancenstry.com recently came under fire for selling customer DNA-related data to pharmaceutical companies and universities for research. However, since they are not classified as healthcare providers or entities, they are not required to follow HIPPA laws. On the other side of the coin, ethical and legal questions are raised when thinking about whether hospitals should be allowed to provide patient data to AI companies – how can a patient’s right to privacy be protected?

Fragmented. For AI to work effectively, it relies on high-quality data sources. Yet this remains a tremendous barrier in healthcare today with data being siloed in legacy systems that do not talk to one another. Plus, the privacy concerns about patient data sharing can hinder obtaining large data sets. So, while large amounts of data may be available via electronic health records (EHRs) and health monitoring devices, it needs to be structured, coded and de-identified properly for AI to utilize the information.

Quality data. Another concern is that predictive analysis is just that – a prediction based on inputted data. But the power of predictive analysis is directly correlated to the quality of data feeding it. Because data remains highly fragmented and often is in multiple formats, much of the data is incomplete and inconsistent. Because of this, there has been much concern over AI’s potential to draw the wrong conclusions, particularly since it does not have the capability to weigh the costs and consequences of false positives or negatives the way a physician might. On top of it, electronic health records and billing claims are often written with broad categories and can be difficult to interpret; not to mention many entries include errors or omissions, whether inadvertent on behalf of the doctor, or whether a patient did not share crucial information.
Data labeling. For deep learning to occur, thousands of data records and labeled examples using consistent terms are required for models to perform classification tasks. Right now, most of that information is recorded using inconsistent terms or is very broad. Still, labeling and training the data remains a manual task and also can open the door to bias and errors in the learning algorithm.

With so many potential challenges and current limitations, where does that leave AI in the healthcare equation?
The future
Despite current perceived and actual barriers, AI is dramatically changing the way healthcare is delivered and it has already shown to have a positive impact on certain healthcare outcomes. For these reasons, one can count on seeing AI being used more frequently in these areas:
Predictive maintenance. As more pressure continues to be put on healthcare providers and leaders to achieve greater profitability, implementing predictive maintenance models for proactively repairing and servicing medical technology and devices can reduce unplanned system and operational downtime by anticipating potential failures before they become an issue.
Data security. As security threats continue to rise, AI in tandem with predictive analytics, will become an important tool in monitoring data-access patterns and calculating real-time risk scores to deny or grant access.
Automation. Some EHR platforms are utilizing AI to bring intuition and automation to rote processes that take up a provider’s valuable time. This also can improve the provider experience.
Proactive intervention. Aggregating information across multiple devices and strategically using that data, particularly as consumer smart devices increasingly enter the healthcare fold, has traditionally been a challenge. With AI, it can enhance the ability to identify potential anomalies, health risks, or potential complications and plan intervention before it becomes an issue.
Expanding access to care. Delivering care to remote areas and developing regions remains a primary challenge within healthcare. However, through the use of AI-diagnostic and imaging tools, patients in remote areas can undergo scans for specific diseases, for instance, without the need for a specialist or provider being on site.
AI has already been used to support clinical decision making by offering predictive outcomes based on patterns and trends, as well as in care coordination and planning. This will only continue to expand in healthcare as more data is collected.

Way forward
With a plethora of issues to overcome, driven by well-documented factors like an aging population and growing rates of chronic disease, the need for new innovative solutions in healthcare is clear.
AI-powered solutions have made small steps toward addressing key issues, but still have yet to achieve a meaningful overall impact on the global healthcare industry. If several key challenges can be addressed in the coming years, it could play a leading role in how healthcare systems of the future operate, augmenting clinical resources, and ensuring optimal patient outcomes.