Introduction

Artificial intelligence (AI) has established innovative, problem-solving processes across diverse industries. 

Within healthcare, AI systems have revolutionized diagnostic, preventive, and treatment measures making this digital design one of the most important introductions to the healthcare field.

This impact is reflected around the world. 

In 2021, the global AI in healthcare market was valued at $11 billion. This figure is projected to rise continuously, with 2030 predictions placing the market at $188 billion.

This development of AI interventions has arrived at an ideal time in medicine. The healthcare ecosystem is currently grappling with challenges ranging from high costs and over-burdened pesonnel, to inadequate medical access, and increasing care demands from a growing population. 

Through machine learning, natural language processing, rule-based expert systems, robotics, and other forms of AI—this technology currently plays a supportive, monitoring, and substitutive role in healthcare delivery. 

This whitepaper will highlight the groundbreaking services, and improvements introduced through the adoption of AI.

What is AI?

Broken down to the bare bones, artificial intelligence refers to machinery’s ability to reproduce human intellect and abilities. 

As the subject of constant and rapid advancements, AI is quickly outpacing human efforts to create a working category all of its own.

To Stuart Russell and Peter Norvig, authors of Artificial Intelligence: A Modern Approach, AI is: “the study of agents that receive precepts from the environment and perform actions.” 

This technology can think, learn, and take action by observing past operations.

Within the medical space, AI uses data, machine learning algorithms, and related technologies within clinical settings. 

As a next-generation addition to the world of medicine, AI systems are gaining ground across the mundane and complex aspects of healthcare. 

AI application in healthcare

AI-powered tools are mapping out new terrain in medical diagnosis, treatment, administrative function, plus more. 

Diverse healthcare fields have recognized this impact. This has led to raising AI priority across operations. 

In a 2021 report by Sage Growth Partners, around 90% of hospitals in the United States have adopted a personalized AI strategy. 

The pharmaceutical industry—popularly slow to adopt technologies—is also experiencing an AI awakening. 

Top players like Pfizer, Sanofi, GSK, and Astrazeneca are adopting AI in key areas such as drug discovery, symptom identification, and inventory management. 

In 2021, the pharmaceutical AI market was valued at $905.91 billion. By  2030, this market is projected to experience an annual compound rate of 29.4%. 

Within hospitals and across healthcare, AI’s performance is helping to augment and relieve person-led operations. 

Many wings of patient care, administrative function, plus payer-provider interactions have been enhanced using this technology. AI is transforming the healthcare industry in the following ways:

Medical imaging

Artificial intelligence is perhaps the most discussed topic in the field of medical imaging research. 

Between 2017-2018 alone, around 1000-1100 publications were written on the subject. 

The American College of Radiology Data Science Institute (ACR DSI) recognizes many use cases of AI in medical imaging. According to Bibb Allen Jr., MD, FACR, ACR, Chief Medical Officer at DSI,: “The ACR DSI use cases present a pathway to help AI developers solve health care problems in a comprehensive way that turns concepts for AI solutions into safe and effective tools to help radiologists provide better care for our patients,” 

Before AI’s introduction, clinicians navigated the world of medical imaging using data derived from projection, tomographic, and more recently, digital images.

As this field evolved into a more complex and data-heavy area, these experts have required more time to execute functions in clinical and laboratory contexts.

Deep learning and other AI systems are improving needed focus on the main areas of radiology. By analyzing previous reviewed cases, these algorithms can recognize patterns that improve diagnostic, predictive, and treatment capacities.

AI supports many clinicians in detection, quantification, and precision processes in healthcare delivery. These features also take over the mundane tasks of radiology. 

AI’s introduction has helped radiologists identify conditions more quickly and accurately. These technologies have proved essential for early intervention in treatment. 

Administrative functions

Data in the healthcare sector is valuable but arduous to obtain. 

Physicians already juggle roles within hospital settings that manage disease prevention, diagnosis, and treatment services. 

Added to these tasks are administrative roles like filling electronic health records, specially reserved for the physician owing to his knowledge of the quality of service, timeliness, plus the effectiveness of the treatment offered. 

Physicians also manage insurance issues and are tasked with delivering test results to patients. 

These administrative duties cut into valuable time which may be channeled to delivering care. 

A 2014 report noted that doctors spent one-sixth of their working hours on simply adminstrative function.

With AI, some of these tasks are automated to spare clinicians the time. Tools like voice recognition, dictation technology, plus other AI devices ease and improve productivity in healthcare. 

AI-powered voice assistants such as Aicure, Alexa, Cortana, and Google home are transforming the landscape of medication reminders and refill notifications. 

Patient monitoring

Monitoring is a core component of managing and treating conditions like diabetes, musculoskeletal conditions, and heart disease. 

Speaking on the potential of this system, Robin Farmanfarmaian, Silicon Valley AI entrepreneur shares: “Remote patient monitoring is still in the first five years of adoption and integration into the healthcare system”

In the very recent past, these examinations were carried out in-person, by a qualified healthcare professional. 

AI is revolutionizing this landscape through remote patient monitoring technology. Through sensors connected to a patient, AI technology can track, record, and transmit data on patient health to hospitals or a health specialist.

Farmanfarmaian adds: “In 10 years, remote patient monitoring will be mainstream, and likely reimbursed by all the major payers. We’re already seeing that RPM has the ability to catch hospital readmissions days before they happen. The healthcare industry is experiencing a revolution in vital-sign measurement devices, with many companies innovating on ways to collect vital signs.”

Valued at $25.32 billion in 2020, remote patient monitoring is exploding to become an integral part of the healthcare experience. 

Companies like AICure adopt AI to measure changes in facial movements for treatment progress. Others include Eko which applies AI to analyze ECG readings. This technology contains an algorithm trained on multiple files of heart sounds.

Robotic surgeries

Robotics have enhanced surgery and medical procedures for around 40 years. These machines introduce an added element of precision into human-led operations. Following decades of use, artificial intelligence is ushering the next stage of robot-assisted procedures. 

Through deep machine learning, AI integrates computing accuracy with robotic systems. 

AI-based surgical robots are applied in different areas of orthopedics, urology, gynecology, and other fields. 

This intelligent technology supports surgeons with instrument manipulation and positioning during procedures. 

Through machine learning systems, AI-driven robots learn from historic data to correctly measure and identify relevant information of the internal body such as tissue size during operations.

Speaking on the AI teaching and learning process for endoscopic procedures, Pól Mac Aonghusa PHD, Senior Research Manager, IBM Research, Europe says: “How can we model video data coming from the camera and transform it into something precise and explainable—a model.” 

This framework provides a knowledge reservoir for AI to reach for during procedures.

AI’s effectiveness has produced many success stories during operations. 

These achievements have raised questions about potential human replacement during surgical interventions. 

In reality, AI replicates patterns and procedures learned from surgeons. This relationship is collaborative, with surgeons and algorithms learning from the other.

Tom Lawry, National Director of AI for Health & Life Sciences at Microsoft shares:

“What artificial intelligence is good at is things like pattern recognition,” “It’s great at sifting through massive amounts of data to find something that humans either aren’t capable of finding or would take years humans to find. On the other hand, humans are great at wisdom, common sense, empathy, and creativity, all of which are vitally important when you think about the care process.”

Chatbots

Chatbots are a mainstream example of AI’s efforts at boosting healthcare accessibility. 

Through machine learning, AI-assisted chatbots answer patients’ most pressing issues on symptoms, medication use, or even physician availability.

John Hostetler, VP of Sales at NeuraFlash breaks down the workings and reasoning behind this feature: “Customers and patients have an expectation of immediate responses through whatever channel…across all industries. Chatbot is taking data from digital channels—chat,SMS, Whatsapp, wechat, Facebook…Applying AI to that data and provides automation through the conversation.”

Already in use, this healthcare feature boomed in the wake of the pandemic. The global chatbox market size was estimated at $184.60 million in 2021. This service is forecast to reach a value of $431.47 million by 2028. 

Chatbots make use of programmed scripts plus machine learning algorithms to provide conversational, prescriptive, and informative services.

Future potential for AI in Healthcare

Healthcare systems of the future are projected to provide decentralized care, accessible from any part of the world. 

These networks will provide equitable, personalized, and more precise health management for patients and providers alike with AI leading the charge to achieve this.

According to the World Economic Forum, AI is poised to provide an enhanced landscape across different health systems come 2030. 

Through its unique ability to sift through and learn from copious amounts of data, AI and predictive analytics can identify and transmit patterns about our environment, pollution, and other factors that may increase disease risk.

In AI-backed settings, healthcare delivery will leverage connected care to minimize the need for physical locations. 

The World Economic Forum predicts that hospitals will be reserved for complex procedures, or the severely ill. 

Instead, smaller centers like retail clinics and same-day surgery centers powered by AI’s clinical and location data networks will become commonplace. 

These systems will run more efficiently with algorithms that help to minimize administrative, personnel, and treatment bottlenecks in healthcare delivery.