AI Transforming Healthcare
Siddhartha Mishra
Global Healthcare Operations & IT Leader | Digital Transformation Strategist | CIO/Program Director | Driving Innovation in Australian Healthcare | Healthcare Ecosystem Enabler |
How Artificial Intelligence Is Transforming Healthcare
The rapid commercialization of Artificial Intelligence and machine learning has contributed to its widespread adoption across industries. Healthcare too has been wooed by the charm of AI and had adopted it to solve a host of problems for patients and the entire healthcare ecosystem.
According to a study by Frost & Sullivan, the AI market for healthcare is expected to reach $6.6 billion by 2021 from USD 7,988.8 million in 2016, reporting an almost 40% growth rate.
Growing digital capacities and a focus on Health IT to improve patient outcomes and increase efficiencies of the healthcare ecosystem, growing healthcare data and increasing adoption of precision medicine can be considered some of the reasons fuelling this growth. A recent report from Accenture further goes on to say that AI is rewiring the modern conception of healthcare delivery with a vast array of AI application geared to save the healthcare industry over $150 billion in the course of the next decade.
Artificial Intelligence is the science of creating computing systems that can complete tasks independently of human intervention. The algorithms used in AI systems have vast amounts of data and self-learning capabilities to provide a solution or a response to a particular question.
In this blog, let’s take a look at how AI is transforming healthcare
Improved Medical Decision Making
AI can be used in hospitals to improve the quality of medical diagnostics. AI systems have the capability to scan through humongous volumes of documents within a span of moments. Since these systems have analytical and reasoning capabilities, these systems can access the most recent and cutting edge medical information from anywhere in the world and provide these results to the doctors to help research and diagnosis. These capabilities can be very useful especially in areas where test and lab results need to be processed faster. For example, the radiologists in Shanghai Changzheng Hospital are utilizing AI technology to read CT scans and x-rays to identify suspicious nodules and lesions in lung cancer patients. The AI system learns the core characteristics of lung cancer and identifies cancer features through CT sequencing. This leads to faster diagnosis and helps the doctors start the treatment process faster.
Effective Data Management
With the rise of internet-enabled communications, we are drowning in a sea of data. The healthcare industry too is becoming data reliant to acquire new information, knowledge, and insights. With the volume of biomedical data approaching a staggering 100–250 exabytes, with an annual growth rate of 1.2 to 2.4 exabytes, AI systems are being effectively used to mine, gather, and store these continuously growing patient data volumes to provide intelligent insights. Owing to its technological capabilities, AI systems can be used to not only conduct basic data searches but can also be utilized to analyze large volumes of raw data and aid medical research and development.
Improved Hospital Management
Artificial Intelligence can be used in healthcare to increase the efficiency of the entire healthcare network. These systems have the capabilities to manage the entire healthcare ecosystem in real-time and, hence, can be extremely useful in identifying any inefficiency or issues in the healthcare network faster. With the help of these insights, hospital management can become more efficient since process lags, inefficiencies of different departments, incorrect distribution of pharmaceuticals, billing inconsistencies, and inefficient staff utilization can be highlighted proactively. AI can also be used to identify when patients are likely to take a turn for the worse and thereby assist in decreasing mortality rates and patient readmissions.
Optimize Doctor-Patient Interactions
The World Health Organization estimates that over 400 million people across the globe do not have access to the most basic medical facilities. As populations continue to grow as does the worldwide shortage of clinical staff, mobile technology coupled with AI can solve the problem. IBM’s Watson supercomputer has already established its credibility by diagnosing the precise condition affecting a leukemia patient in Japan by cross-referencing the patient's information with 20 million oncology records within a matter of minutes. AI systems, because of their computing power aide the advancement of preventive medicine.
Along with this, AI systems can also help in optimizing doctors’ schedules by ensuring the most urgent messages reach them faster. Doctors can use AI apps for post operative care such as provide medication alerts, assess a patient’s mental state and assess if human medical intervention is required to treat an ailment.
Online Consultations
Accenture estimates that virtual nursing assistants will help in saving over $20 billion to the healthcare industry. AI powered voice-driven applications will help patients answer questions regarding their health and assist in health management. These applications can help in chronic disease management and long-term maintenance, assess patients’ conditions, offer education and insights regarding their health and also assess them for risk. If needed, these AI applications can also let the patients know when they need to go to the hospital and answer all patients’ questions. All such things can help in reducing patient-physician interaction times and reduce healthcare costs for patients.
Robotic Assisted Surgery
AI is also entering the operating room! Many hospitals are leveraging robotic assisted surgery where automation is replacing repetitive tasks and helping in reducing the surgery time. The robot used in these surgeries act as the doctor's assistants and help in improving patient safety by taking over the tedious and more routine parts of the operation. Cognitive robots can take pre-operative medical information and integrate it with real-time operating metrics and increase the physician's instrument precision. In one such operation, it was noticed that an autonomous robot could sew more evenly and consistently than the most experienced surgeon. Much like autonomous cars, the use of AI in the operating room is also increasing incrementally, however with human intervention.
AI is quite contrary to the images of rogue machines and terminators that come to mind when we speak of this technology. Today, Artificial Intelligence is slowly becoming the core of every application. As AI applications get more experience in healthcare, their ability to self-improve will lead to greater precision, increased hospital efficiency and improved patient outcomes.
MD at Central Maine Medical Center
7 年AI would seem much better applied in health care administration, billing, insurance, and patient services. It's all about plastic cards anyway. Outpatient check in, parking, and future appointments can all be handled on EZ PASS. Huge growth in health care costs has virtually all been in administration. The growth chart of administrative personnel has been on steroids, 45 degree angle compared to physician numbers which have be nearly flat. I'm just waiting for mandatory subcutaneous chips! EZ pass could notify Hospital AI of a no-show before it occurs. (actually that's possible now with cell phone AI) Outpatient vaccinations packets would arrive daily from AMAZON and be loaded into the AI VACCINATORS allowing for the robotic administration of the proper vaccine into the child with the proper butt chip, which was injected was placed at birth by same robot who feeds the pregnant mother her Soylent Green gestational supplement.
Consultant Cardiac Surgeon Barts Heart Centre, Director Barts Life Science’s, Professor of Cardiovascular Surgery, WHRI,QMUL
7 年Undoubtedly a disruptive force which is going to change how we deliver and think about health.
Specialist in Internal Medicine at Dar Al Shifa Hospital & Clinic
7 年A hybrid system of care with physical consultation and examination followed by virtual follow up and remote monitoring have helped my patients achieve goals in chronic conditions like diabetes earlier than the traditional system of clinic consultations alone.
Co-Founder, Director, and CMO at Africa Healthcare Network
7 年AI systems requires many months of laborious training, as experts must feed vast quantities of well-organized data into the system for it to be able to draw any useful clinical conclusion. And decisions can only be drawn based upon the body of data, that it has been trained on. If trained on a corpus of nephrology, the system cannot deal with even the basic insights of cardiology. Medicine again is a rapidly changing complex domain, further the technology use in medicine is even more dynamic which is interpreted based on changing evidence. New data set aquisions take inordinate time, costs and expertise. Inadequate and unprepared data sets to train leads to erronous calls and classification. Yes machine learning algorithms continue to improve, but AI as of now, cannot replace the doctor. The mature capabilities of the human for perceiving, reasoning, or explaining when it comes to patient care has no matching machine learning algorithm as yet. State of the art machine learning algorithms often cannot deliver the positive predictive value needed for clinical decision making. e.g. IBM’s Watson has been a total failure. Even after 4 years, IBM could not reach a pilot for clinical use at the MD Anderson Cancer Center and the contract was called off. Again I must add that a lot of the technology stalwarts from outside medicine have no clue as how clinical decision making occurs. These are people linked to big funding and formidable marketing which drives big bold corporate notions which continue to be artificial as of now.