August 24, 2017

The Impact of Artificial Intelligence in Healthcare


According to PricewaterhouseCoopers, "chronic diseases and conditions are on the rise worldwide." When infectious diseases like SARS and Ebola emerged, there was a rapid, global spread. Given the significant increase in global mobility, outbreaks must be dealt with quickly to minimize the number of people who may be infected.


Although there have been significant advances in the control of common communicable diseases, presently, some of the most common infections like tuberculosis, malaria, and HIV still do not have effective vaccines.


On a positive note, the past few years has also seen major progress in the diagnosis, management and prevention of certain cancers like cervix and breast cancer and childhood leukemia.


The World Health Organization reports that by 2020, the prevalence of chronic disease is expected to rise 57%. However, advancements in detecting and diagnosing diseases will help to minimize the cost of treating chronic diseases. Some of these new technologies include genomics, proteomics, cell biology, stem cell and organ therapy, and minimally invasive and robotic surgery.


In the past 10 years, medical advances and breakthroughs have included new technologies including:



According to VentureBeat, 55 out of 218 health care AI startups selected from an industry database were involved in predictive medicine.


According to a 2016 study by Frost & Sullivan, the market for AI in healthcare is projected to reach $6.6 billion by 2021. This is not surprising as the collection of multiple AI technologies continues to grow. AI is definitely part of the future of healthcare, and it will evolve in a way that will help doctors, not replace them.


The Emergence of AI & its Significance

The term "artificial intelligence" was coined at a conference at Dartmouth College in 1956. Until 1974, AI consisted of work that included reasoning for solving problems in geometry and algebra and communicating in natural language.


Between 1980 and 1987, there was a rise in expert systems that answered questions or solved problems about specific knowledge. Interest in AI declined until IBM's Deep Blue, a chess-playing computer, defeated Russian grandmaster Garry Kasparov in 1997. Since then, other AI achievements have come to include handwriting recognition, testing for autonomous vehicles, the first domestic or pet robot, and humanoid robots.


In February 2011, IBM's Watson, defeated the two greatest Jeopardy! Champions in an exhibitio­n match. Last year­­­, DeepMind's (Google)AlphaGo, an AI computer program, beat a human professional player in a game of Go. Today, big data, faster computers and advanced machine learning all play a role in the development of artificial intelligence.


AI has many applications in a myriad of industries, including finance, transportation and healthcare - which will change how the industry diagnoses and treats illnesses. AI has been applied to object, face, speech and handwriting recognition; virtual reality and image processing; natural language processing, chatbots and translation; email spam filtering, robotics and data mining. According to market intelligence firm, Tractica, the annual worldwide AI revenue will grow to $36.8 billion by 2025.

Role of AI in Healthcare

  • Virtual Health Assistant

Virtual Health Assistants (VHA) can proactively help patients in a number of ways. For one, VHA's can help dementia patients stay on track with their prescribed medications by sending reminders. Moreover, virtual health assistants may give advice on treatments for common medical conditions or provide recipes for patients with specific diet restrictions. VHAs can also monitor patients based on data, allow doctors to engage with patients and pharmacies to remind patients of prescription refills and pickups, and even recommend preventive health screenings.

  • Diagnosis

AI has been utilized to improve medical diagnosis. For example, AI aided medical image diagnosis from Beijing-based artificial intelligence high-tech company, Infervison, is being used to improve reading CT scans and x-rays. The technology, which is used in hospitals in China, can detect suspicious lesions and nodules in lung cancer patients. This allows doctors to provide patients with an early diagnosis as opposed to sending tissue samples to a lab for analysis, thereby providing treatments earlier than usual.


Researchers at Stanford University have created an AI algorithm that can identify and diagnose skin cancer. This technology, using images of moles, rashes, and lesions, may someday be available as a mobile app on smartphones.


Google's parent company, Alphabet, is working on an AI program to detect metastasis using high-level image recognition. The program will be able to do this faster than the conventional way, which again translates to earlier diagnosis and treatment.


Furthermore, because AI can analyze large volumes of data it enables the detection of disease and helps with clinical decisions.

  • Healthcare BOTs

Bots for healthcare exist primarily for patient engagement. Healthcare bots, which are found in mobile messaging apps, can help patients quickly and in real time simply by sending a message. Health chatbots can answer health-related questions and even help patients manage medications by providing information on types of medications and recommended doses.


Some advancements that have been made in healthcare bots include the ability to:



Other artificial intelligence solutions being developed in the healthcare field include:


Benefits of AI in Healthcare

  • Advancement in treatments

AI is leading to advancements in healthcare treatments, such as improving the organization of treatment plans, analyzing data to provide better treatment plans, and monitoring treatments.


AI has the ability to quickly and more accurately identify signs of disease in medical images, like MRI, CT scans, ultrasound and x-rays, and therefore allows faster diagnostics reducing the time patients wait for a diagnosis from weeks to mere hours and accelerating the introduction of treatment options.

  • Virtual Assistants

In this day and age when people expect to get answers instantly, virtual assistants enable patients to get answers in real time. Patients can ask medical questions and receive answers, get more information and reminders about taking medications, report information to physicians, and gain other medical support. Physicians can also take advantage of healthcare virtual assistants by tracking and following through with orders and making sure they are ordering the correct medication for patients.


  • Reduce Costs

Frost & Sullivan reports that AI has the potential to improve outcomes by 30- 40% and reduce the cost of treatment by as much as 50%. Improvements in precision and efficiency means fewer human errors, leading to a decrease in doctor visits. Doctors are also able to get information from data for patients who are at risk of certain diseases to prevent hospital re-admissions.


On a larger scale, according to Healthcare IT news, potential cost savings in AI applications in billions of dollars are:




According to Accenture, key clinical health AI applications can generate $150 billion in savings annually for the healthcare economy in the United States by 2026.

  • Treatment Plans

Another benefit of AI in healthcare is the ability to design treatment plans. Doctors can now search a database, such as Modernizing Medicine, a medical assistant used to collect patient information, record diagnoses, order tests and prescriptions and prepare billing information. Moreover, the ability to search public databases with information from thousands of doctors and patient cases can help physicians administer better personalized treatments or find comparable cases.




AI Risks in Healthcare

  • Accuracy and Safety

Since AI is fairly new, it has the potential to be less accurate and reliable thereby putting patients at risk. The BBC article, The Real Risk of Artificial Intelligence addresses this:



"Take a system trained to learn which patients with pneumonia had a higher risk of death, so that they might be admitted to hospital. It inadvertently classified patients with asthma as being at lower risk. This was because in normal situations, people with pneumonia and a history of asthma go straight to intensive care and therefore get the kind of treatment that significantly reduces their risk of dying. The machine learning took this to mean that asthma + pneumonia = lower risk of death."


Furthermore, AI has to be reliable enough to keep sensitive data, like addresses and financial and health information secure. Institutions that handle sensitive medical information need to make sure their sharing policies keep information safe.


  • Risk in new/exceptional health cases

Not only does AI have to be accurate and safe, it has to be created so it is up to date with new health cases. In other words, a program will only be as good as the data it learns. Programs need to be trained, or at least constantly updated, to be able to identify new/exceptional health cases.


  • Risk for Doctors & Patients

AI can also pose a risk for doctors and patients. Since AI has not been perfected, doctors cannot fully rely on AI and still need to make decisions based on their knowledge and expertise. Patients are also at risk for the same reason. If a program provides incorrect information, patients will not be treated properly.

Challenges for AI in Healthcare

  • Adoption

One of the challenges AI faces in healthcare is widespread clinical adoption. To realize the value of AI, the healthcare industry needs to create a workforce that is knowledgeable about AI so they are comfortable using AI technologies thereby enabling the AI technologies to "learn" and grow smarter.

  • Training Doctors/Patients

Another challenge is training doctors and patients to use AI. Learning how to use technology may be a challenge for some. Likewise, not everyone is open to information given by a "robot." In other words, accepting AI technology is a challenge that needs to be addressed through education.

  • Regulations

Complying with regulations is also a challenge for AI in the healthcare industry. For one, there is the need for approvals from FDA before an AI device or application is applied to health care. This is especially true because AI is at a nascent stage and not a technology that is fully known or understood. Moreover, the existing approval process deals more with AI hardware and not about data. Therefore, data from AI poses a new regulatory challenge for FDA needs to be validated more thoroughly.

How healthcare will grow in Future with AI?


AI is gaining traction in many fields. AI has the possibility to have a huge and positive impact for doctors and patients in healthcare. Because of the ability to aggregate and analyze a massive amount of varied data, AI could yield significantly faster and more accurate diagnoses for a broader segment of the population. Individuals without access to highly specialized healthcare could gain the benefit of that experience through AI. Healthcare costs could potentially drop due to earlier and more accurate diagnoses. That said, AI also poses risks for the medical profession and patients. Until the data repository gets large enough and extremely well validated, doctors will have to continue to use their training and experience to assure that artificial intelligence is yielding the proper diagnoses and course of medical treatment. That said, we're not expecting to see a robot in our doctor's office for quite some time.

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This post was written by Asokan Ashok, the CEO of UnfoldLabs. Ashok is an expert in driving customer insights into thriving businesses and commercializing products for scale. As a leading strategist in the technology industry, he is great at recommending strategies to address technology & market trends. Highly analytical and an industry visionary, Ashok is a sought after global high-tech industry thought leader and trusted strategic advisor by companies.

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