Artificial intelligence (AI) in healthcare is the use of intricate algorithms and software to imitate human cognition in the examination of complex medical data. Specifically, AI is the capability of computer algorithms to approximate inferences without direct human input.

AI could be basically defined as computers and computer software that are capable of intelligent behaviour, such as study and learning. It is an expansive category at the cutting edge of technological expansion, growing and changing every day.

History

Research in the 1960s and 1970s fashioned the first problem-solving program, or expert system, branded as Dendral. While it was intended for applications in organic chemistry, it provided the foundation for a subsequent system MYCIN, considered one of the most noteworthy early uses of artificial intelligence in medicine. MYCIN and other systems such as INTERNIST-1 and CASNET did not accomplish routine use by practitioners, however.

The 1980s and 1990s brought the propagation of the microcomputer and new levels of network connectivity. During this time, there was an acknowledgment by researchers and developers that AI systems in healthcare must be planned to compensate for the absence of perfect data and build on the proficiency of physicians. Methods involving fuzzy set theory, Bayesian networks, and artificial neural networks, have been used to intelligent computing systems in healthcare.

Machine learning and neural networks

Machine learning is the foundation of modern AI and is fundamentally an algorithm that lets computers to absorb independently without following any explicit programming. As machine learning algorithms encounter additional data, the algorithms’ performance develops.

Deep learning is a subsection of machine learning that works in a similar way with a slight change. Deep learning goes a stage further, making readings based on the data it has encountered before. In other words, deep learning allows an AI application to draw its own assumptions. It works through an artificial neural network, which is a set of machine learning algorithms that work in unison. A neural network somewhat bears a resemblance to the human brain, with a series of “neurons” that “fire” when certain stimuli (in this case, data) are available.

Machine learning works efficiently in detecting something anticipated, but it fails when challenged by the unexpected. To take artificial intelligence to the next level, developers must underline both deductive and inductive reasoning, and imitate those cognitive patterns in the machines they design. A benefit of dynamic, deep learning solutions, is that they can clarify their reasoning and conclusions, a major advantage for complex decision-making.

Use of Artificial Intelligence in Healthcare

AI is still a comparatively new technology, especially in the healthcare industry where acceptance remains in its initial stages. As AI and machine learning tools become more refined, their use cases have stretched; however, implementation of AI remains low.

Modern AI applications comprise wide-ranging use cases, from cybersecurity to radiographic imaging, etc. As AI applications continue to advance, the entire healthcare industry could undergo an alteration. Here are some of the ways AI is anticipated to shape healthcare in the coming years.

Diagnostics

AI excels at sorting data, especially once it has been fed large amounts of data on the subject. That creates great potential for AI when it comes to diagnostics – medical imaging analysis and patient medical records, genetics, and more can all be pooled to improve diagnostic results. Moreover, AI tools can use related information to craft distinctive treatment approaches and offer references to doctors.

Diagnosis and treatment of disease has been an emphasis of AI since at least the 1970s, when MYCIN was developed at Stanford for detecting blood-borne bacterial infections. This and other early rule-based systems displayed potential for accurately diagnosing and treating disease, but were not approved for clinical practice. They were not significantly better than human diagnosticians, and they were poorly incorporated with clinician workflows and medical record systems.

Clinical prescriptive analytics is perhaps the closest AI is getting to support direct patient care in 2019.

Robot-assisted surgery

Robotic surgeries allow surgeons to use small tools and make more accurate incisions. Surgeons (and patients) could also profit from AI by linking medical records with real-time data for the duration of operations, as well as drawing on data from preceding successful surgeries of the same type. Accenture, a technology consulting firm, approximates that AI-enabled, robot-assisted surgery could save the U.S. healthcare industry $40 billion annually by 2026.

Virtual nursing assistants

Think of virtual nursing assistants like an Alexa for your hospital bedside. These virtual assistants imitate the characteristic behaviour of a nurse by supporting patients with their daily routines, prompting them to take medications or go to appointments, helping answer medical questions and more. Accenture approximates that virtual nursing assistants could be the second-largest source of annual savings for the U.S. healthcare commerce, cutting as much as $20 billion in costs.

Administrative workflow assistance

Logically, medical practices, hospitals and other points of care result in a great deal of paperwork. In fact, it was combining and digitizing these records that led to the industry-wide implementation of electronic health records systems. AI has already started to make its way into these systems and can be used to reorganize administrative functions as well. Accenture estimates that new efficacies in administrative workflow due to emerging AI technologies could result in $18 billion in annual savings.

Another AI technology with significance to claims and payment administration is machine learning, which can be used for probabilistic matching of data across diverse databases. Insurers have a responsibility to confirm whether the millions of claims are correct. Dependably identifying, analysing and correcting coding issues and incorrect claims saves all stakeholders – health insurers, governments and providers alike – a great deal of time, money and effort. Incorrect claims that slip through the cracks constitute substantial financial potential waiting to be unlocked through data-matching and claims audits.

Patient Engagement

At present, automatic scheduling and appointment reminders are conventional, but the appearance of patient engagement could soon become more robotic (and yet, at the same time, more private.)

Imagine a cancer patient going through radiation therapy [who] is unfamiliar with what a normal side effect is and what isn’t. Now, instead of being distressed through the night until the doctor’s office opens, the chat-bot can inform them immediately.

Providers and hospitals often use their medical knowledge to develop a plan of care that they know will improve a chronic or acute patient’s health. However, that often doesn’t matter if the patient fails to make the behavioural adjustment obligatory, e.g. losing weight, planning a follow-up visit, filling prescriptions or complying with a treatment plan. Noncompliance – when a patient does not follow a course of treatment or take the prescribed drugs as suggested – is a major problem.

Economics of Artificial Intelligence in Healthcare

How much is all this worth? Accenture guesses the top 10 AI applications in healthcare could save the industry $150 billion yearly by 2026. The AI healthcare market itself is expected to be worth $6.6 billion by 2021, which signifies an enormous compound annual growth rate of 40% since 2014 but also a modest investment when equated with the predicted savings directly related to the implementation of AI.

Ethical implications

Finally, there are also a selection of ethical associations around the use of AI in healthcare. Healthcare decisions have been made almost completely by humans in the past, and the use of smart machines to make or assist with them raises issues of responsibility, transparency, consent and confidentiality. Perhaps the most challenging issue to address given today’s technologies is transparency. Many AI algorithms – particularly deep learning algorithms used for image analysis – are virtually impossible to understand or explain. If a patient is informed that an image has led to a diagnosis of cancer, he or she will likely want to know why. Deep learning algorithms, and even physicians who are generally familiar with their operation, may be unable to provide a clarification. Errors will undoubtedly be made by AI systems in patient diagnosis and treatment and it may be difficult to find accountability for them. There are also likely to be events in which patients receive medical information from AI systems that they would desire to receive from an empathetic clinician. Machine learning systems in healthcare may also be matter of algorithmic bias, perhaps forecasting greater likelihood of disease on the basis of gender or race when those are not actually causative factors. We are likely to encounter many ethical, clinical, occupational and technical changes with AI in healthcare. It is vital that healthcare institutions, as well as governmental and regulatory bodies, establish structures to oversee key issues, react in an accountable manner and form governance mechanisms to limit negative implications. This is one of the more influential and consequential technologies to impact human societies, so it will require constant attention and considerate policy for many years.

The future of AI in healthcare

We believe that AI has an important role to play in the healthcare events of the future. In the form of machine learning, it is the primary ability behind the development of precision medicine, widely agreed to be a sorely needed advancement in care. Although early efforts at providing diagnosis and treatment recommendations have proven perplexing, we expect that AI will ultimately dominate that area as well. Given the swift advances in AI for imaging analysis, it seems likely that most radiology and pathology images will be inspected at some point by a machine. Speech and text recognition are already engaged for tasks like patient communication and capture of clinical notes, and their usage will rise.

The greatest trial to AI in these healthcare fields is not whether the technologies will be proficient enough to be useful, but rather certifying their adoption in daily clinical exercise. For common adoption to take place, AI systems must be sanctioned by regulators, incorporated with EHR systems, standardised to an appropriate degree that similar products work in a similar fashion, taught to clinicians, paid for by public or private payer organisations and modernized over time in the field. These challenges will eventually be overcome, but they will take much longer to do so than it will take for the technologies themselves to be established. As a result, we anticipate to see limited use of AI in clinical practice within 5 years and more widespread use within 10 years.

It also seems increasingly clear that AI systems will not substitute human clinicians on a large scale, but rather will supplement their efforts to care of patients. Over time, human clinicians may move toward jobs and designs that draw on exclusive human abilities like compassion, persuasion and big-picture assimilation. Perhaps the only healthcare providers who will lose their jobs over time may be those who refuse to work in tandem with artificial intelligence.