The reality of AI in healthcare: promises, roles, evolution, and more

The role of Artificial Intelligence (AI) in various industries has been long discussed. While no one denies its potential to change the face of an industry, how best to leverage it is still up for debate. AI in healthcare is not a new discourse either, and with governments across the globe pushing the cause, the revolution has frankly only started. It has the power to disseminate more fastidious, efficient, and impactful interferences at precisely the right moment in a patient's care journey.

According to a survey, the global healthcare AI market will grow from $4.9 billion in 2020 to $45.2 billion by 2026. From neurology to radiology and risk assessment to chronic diseases such as cancer — more and more avenues are now being explored. It can also help maintain and interpret data, make arbitrations, and even carry discussions. But how much of it is hype, and what's really beneath the surface?

ALSO READ

Here’s how connected health and artificial intelligence is transforming healthcare industry

What makes AI special

Technologies, such as clinical decision support systems and predictive analytics help providers stay ahead of unexpected deterioration and chronic illnesses, as well as risks like antibiotic resistance. BCIs or Brain-computer interfaces can restore cardinal adroitness to those who feared them lost forever.

AI can help refine pathology data, thereby bringing an increase in the efficiency and the value of time a pathologist spends for each case.

Magnetic resonance imaging or MRI scans are considered one of the most expensive singular procedures a hospital can run. However, it is believed that these expenses can be cut down with the help of ML, hence bringing the total charges down and enhancing patient satisfaction.

In fact, an industry report predicted that AI could reduce healthcare costs by as much as 50 percent and improve outcomes by up to 40 percent a couple of years ago. In a survey, 63 percent of professionals agreed that AI would benefit patients with cancer and heart ailments.

Simply put, AI can assist our clinicians, not only in the moment of care but before and after that too.

AI and Healthcare Information Technology (HIT)

HIT is an even mature domain. Real-time mapping of all factions in the life-cycle of a patient's healthcare facility using physical, virtual, and synergy data processes across people, places, systems, and devices, is something that has been helping care providers across the world for some time now.

With ever more reliable techniques for accumulating and aggregating data, such as demographic illness trends and outcomes — optimum designs for healthcare facilities are now being created based on community-focused care models, surgery vs. recovery times, and palliative care trends, among other things.

In monetary terms, AI can potentially generate $18 billion in savings for the healthcare industry by automating administrative tasks.

Headways in HIT offer the prospect of providing personalized care by taking into reckoning granular patient diversities. ML uses images, clinical notes, and other data points for several clinical duties, such as detecting diabetic retinopathy and distinguishing between malignant and nonmalignant skin lesions in dermatoscopic images.

Former research has ascertained that machine learning using clinical notes to augment lab tests and other structured data is more precise than an algorithm using structured data singly to stratify patients with rheumatoid arthritis and prognosticate mortality, and the incipience of critical care arbitrations in intensive care environments.

What are the fears?

AI's ability to distinguish among patients, separating them, sometimes brings with it the uncertainty of augmenting subsisting biases, which can be particularly worrying in sensitive fields like healthcare. Since data sustain machine learning models, predilection can be encoded by modelling preferences or even within the data itself if not done right.

Additionally, this powerful technology gives rise to a neoteric set of ethical hurdles that must be recognized and alleviated since AI in HIT has a formidable potential to endanger patient preference, safety, and privacy.

Some of the most exigent concerns involve addressing the appended risk to patient privacy and confidentiality, parsing out the ambits of the physician's and machine's role in patient care, and modifying future physicians' education to confront the imminent changes in the practice of medicine proactively.

How do we equipoise the pros and cons of AI in HIT? There is an advantage in speedily mainstreaming AI technology into the healthcare system, as AI raises the opportunity to enhance current care delivery models' efficacy and quality.

However, there is a need to mitigate ethical hazards of AI implementation in HIT, including threats to privacy and confidentiality, apprised acquiescence, and patient autonomy – and to consider how we can integrate AI in clinical practice.

ALSO READ

Shot in the arm: 6 use cases of AI that will disrupt healthcare space

Understanding the viewpoint of the critics

AI, which includes natural language processing, ML, and robotics, can be implemented in almost any domain of medicine. This also incorporates its latent contributions to biomedical research, medical education, and healthcare delivery.

A concept, which theoretically seems a must-have is still looked down upon by a section of professionals. Critics often question the very relevance of AI in healthcare. Fewer still believe that it is only a question of time before physicians are rendered obsolete by this technology type.

Let's get to the core of the entire deliberation and ask: Should AI, which supposedly has a better success rate than manual work, be used to supplant or augment people in critical healthcare decisions over traditional methods?

A closer inspection of this technology's role in healthcare delivery is warranted to bring forth its current strengths, limitations, and ethical complexities. Ease of use, familiarity with legacy processes, over-simplification of medical complexities at the time of data visualisation, among others, have been traditionally cited as an argument against the use of emerging technologies.

The last thing HIT champions should do is to neglect their concerns and address them with complete honesty.

The Solution Is

The solution can be brought down to one single logic: A technology ecosystem that lets care providers do what they love doing the most, which is providing care, should be at the core of all developments. Stakeholders should become flexible in consolidating AI technology but ensure that it stays as a complementary accessory and not a surrogate for a care provider.

AI in HIT will, undoubtedly, have extensive consequences that revolutionise the practice of medicine, remodelling the patient experience and physicians' daily routines. Nevertheless, there is much to do before laying down the precise ethical framework for using AI securely and efficiently in healthcare as supplemental appurtenances.

Ultimately, physicians will still treat patients, regardless of how much AI changes the delivery of care: there should and will always be a human element in the practice of medicine. No matter how high the confidence rating for the diagnosis or therapy recommended by an AI program may be, humans and their reactions to treatment are infinitely variable at the individual level.

The conversations about AI replacing the human essence in healthcare are groundless, and no technology in the near future would ever have the potential to do this.