The opportunity for AI in Healthcare

Over the past decades, Artificial Intelligence (AI), has played a robust and growing role in the world. What many people don’t realize is that AI presents itself in several different forms that impact everyday life.

Logging into your email, social media, car ride services, and online shopping platforms all involve AI algorithms to ensure a better user experience. The medical field is one key area where AI is experiencing rapid growth; specifically, in managing treatment and diagnostics.

There is significant research undertaken into how AI can help aid in clinical decisions, increase the efficiency of treatment, and support human judgment.

The Increased Presence of AI in Healthcare

Increasing AI is allows doctors and other healthcare practitioners access to new diagnostic resources. Since physicians are deeply educated in their professional field and are up-to-date with current research, using AI results in a quicker outcome that can be matched with their clinical knowledge.

Artificial Intelligence, however, still presents many fears, particularly in the clinical setting and reducing the need for human physicians. Recent research and data, however, has shown that it is more likely that the tool will benefit and enhance clinical diagnostics and decision making as opposed to reducing the need for clinicians.

Many times, patients may present multiple symptoms that may correlate with different conditions by both physical and genetic characteristics and that may delay a diagnosis. So, AI not only benefits practitioners with regards to efficiency, but also provides qualitative and quantitative data based on input feedback thus improving accuracy in diagnosis, early detection, treatment plan, and predicting outcomes.

AI’s ability to “learn” from the data presents the opportunity for more accuracy based on feedback responses. The feedback includes numerous back-end databases, input from doctors, practitioners, and research institutions. The AI systems used in healthcare work in real time, which results in frequently updated data, which translates to higher accuracy and relevance.?

Assembled data is a compilation of different medical notes, electronic recordings from medical devices, physical examinations, laboratory images, and various demographics. With the compilation of endlessly updating information, practitioners have almost unlimited resources for improving their treatment capabilities.

More Targeted Diagnostics with AI Machine Learning

With huge amounts of healthcare data, AI has to efficiently sort through the data presented to “learn” and build a network. Within the realm of healthcare data, there are 2 different types of data that can be sorted; structured and unstructured.

Structured learning includes 3 different types of techniques, which include Machine Learning (ML) techniques, Modern Deep Learning, and a Neural Network system. All unstructured learning, on the other hand, uses Natural Language Processing (NLP).

Machine Learning (ML) techniques use technical algorithms for pulling out specific patient traits that include any information that’s typically collected in a patient visit with a practitioner. The traits, such as physical exam results, symptoms, medications, disease specific data, basic metrics, gene expressions, diagnostic imaging, and various laboratory tests all contribute to the collected structured data.

Machine learning makes it possible to determine patient outcomes. Neural Networking was utilized in one study in a breast cancer diagnostic process that sorted from 6,567 genes and paired with texture information input from the subjects’ mammograms. The combination of logged physical and genetic characteristics allowed for a more specific tumor indicator outcome.

Supervised learning is the most common type of Machine Learning in a clinical setting. It uses the patient’s physical traits, backed with a database of information (breast cancer genes in this case), to provide a more targeted outcome. Modern Deep Learning is another type of learning used, which is considered to go beyond the surface of Machine Learning.?

The new approach takes the same inputs as Machine Learning, but feeds it into a computerized neural network; a hidden layer that files the information further to a more simplified output. Practitioners that may have multiple possible diagnoses are now able to narrow down to one or two outcomes, which allows them to reach a more concrete and definite conclusion.?

Natural Language Processing is similar to structured data processes. It focuses on all the unstructured data in a clinical setting. This kind of data comes from clinical notes and documented speech to text processing when a practitioner sees a patient. The data includes laboratory reports, narratives from physical examinations, and exam summaries.

The Natural Language Processing makes use of historical databases with disease relevant keywords that aid the decision-making process to make a diagnosis. Using such processes can provide a more efficient and accurate diagnosis for a patient, which in turn saves the practitioner time, and more importantly can speed up the process of treatment. The more targeted, faster, and specific the diagnosis, the sooner the patient will be on the road to recovery.

AI Integrated in Major Disease Areas.

Cancer, neurological, and cardiovascular disorders are consistently the top causes of death and it is important that as many resources as possible be utilized to aid in early detection, diagnosis, and treatment. The implementation of AI provides benefits in early detection through the ability to identify any risk alerts that a patient might have.?

A study involving patients who were at risk of stroke made use of AI algorithms based on the genetic history and presented symptoms of the patients to place them in an early detection stage. This stage was based on movement, where any abnormal physical movement in the patient would be documented and an alert would be triggered.

The trigger alert allowed for practitioners to get patients to a CT/MRI scan sooner for disease evaluation. In the study, early detection alerts provided 88 percent accuracy in diagnosis and prognosis evaluation.

That said, practitioners managed to provide treatment sooner and determine whether the patient were more likely to suffer from a stroke in the future. Machine learning was similarly used in 48-hour post-stroke patients and gained a prediction accuracy of 70 percent whether or not the patient may have another stroke.

Telehealth: AI on a Smaller Scale

AI is primarily used on a larger scale and for high-risk diseases, but telehealth tools are now being used in the homes of patients to help in the treatment and prevention of high-risk situations and reduce hospital readmissions.

The telehealth tools are used for capturing, documenting, and processing various metrics much more like a more expansive Artificial Intelligence machine. Practitioners can be notified by the equipment immediately when patients report high-risk variables.

Early detection, quicker diagnosis, and an updated treatment plan, reduce money and time for the hospital and patient alike, while getting more immediate care. AI is allowing practitioners to make more logical and efficient decisions, providing better care for patients as a whole; which is the ultimate goal, in the end.