How Artificial Intelligence Can Ensure Success for Healthcare

AI used in healthcare can serve clinicians, patients, and other healthcare workers in four different ways. The likely success factors depend largely on the satisfaction of the end users and the results that the AI-based systems produce.

1. Assessment of condition

Prediction and assessment of a condition is something that individuals will demand to have more control over in the coming years. This increase in demand is partly due to a technology reliable population that has grown to learn that technological innovation will be able to assist them in leading healthy lives. Of course, while not all answers lie in this arena, it is an extremely promising field.

Mood and mental health-related conditions are immensely important topic in today’s world and for good reason. According to the WHO, one in four people around the world experiences such conditions and as a result can accelerate their path toward ill-health and comorbidities.

Recently, machine learning algorithms have been developed to detect words and intonations of an individual’s speech that may indicate a mood disorder.

Using neural networks, an MIT-based lab has conducted research onto the detection of early signs of depression using speech. According to the researchers, the “model sees sequences of words/speaking style” and decides whether these emerging patterns are likely to be seen in individuals with and without depression.

2. Managing complications

The general feeling of being unwell and its various complications that accompany mild illnesses are usually well tolerated by patients. However, for certain conditions, it is categorically important to manage these symptoms as to prevent further development and ultimately alleviate more complex symptoms.

Machine learning techniques can contribute toward the prediction of serious complications such as neuropathy that could arise for those suffering from type 2 diabetes or early cardiovascular irregularities. Furthermore, the development of models that can help clinicians detect postoperative complications such as infections will contribute toward a more efficient system.

3. Patient-care assistance

Patient-care assistance technologies can improve the workflow for clinicians and contribute toward patient’s autonomy and well-being. If each patient is treated as an independent system, then based on the variety of designated data available, a bespoke approach can be implemented. This is of utmost importance for the elderly and the vulnerable in our societies.
An example of this could be that of virtual health assistants that remind individuals to take their required medications at a certain time or recommend various exercise habits for an optimal outcome.

4. Medical research

AI can accelerate the diagnosis process and medical research.

In recent years, an increasing number of partnerships have formed between biotech, MedTech, and pharmaceutical companies to accelerate the discovery of new drugs. These partnerships are not all based on curiosity-driven research but often out of necessity and need of society.

A good example of this collaboration is seen in a recent breakthrough for antibiotic discovery, where the researchers devised/trained a neural network that actively “learned” the properties of a vast number of molecules in order to identify those that inhibit the growth of E. coli, a Gram negative bacterial species that is notoriously hard to kill.