Artificial Intelligence is changing the way we live, work and interact with each other. Its applications in healthcare and medicine are both significant and transformative. Here are some:

Medication discovery and development

AI has been widely used in drug discovery and development because it provides a faster and more efficient method for identifying and testing potential drug candidates. One example of this is the use of AI in identifying new antibiotics.

AI algorithms can analyse vast amounts of data and detect potential candidates that may be effective against antibiotic-resistant bacteria. This has the potential to revolutionise the field of medicine because antibiotic resistance is a growing concern worldwide.

The same can be said about cancer medications, which usually are tried on animals and humans, a process that can be tedious, time-consuming and fraught with undesirable outcomes. AI can make it swift, efficient, and safe.

In addition to identifying new drug candidates, AI can also be used to optimise drug development. For example, machine learning algorithms can be used to predict how a certain preparation would behave in the human body, allowing researchers to optimise dosages and minimise side effects.

Diagnosis and treatment

AI has the potential to significantly improve the accuracy and speed of diagnosis and treatment of diseases. One example of this is the use of AI in detecting skin cancer. Machine learning algorithms can analyse images of skin lesions and accurately identify malignant growths, often with the same accuracy as trained dermatologists.

Another example is the use of AI in predicting and managing chronic diseases such as heart failure, coronary disease and atrial fibrillation. Machine learning algorithms can analyse population data and predict the likelihood of a patient developing such illnesses, allowing healthcare providers to intervene quite early and prevent adverse consequences.

For instance, in cardiology, future cardiac events prediction algorithms developed by researchers can incorporate a limited number of variables before they become confusing and complex to apply. On the other hand, an AI developed prognostication algorithm could incorporate, master, analyse and allocate weights to hundreds of cardiac risk parameters, significantly improving cardiac disease chance estimation and early diagnosis.

AI can also be used to develop “customised” treatment plans for patients, an evolving field in modern healthcare, called “personalised medicine”. For example, AI algorithms can analyse data to identify the most effective treatment for a particular patient, taking into account specific factors such as age, gender, genetics, lifestyle, allergies, medical history and idiosyncrasies.

Medical imaging

Medical imaging is another area where AI has significant potential. Machine learning algorithms can analyse images such as X-rays, MRIs and CT scans, and accurately identify abnormalities such as tumours and fractures. This has the potential to improve diagnostic earliness, accuracy and speed, leading to better patient outcomes.

AI can also be used to improve the quality of medical images. For example, deep learning algorithms can be used to remove noise and artefacts from medical images, improving their clarity.

Finally AI can, in some imaging modalities, obviate the need for extensive radiation or contrast agents’ use, and decrease the potential toxicity related to them.

Electronic health records

Electronic health records are a digital version of a patient's medical record, containing information such as medical history, medications and test results. AI can be used to comb through massive EHR data to identify patterns and trends, allowing healthcare providers to make better-informed medical care decisions, both at a small scale and a population-wide level.

Administrative tasks

AI can help automate administrative tasks such as appointment scheduling, billing, and coding, allowing healthcare providers to focus more on patient care.

Limitations and concerns

While AI applications in healthcare offer numerous benefits, they also raise significant ethical, legal, and social implications. Some of the key concerns are:

1. Data privacy and security: the adoption of AI in healthcare requires the collection and analysis of large amounts of sensitive patient information. This raises concerns about data privacy and security, and the potential misuse of patient records.

2. Bias and discrimination: AI algorithms are only as good as the data they are trained on. If the latter is biased or discriminatory, this can lead to unfair and unequal healthcare outcomes.

3. Liability and accountability: as AI becomes more integrated into healthcare delivery, questions arise about who is responsible for the decisions made by AI algorithms and the potential liability for errors or adverse outcomes.

4. Workforce displacement: the use of AI in healthcare may lead to the displacement of some healthcare workers as tasks are automated. This raises concerns about the potential impact on the healthcare workforce and the need for laying off or retraining some staff and talents.

Joseph Yammine is a consultant cardiologist at the Bermuda Hospitals Board. The information herein is not intended as medical advice nor as a substitute for professional medical opinion. Always seek the advice of your physician