Machine Learning in medicine.

 23th December 2024

Artificial Intelligence (AI) and machine learning (ML) are so much more than Chat GPT, image creators, and other ways of filling the internet with even more content. In a world with data is everything, machine learning can help us analyse and understand this information more effectively. It can help us make breakthrough discoveries and create innovative solutions to improve people’s lives. This is particularly true in the healthcare sector.

In healthcare, machine learning has the potential to transform patient outcomes, enhance the quality of care, streamline healthcare delivery, and, more importantly, make healthcare more accessible and cost-effective.

In recent years, predictive healthcare models have been developed that can accurately identify trends, patterns, and subtle abnormalities that may escape even the most experienced professional, and, as such, applications of machine learning are growing rapidly, from early diagnosis of diseases to predictive analytics and personalised medicine.

 

How is machine learning used in medicine?

Personalised medicine and treatment plans

Personalisation is one of the most promising developments in modern medicine. Instead of patients following a one-size-fits-all approach, machine learning can analyse individual patient data to enable healthcare providers to predict which treatment option is likely to be the most effective, minimising the risk of an adverse reaction. This approach is most valuable in fields like oncology, where treatment effectiveness varies enormously between patients.

And because machine learning models are continuously learning from new data, this means personalised treatment plans are dynamic and adaptable. So if a treatment is or isn’t working, the model can suggest adjustments to dosage or medication, meaning it’s continuously being optimised to suit the patient’s evolving condition and ensuring the treatment remains effective, which is especially useful in managing chronic diseases such as hypertension or diabetes.

Drug discovery and development

Machine learning is helping researchers with drug development by helping to accurately identify potential new drugs, and predict their safety and effectiveness through:

• Analysing complex biological data such as genetic information, protein structures, and molecular interactions, and predicting how different compounds will interact with various target proteins and enzymes.
• Modelling and predicting how changes to the chemical structure of the drug will affect its performance to ensure that it is safe for patients. This also leads to drugs that are more likely to succeed in the clinical trial phase.
• Analysing past clinical trial data to predict how the new drug might work along with any potential risks. This analysis can also enhance the trial’s design so that it run more efficiently and effectively by identifying suitable patient populations, appropriate dosing strategies, and potential side effects.

There are still so many advantages of machine learning in medicine, and the ones I mentioned are what I wanted to discuss with you today. See you in the next article!