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Introduction
Artificial Intelligence (AI) is the mimicry of certain aspects of human behaviour such as language processing and decision-making using Large Language Models (LLMs) and Natural Language Processing (NLP).
LLMs are specific type of AI that analyse and generate natural language using deep learning algorithms. AI programs are made to think like humans and mimic their actions without being biased or influenced by emotions.
LLMs provide systems to process large data sets and provide a clearer view of the task at hand. AI can be used to identify patterns, analyse data, and make predictions based on the data provided to them. It can be used as chatbots, virtual assistants, language translation and image processing systems as well.
Some major AI providers are ChatGPT by Open AI, Bard by Google, Bing AI by Microsoft and Watson AI by IBM. AI has the potential to revolutionize various industries including transportation, finance, healthcare and more by making fast, accurate and informed decisions with the help of large datasets. In this article we will talk about certain applications of AI in healthcare.
Applications of AI in healthcare
There are several applications of AI that have been implemented in healthcare sector which has proven quite successful.
Some examples are:
Medical imaging: AI algorithms are being used to analyse medical images such as x-ray, MRI scans and CT scans. AI algorithms can help radiologists identify abnormalities - assisting radiologists to make more accurate diagnoses. For example, Google’s AI powered Deepmind has shown similar accuracy when compared to human radiologists in identifying breast cancer.
Personalised medicine: AI can be used to generate insights on biomarkers, genetic information, allergies, and psychological evaluations to personalise the best course of treatment for patients.
This data can be used to predict how the patient will react to various courses of treatment for a certain condition. This can minimize adverse reactions and reduce the costs of unnecessary or expensive treatment options. Similarly, it can be used to treat genetic disorders with personalised treatment plans. For example, Deep Genomics is a company using AI systems to develop personalised treatments for genetic disorders.
Disease diagnosis: AI systems can be used to analyse patient data including medical history and test results to make more accurate and early diagnosis of life-threatening conditions like cancer. For example, Pfizer has collaborated with different AI based services to diagnose ailments and IBM Watson uses NLP and machine learning algorithms for oncology in developing treatment plans for cancer patients.
Drug discovery: AI can be used in R&D for drug discovery, making the process faster. AI can remove certain constraints present in drug discovery processes for novel chronic diseases. It can lead to saving millions of patients worldwide with a sped-up process, making it both cost and time efficient.
Per McKinsey research, there are around 270 companies working in AI-driven discovery with around 50% situated in the US. In addition, they have identified Southeast Asia and Western Europe as emerging hubs in this space. For example, Merck & Co. are working to develop a new treatment with the help of AI for Alzheimer’s.
What to expect in the future
We are seeing a revolution in the field of Machine Learning and AI happen in the past few years. Now we have LLMs and Image Processing Systems which can be used for faster, more efficient and prioritized results to make decisions more accurately and provide the best possible patient care.
Properly trained AIs are not biased - it’s important to develop these AI systems ethically. The efficiency of these systems depends on specific application and implementation.
AI systems can be biased if they are trained on biased data, so it is important to ensure that the data these models are trained on is diverse and representative. Implementation of AI in healthcare is still in early stages in drug discovery and it’ll see a continued growth going forward.