10 June, 2024
Medicine is a continuously evolving, dynamic field aimed at improving patient care. Hospitals, hospital management, doctors, nurses, other healthcare workers, insurance companies, pathology laboratories, imaging, pharmacy, pharmaceutical industries, research, and many other elements are present in a smoothly functioning healthcare ecosystem. Artificial Intelligence (AI) has the potential to improve accuracy, precision, and outcomes in multiple aspects of this ecosystem. It also has the ability to reduce the burden on the healthcare system by assisting in laboratory diagnosis, clinical diagnosis, imaging analysis, research studies, financial management, documentation, streamlining workflow, and much more. Machine Learning (ML), Deep Learning (DL), and Natural Language Processing (NLP) are different AI approaches applied in the healthcare sector.
The application of different AI techniques in the healthcare sector is primarily determined by the type of data to be interpreted. Healthcare providers, insurance companies, pharmaceutical firms, and research organizations are some of the important sources of healthcare data. Data is primarily of two types: structured and unstructured. Structured data is consistent and well organized (e.g., blood glucose values of patients taking part in a research study). Unstructured data is inconsistent and may vary widely amongst itself (e.g., human language, imaging, signals such as ECG). After plotting data on a correct timeline, eliminating biases, and converting it into a format interpretable by the corresponding AI application, the data is ready to be used for training the respective AI model.
Different kinds of data require different AI architectures:
Challenges of applying AI in Healthcare
One of the most important considerations for any AI model to be successful is the quality, amount, and type of data used to train and validate the model. With medical data growing at a rapid rate, it is essential to use the latest and most accurate available data. It is also important to periodically upgrade the model based on new data. AI algorithms can only identify correlation. They do not prove causation.
Thus, it is important to verify whether the model employs correct methods to correlate the input and output. Many times, complex correlations predicted by the model are hard to interpret.
Currently, there is a lot of mystery, hype, misunderstanding, and scepticism as far as the relevance of AI in healthcare is considered. A major concern is the loss of jobs requiring repetitive work (clerical, billing, triaging, data compilation, etc.). Another concern is deskilling, which causes physicians to become overly reliant on AI, resulting in a decreased skillset of the physicians themselves. Automation bias, a phenomenon in which AI systems are considered to be superior to human intelligence, should be taken into consideration. Breach of privacy, data theft, and unethical use of data are all genuine challenges that are being addressed globally.
This branch, though still in its infancy, if deployed in hospital workflows, keeping the interests of the patient in mind, has the potential to transform and revolutionize the healthcare sector.