Over the past few decades, the integration of AI within healthcare has exponentially increased, accelerated by advancements in technology, reflecting the shift towards efficiency, personalisation, and accuracy. Through the use of AI and ML, healthcare can offer more detailed services, including better diagnostics, preventative measures, and increased operative efficiency.

From the first dated usage of AI in Alan Turing’s book Computers and Intelligence and the rudimentary AI models which comprised merely of “if, or” statements (Kaul et al., 2020), AI has greatly developed within diagnostics. Traditionally, many scans would be examined by radiographers, making it time-consuming, whereas now AI is able to process data and scans instantaneously at high speeds. Furthermore, utilising AI reduces the subjectiveness of the human eye, providing more accurate results. In some cases, AI is able to notice anomalies such as lesions or tumours that practitioners couldn’t (Machine learning in healthcare: Uses, benefits and pioneers in the field, 2024) by learning large data sets of scans ranging from mammograms to spirometers. A research study by Dabowsa et al. in 2017 used a back-propagation neural network to diagnose dermatological conditions using data sets from a dermatology clinic, with the results displaying very high accuracy (Kumar et al., 2022).

Another advantage of AI and ML in diagnostics is that whilst being more accurate, it can also diagnose conditions earlier. ML can collate the patient’s data, including their predisposition, lifestyle, past medical history, and data from wearable devices, and predict the chance of the individual having a certain condition from its onset to its progression. ML can also take into account the demographics of the area (Dorocka, 2024) and see what conditions are prevalent. If AI does notice this, healthcare professionals can implement measures preventing the disease from becoming symptomatic (Agarwal et al., n.d.).

To add to this, ML enables a patient’s treatment plan to become more dynamic and flexible. Initially, AI predicts how the patient would react to certain treatments or medication. Consequently, the professional can provide treatments more attuned to the patient’s needs that also minimise adverse risks. This is further enhanced through the main advantage of ML, which is that it continually develops and therefore is kept up to date with the latest discoveries in medical literature. As a result, AI is able to suggest effective alternative methods of treatment.

As well as assisting with the medical tasks, AI also supports the operative sides of the healthcare industry. ML can manage the administrative tasks, including actions such as allocating resources through analysing past medical admissions and modelling seasonal trends. It can also manage patient inflow management, which prevents bottlenecking and ensures that individuals receive care on time. The usage of AI increases the overall efficiency of the healthcare system, enabling individuals to obtain the highest quality of care possible.

Overall, AI has many benefits, providing a holistic solution to the current pressing concerns. However, it is also important to mention that the amalgamation of AI comes with its drawbacks. Ranging from conflict between the confidentiality of patients against the need to obtain data sets to the social concerns of the general public fearing AI (Khan, 2023), for AI to reach its full potential, such as robotic surgery, there needs to be a radical change in mindset and more cohesion between different sectors.

Bibliography

Agarwal, A., Gangwar, T. (n.d.) Can artificial intelligence help us achieve the dream of personalised care? Infosys BPM. Retrieved December 29th, 2024 from Infosys BPM

Dorocka, W. (2025, September 25). How AI is improving diagnostics and health outcomes, transforming healthcare. World Economic Forum. Retrieved December 26th, 2024 from WEF

Kaul, V., Enslin, S., Gross, S. (2020). History of Artificial Intelligence in Medicine. Gastrointestinal Endoscopy, 92(4), 807-808. Retrieved January 9th, 2025 from GIE Journal

Khan, B., Fatima, H., Qureshi, A., Kumar, S., Hanan, A., Hussain, J., Abdullah, S. (2023 February). Drawbacks of Artificial Intelligence and Their Potential Solutions in the Healthcare Sector. PubMed Central. Retrieved January 8th, 2025 from PMC

Kumar, Y., Koul, A., Singla, R., Ijaz, M. (2022 January). Artificial intelligence in disease diagnosis: a systematic review, synthesising framework and future research agenda. PubMed Central, 14(7). Retrieved December 29th, 2024 from PMC

Machine learning in healthcare: Uses, benefits and pioneers in this field (2024, September 18th). EIT Health. Retrieved January 3rd, 2025 from EIT Health