In today’s world, the application of artificial intelligence (AI) is growing rapidly across all sectors, and one sector that needs to be highlighted is healthcare. AI is commonly known as a computational ability to adapt, reason, and solve problems that mimic human cognitive functions (Summerton & Cansdale, 2019). In modern healthcare, this mathematical algorithm and its advancement have become a core part of improving general practice in healthcare because of its comprehensive features. This article outlines the utilisation of AI in general practice, including diagnosis and personalised care, administrative tasks, and remote monitoring.

Application of AI in General Practice

Diagnosis and Personalised Care

One of the primary applications of AI in general practice is assisting in making diagnoses where general practitioners (GPs) can make accurate diagnostics to confirm a particular disease. Summerton and Cansdale (2019) suggest that when confronted with prevalent symptoms, such as lethargy or exhaustion, it is possible that AI can be utilised to facilitate more logical methodologies for testing in general practice. This allows it to evaluate patient data encompassing symptoms, medical history, and demographic details to discern patterns that may indicate particular illnesses.

Through the utilisation of data-driven approaches, general practitioners can guarantee that their testing protocols are rational and customised to each patient’s specific circumstances. For instance, Buch et al. (2018) visualised a patient with type 2 Diabetes Mellitus; the initiation threshold for statin therapy might be discovered by AI on a personalised basis, taking into consideration each aspect of the individual’s medical condition as opposed to adhering to a strict, universal approach.

Administration

AI is also important in administrative tasks, improving overall administrative efficiency. This tool allows GPs to spend more time consulting patients. A recent review proposes that most research involved scheduling tasks that employed supervised machine learning techniques. For example, some proposed models are meant to encompass patient no-shows and time-efficient appointment scheduling according to each patient’s needs to show that AI is useful for handling high-stakes and time-consuming problems (Sørensen et al., 2023). This powerful computerisation mitigates the potential errors from manual administrative work.

Shared Decision Making (SDM)

The application of AI in SDM is vital to creating patient-centred care, where SDM is a collaborative measure that involves decision-making between patients and physicians when deciding on treatment options. One study that grasps this attention shows that AI can help run decision-support technologies that assist healthcare providers in customising diabetes treatments, hence improving population compliance and outcomes (Thomas Wojda et al., 2023). In another study, a decision-support tool called “CONSULT” was developed by Kökciyan et al. (2019) to assist stroke survivors in following prescribed treatments and practising self-care with their physicians by providing individualised health data from medical records and wireless sensors. Though research in AI applications in SDM is limited, AI shows promising potential in improving SDM in general practice.

Remote monitoring

Through AI, GPs can monitor their patient’s health remotely and seamlessly. As John et al. (2023) reported, AI technologies leverage wearable device data to detect alterations in health markers. The ultimate goal is promptly responding to deviations from the usual range by coordinating with medical professionals. This will enable early intervention for health deterioration and behaviour patterns. Thus, integrating AI in general practice improves healthcare efficiency and patient outcomes through timely interventions.

Summary

In short, AI is synonymous with knowledge, the ability or potential to change how the healthcare system is run regarding diagnostics and personalised treatment, overall administrative work, shared decision making and remote health monitoring. This will create data-driven insight that is useful for healthcare industries, especially in general practice. AI helps the GPs make improved decisions, saves time, eases workload, and ensures timely intervention for optimal health outcomes. In general practice, as technology evolves, its importance will extend as it provides working models for addressing numerous healthcare system problems while making the process more efficient.

Reference

1. Buch, V. H., Ahmed, I., & Maruthappu, M. (2018). Artificial intelligence in medicine: Current trends and future possibilities. The British Journal of General Practice : The Journal of the Royal College of General Practitioners, 68(668), 143–144. https://doi.org/10.3399/bjgp18X695213
2. Kökciyan, N., Chapman, M., Balatsoukas, P., Sassoon, I., Essers, K., Ashworth, M., Curcin, V., Modgil, S., Parsons, S., & Sklar, E. I. (2019). A Collaborative Decision Support Tool for Managing Chronic Conditions. Studies in Health Technology and Informatics, 264, 644–648. https://doi.org/10.3233/SHTI190302
3. Sørensen, N. L., Bemman, B., Jensen, M. B., Moeslund, T. B., & Thomsen, J. L. (2023). Machine learning in general practice: Scoping review of administrative task support and automation. BMC Primary Care, 24(1), 14. https://doi.org/10.1186/s12875-023-01969-y
4. Summerton, N., & Cansdale, M. (2019). Artificial intelligence and diagnosis in general practice. The British Journal of General Practice : The Journal of the Royal College of General Practitioners, 69(684), 324–325. https://doi.org/10.3399/bjgp19X704165
5. Thomas Wojda, Carlie Hoffman, Jeffrey Jackson, Tracey Conti, & John Maier. (2023). AI in Healthcare: Implications for Family Medicine and Primary Care. In Stanislaw P. Stawicki (Ed.), Artificial Intelligence in Medicine and Surgery (p. Ch. 11). IntechOpen. https://doi.org/10.5772/intechopen.111498
6. Wang, W.-H., & Hsu, W.-S. (2023). Integrating Artificial Intelligence and Wearable IoT System in Long-Term Care Environments. Sensors, 23(13). https://doi.org/10.3390/s23135913