The Role of Artificial Intelligence in Clinical Practice

Although Artificial Intelligence (AI) is not new, it has surpassed what we thought was humanly possible in recent years, as far as the application of technology is concerned. From healthcare to education to productivity to entertainment to agriculture, AI has far-surmounted what it could do about 75 years ago when it was first discovered.^[1]Chopra H, Annu, Shin DK, Munjal K, Priyanka, Dhama K, Emran TB. Revolutionizing clinical trials: the role of AI in accelerating medical breakthroughs. Int J Surg. 2023 Dec 1;109(12):4211-4220. doi: … Continue reading

In the area of healthcare, artificial intelligence plays a pivotal role in clinical practice, medical imaging and diagnostics, patient monitoring, electronic health records, genomic medicine, etcetera.

Whether for academic purposes or for general knowledge, you might want to know how Artificial Intelligence has facilitated various aspects of healthcare, including clinical trials, diagnostics, health records, and other areas above, amongst so much more. In this post, you will see the various ways AI has contributed to healthcare and allied areas.

Brief Overview of Artificial Intelligence

Britannica gave one of the simplest ways to describe Artificial Intelligence (AI) as “the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings.”^[2]Copeland, B. J. (2025, August 15). Artificial intelligence. In Encyclopædia Britannica. Encyclopædia Britannica, Inc. Retrieved from https://www.britannica.com/technology/artificial-intelligence

It is called artificial intelligence because it is a simulation of how humans use neural algorithms to solve problems, only that in this case, it is artificial, involving tech-based machines.

When a human baby encounters a burning candle, he explores it out of curiosity, and when it burns his hand, he learns not to touch it again next time. In addition, he also begins to associate objects or materials that look like the burning candle or the fire it produces, to be dangerous. This is what intelligence means: the ability to link experiences to adapt to the environment and solve problems.

In the case of machines, scientists have developed machine models that closely resemble how we reason as humans. In the 1950s, the first generation of modern Artificial Intelligence was developed, which involved rule-based models, where machines solved problems only if a certain rule was true.^[3]IBM. (2024, August 9). What is artificial intelligence (AI)? IBM Think. Retrieved August 18, 2025, from https://www.ibm.com/think/topics/artificial-intelligence

First-gen AI gave way to a more advanced generation in the 80s called Machine Learning (ML). These newer models are built to learn from a pool of data without being explicitly programmed for every single task. Further improvements in ML paved the way for a newer generation called Deep Learning. Deep learning is a subset of ML that uses complex neural networks to process complex datasets and solve more complex queries or problems.

Today, with Generative AI, AI machine models can generate new information from complex datasets using deep learning core infrastructure.

Role of Artificial Intelligence in Clinical Practice

Clinical practice involves every step from the patient’s reception, diagnosis, and treatment of the patient’s condition. Current generations of Artificial Intelligence have capabilities that can potentially play significant roles in clinical practice and healthcare.

We now examine the following areas where AI plays a role in clinical practice, healthcare, and clinical research.

1. Electronic Health Records

Maintenance of health records always presents a huge challenge in healthcare. This is because the growing number of patients in any health facility could gradually overwhelm health record officials. To solve this problem, health facilities are now adopting electronic health records instead of the traditional paper file system.

Artificial intelligence plays a significant role in the management of electronic health records (EHR). According to a systematic review in 2023, AI was shown to play a role in various aspects of electronic patient records, including prognostication, quick sorting, identification, and retrieval of patients’ records.^[4]Rahdar, M. and Esmaeili, H. (2023). Artificial Intelligence and Its Role in Electronic Patient Records. Hospital Practices and Research, 8(4), 333-343. doi: 10.30491/hpr.2024.454379.1424

From the review, it was established that AI-integration in EHR can result in a 20-30% improvement in resource planning, a 30% decrease in patients’ wait times, better resource use, and more accurate predictions of outcomes.

Electronic patients’ data can be easily analyzed using AI systems to predict relapse and average recovery periods, etc. In association with Patient-Generated Health Data (PGHD), EHR can greatly improve clinicians’ ability to diagnose patients’ health issues and classify risks at the patient’s level. Complex analyses of readily available health data by AI can provide organized PGHD, which can be further organized and applied to individual patients’ care.^[5]Ye J, Woods D, Jordan N, Starren J. The role of artificial intelligence in the application of integrating electronic health records and patient-generated data in clinical decision support. AMIA Jt … Continue reading

2. Clinical Diagnostics

AI now plays a significant role in assisting clinicians in making faster and more accurate diagnoses. With more advanced pattern recognition, recent models of AI (Deep learning systems and Generative AI) can help synthesize main and differential diagnoses for cases simulated by a clinician, thus improving diagnostic accuracy and saving time to improve patients’ outcomes.^[6]Alowais, S.A., Alghamdi, S.S., Alsuhebany, N. et al. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Med Educ 23, 689 (2023). … Continue reading

Irrespective of the degree of accuracy current AI models have in making diagnoses, ideas presented by these AI can guide experienced and inexperienced clinicians alike towards making a sound diagnosis in record time, especially when a dilemma exists or when making a diagnosis is challenging.

AI offers higher diagnostic accuracy, reduced costs, and time savings while minimizing human errors. It can revolutionize personalized medicine, optimize medication dosages, enhance population health management, establish guidelines, provide virtual health assistants, support mental health care, improve patient education, and influence patient-physician trust.^[7]Alowais SA, Alghamdi SS, Alsuhebany N, Alqahtani T, Alshaya AI, Almohareb SN, Aldairem A, Alrashed M, Bin Saleh K, Badreldin HA, Al Yami MS, Al Harbi S, Albekairy AM. Revolutionizing healthcare: the … Continue reading

3. Diagnostic Imaging

Another subset of clinical diagnosis is diagnostic imaging. Artificial intelligence can play a significant role in the analysis and interpretation of radio images from X-rays, CT-scans, MRIs, and even Ultrasound scans.

A study published in the UK showed that employing AI systems in the interpretation of mammograms for breast cancer diagnosis resulted in an absolute reduction in the cases of false positives and false negatives. A similar study in South Korea showed that AI-aided diagnoses were more sensitive (91%) to breast cancer diagnosis compared to radiologists (74%).^[8]Ibid. Alowais, S.A., Alghamdi, S.S., Alsuhebany, N. et al.

4. Genomic Medicine

AI can easily aggregate patients’ health data to better understand the contributions of the genome in disease prevalence/incidence, identification of genetic risk factors, and therapeutics. This holds immense promise in the realms of disease surveillance, disease prediction/diagnosis, and personalized medicine.^[9]Ibid. Alowais, S.A., et al.

Additionally, AI can potentially advance the clinical application of genomics by directly facilitating the steps involved in clinical genome analysis, improving our understanding of genomic variation in relation to health and disease, and accelerating discovery in genomic medicine. AI can drive the creation of algorithms for better identification of genetic variants, including those that are currently difficult to accurately detect.^[10]Raza, S. (2020). Artificial intelligence for genomic medicine: Executive summary (PHG Foundation Executive Summary). PHG Foundation. Retrieved August 19, 2025, from … Continue reading

5. Critical Care Management

Intensive care management relies on continuous patient monitoring, evaluation, and life support. These tasks, in themselves, define the intensive nature of critical care. Recent development reveals the potential role of AI in solving some of the most challenging problems of intensive care.

For example, new AI models are showcasing advanced capabilities, markedly improved reliability, and the ability to adapt their knowledge to new scenarios never encountered before. These capabilities make artificial intelligence of high potential usefulness in the Intensive Care Unit (ICU).^[11]Pinsky, M.R., Bedoya, A., Bihorac, A. et al. Use of artificial intelligence in critical care: opportunities and obstacles. Crit Care 28, 113 (2024). https://doi.org/10.1186/s13054-024-04860-z

6. Surgery and Interventions

Artificial intelligence has already started making imprints in surgery and surgical interventions through four main subfields of artificial intelligence: machine learning, artificial neural networks, natural language processing, and computer vision.

With the gradual rise of surgical bots/robots and nanotechnology, AI holds great promise in the field of surgery. Over time, artificial intelligence systems can be trained with more complex data, making them able to recognize or identify normal tissues and pathologies, make laparoscopic diagnoses, and carry out near-human surgical interventions.^[12]Amin A, Cardoso S, Suyambu J, et al. (January 04, 2024) Future of Artificial Intelligence in Surgery: A Narrative Review. Cureus 16(1): e51631. doi:10.7759/cureus.51631

In addition, AI systems, including AI-robots, can be able to categorize patients’ states, offer guidance to surgeons intra-op, and better estimate the risk of pre- and post-operative problems in surgical patients.^[13]

7. Specialty Care

Whether in disease diagnosis or in treatment, artificial intelligence has potentially encroached into all the domains of clinical practice. AI now potentially plays a role across all specialties of clinical care, including haematology, nephrology, neurology/neurosurgery, cardiology, and the rest.^[13]Krittanawong, C. (Ed.). (2023). Artificial intelligence in clinical practice: How AI technologies impact medical research and clinics (1st ed.). Academic Press.

Depending on the speciality, Artificial Intelligence has specialized usages in diagnosis or treatment. For example, the Epic’s Sepsis Model in Intensive Care is currently being used in many hospitals across the US for early detection of sepsis, leading to reduced mortality in general and critical care admissions.^[14]Cull J, Brevetta R, Gerac J, Kothari S, Blackhurst D. Epic Sepsis Model Inpatient Predictive Analytic Tool: A Validation Study. Crit Care Explor. 2023 Jun 30;5(7):e0941. doi: … Continue reading

Will AI Replace Clinicians?

This is a question that a lot of people are beginning to ask. Can AI completely replace clinicians?

According to an expert debate publication, responses in support of AI takeover can take either a top-down or bottom-up approach.^[15]Hatherley J, Kinderlerer A, Bjerring JC, Munch LA, Threlfall L. The FHJ debate: Will artificial intelligence replace clinical decision making within our lifetimes? Future Healthc J. 2024 Sep … Continue reading The top-down replacement can be from an increasing need for AI systems over human professionals due to potentially cheaper costs of maintenance. Also, as AI becomes increasingly accepted in medicine, this top-down replacement will provide hospitals, healthcare organizations, and AI organizations with a compelling opportunity to maximize their return on investment.

The bottom-up replacement is the one reflecting increasing patients’ preference for AI systems over human professionals. First, healthcare is becoming increasingly expensive and unaffordable, and AI systems could potentially offer cheaper healthcare services.

Secondly, as traditional healthcare systematically disempowers or disconnects patients from their own medical care and treatment due to imbalanced power dynamics in clinician-patient relationships, bottom-up replacement provides patients with an opportunity for greater empowerment and control in their healthcare. AI devices like wearables are likely to enable patients to manage many aspects of their health independently of clinicians.^[16]Ibid. Hatherley J, Kinderlerer A, Bjerring JC, Munch LA, Threlfall L.

But on the other hand, while AIs are increasingly becoming more intelligent than humans, there are moral and practical complexities with the way they function.^[17]Ibid. Hatherley J, et al. Current AI models, or the so-called LLMs (Large Language Models), are designed to generate or synthesize responses only by probability, with no reference to truth. By being plausible rather than correct, we might not be able to hold the systems accountable when they ‘hallucinate’.

Telemedicine Could Potentially Offer a Hybrid

In situations where individuals seek to take more control over their health and access healthcare more easily and affordably, telemedicine could potentially offer a tradeoff between dependence on a human clinician and artificial technology.

AI systems could easily be integrated with telemedicine software or platforms, where trained chatbots and virtual assistant software can help a patient get medical advice, including referral, prescriptions, and connection with physical health facilities.

However, these telemedicine apps might not fully rely on AI for healthcare. The option to consult with a human professional will always be preserved. Thus, AI is not particularly ready to fully replace human clinicians, at least not in our lifetime.

Final words

Artificial Intelligence has come to stay in several aspects of our lives, including healthcare. The role of AI in clinical practice is far-reaching, involving areas like clinical diagnosis, imaging, electronic health records, genomics, etc. Some of these were discussed in this post.

I hope this exposition further opens you to the possibilities of AI in clinical practice, and what AI cannot do, at least yet. I look forward to hearing your thoughts in the comments section below.