10 Benefits of Artificial Intelligence in Healthcare
By: Nataliya Zlotnikov MSc, HBSc; Editor: Kristen Lee, BSc ∙ Estimated reading time 6 minutes

AI can support and enhance healthcare in many ways

At Embodia, we recently launched our AI scribe feature, AI Charting. However, Artificial intelligence (AI) has many other healthcare applications beyond charting. 

"Artificial intelligence has the potential to shape the future of human health and medicine. “Machine eyes will see things that humans will never see,” says Dr. Eric Topol, executive vice president and professor of molecular medicine at Scripps Research Institute and founder and director of the Scripps Research Translational Institute" (Bock, 2024). 

This blog will give you a comprehensive overview of just a few of these applications while also addressing some challenges and risks involved. 

Administrative tasks 

Simplify and decrease administrative burden to prevent burnout

Practitioners do a lot these days, often wearing multiple hats and feeling overworked as a result (Ellis, 2024). 

Tools like AI scribes for recording patient sessions and translating the recording to a chart, AI scheduling assistants (IBM, 2023), and AI-assisted billing and coding (Al-siddiq, 2025) decrease the time allied health professionals need to spend on these tasks. 

Ultimately, AI can help to prevent practitioner burnout (Ellis, 2024) and free up time for other tasks. 

But to err is not only human, t’is also AI. 

As we mentioned in our previous blog, 7 Benefits of AI Charting on Embodia, imagine having said ilium, only to find that your AI scribe wrote ileum! Oy vey! 

AI is the product of our collective knowledge - it does not replace us. 

That’s why we can’t allow ourselves to become overly reliant on AI. We saw how that turned out in Terminator and iRobot. 

Though its capabilities are continually being improved upon, we need to stay sharp and make it a regular practice to check the work of AI to ensure its accuracy.    

Virtual healthcare assistant 

This new, innovative technology mimics human dialogue to deliver personalized patient care according to individual input and encompasses applications, chatbots, sounds, and interfaces. 

These virtual assistants can aid patients in recognizing potential issues based on their symptoms, offer medical guidance, remind them to take their medications, set up appointments with healthcare providers, track vital signs, and provide continuous support. 

Furthermore, these assistants can gather patient health data and relay it to their allied health professional (Alowais et al., 2023).  

Assessment, Diagnosis & Treatment

Diagnostic accuracy and speed

AI has shown greater-than-human accuracy and faster analysis in medical imaging. 

This assists in the early detection of various diseases, such as breast cancer and mental health conditions, which can lead to improved patient outcomes, reduced disease burden, timely treatment, and lower healthcare costs associated with late-stage diagnoses (Chustecki, 2024). 

However, AI diagnosis is not always superior to human diagnosis. “In addition, AI models can suffer from overfitting, generating irrelevant correlations between patient characteristics and outcomes, which can lead to incorrect predictions when applied to new cases” (Chustecki, 2024). 

Clinical decision support 

AI-powered chatbots can help alleviate the workload for allied health professionals, enabling you to concentrate on more complex cases requiring your expertise (Trudinger, 2024). Additionally, AI algorithms can provide personalized suggestions for each patient by considering various factors, such as medical records, lifestyle, health issues, and more (Alowais et al., 2023).  

This, however, raises a few issues, including that of patient autonomy. We know that patient education and communication are crucial to a patient-practitioner relationship and successful patient outcomes. Reliance on AI-based decision-making can undermine this relationship by reducing meaningful dialogue opportunities. In addition, if, for example, insurance companies begin to require AI recommendations for reimbursement, refusing to cover treatments if AI recommends against them, this would further remove autonomy and choice from the patient (Vayena et al., 2018).  

Provides data & wealth of organized expert information for clinical decision-making 

Previously, allied health practitioners needed to conduct thorough research to focus on challenging cases requiring diagnosis or treatment. 

Nowadays, clinicians can pose diagnostic inquiries to AI chatbots and receive instant access to an abundance of information and guidance.

This can save considerable time when searching for comparable cases (Ellis, 2024) and can also lead to more data-informed decisions within the healthcare system (Chustecki, 2024). 

Additionally, AI is capable of enhancing the process of clinical decision-making by helping health practitioners decide which patients will respond to a treatment based on the availability of prior data to create more effective and individualized treatment plans (Ellis, 2024). 

As helpful as that is, unfortunately, AI is not free from bias. This is largely due to the biased data sets that may be used for its training. For instance, if certain races or genders are underrepresented in the training data, this can serve to exacerbate health inequities. As a result, there may be a risk of under or overestimating health outcomes for specific patient populations (Chustecki, 2024).

Fewer errors

AI technology can aid in the reduction of human error in healthcare practices (Alsobhi et al., 2022). 

AI has an aptitude for handling repetitive tasks, processes, and large amounts of data (Ahuja, 2019).

That can help support practitioner decision-making, differential diagnosis, and treatment option suggestions, reducing the potential risk of human error and misdiagnosis (Dilsizian & Siegel, 2014).   

However, what if AI does make a mistake in diagnosis? Who is to be held accountable? 

Determining liability with AI in healthcare is an ongoing concern that remains undetermined (Chustecki, 2024). 

Patient education, support & empowerment 

AI mental health support 

Traditionally, allied health practitioners providing mental health support have relied on therapeutic discourse and patient narrative to assess mental health. 

Now, AI is showing potential to revolutionize mental health in numerous ways (Alowais et al., 2023; Chustecki, 2024), such as being able to identify suicidal thoughts in clinical notes, forecasting suicide risk found online (Chustecki, 2024), providing web-based cognitive behavioural therapy (CBT), round-the-clock support, and patient progress monitoring. 

A recent study assessed the effectiveness of the mental health app Woebot in patients with substance use disorders. The findings indicated that using Woebot was significantly linked to reduced substance use, cravings, depression, and anxiety (Alowais et al., 2023).

Education and empowerment 

“Informed patients are more likely to adhere to their treatment regimens and achieve better health outcomes” (Alowais et al., 2023). 

Artificial intelligence has the potential to enhance patient education significantly by providing personalized and interactive information and guidance to patients. 

For example, a prostate cancer chatbot (PROSCA) improved participants' understanding of their condition. 

Similarly, research has shown that ChatGPT can help patients with diabetes understand their diagnosis and treatment options and provide feedback and encouragement. 

AI can also adapt patient educational materials to different reading levels, empowering patients to better manage their health.

Transformational change 

Aiding in the development of health guidelines and regulations 

With approximately 1.8 million articles published annually in medical peer-reviewed journals (Dai et al., 2014), creating evidence-based policies can be a slow, lagging process. 

AI can help identify new publications from clinical trials and real-world patient outcomes to facilitate the first stage of mining information (Alowais et al., 2023) to expedite this process. 

Experts in the field can then use this data to develop up-to-date, evidence-based guidelines to inform practice (Alowais et al., 2023). 

Proactive data 

AI can help use large amounts of data to identify people at higher risk of various conditions. 

For example, it can identify persons who have a higher likelihood of developing opioid dependency following surgery, to monitor them closely and wean them off the drugs as soon as possible. 

AI can also be used to identify situations where errors, such as drug errors, are most likely to occur and implement stronger safety and prevention protocols (Ellis, 2024). 

The bottom line for healthcare (TL;DR)

AI is extremely helpful to the healthcare industry. It can reduce administrative burden, identify trends from healthcare data, thereby helping practitioners make more informed conclusions faster and provide automated support to patients for easier tasks, which is pretty incredible and impactful. 

However, there’s a pretty glaring trend. 

Artificial intelligence does not replace the real thing. 

It’s an excellent assistant and can pick up on things that would take us longer to notice or that we may not have caught, but it isn’t always correct and still needs diligent supervision. 

As our use of artificial intelligence changes and grows, and AI continues to learn with our usage, formalized and evolving policies will have to be created, monitored and updated to ensure patient safety and the ethical use of AI.

So, while human intelligence is irreplaceable, artificial intelligence can have a net positive impact on health and with a powerful assistant, our finite cognitive bandwidth can go even further.

References

Ahuja, A.S. (2019). The impact of artificial intelligence in medicine on the future role of the physician. PeerJ, 7:e7702. https://doi.org/10.7717/peerj.7702. 

Al-siddiq, W. (2025). Accelerating Healthcare With AI: Reducing Administrative Burdens. Forbes. Retrieved from https://www.forbes.com/councils/forbesbusinesscouncil/2025/01/07/accelerating-healthcare-with-ai-reducing-administrative-burdens/

Alsobhi, M., Khan, F., Chevidikunnan, M.F., Basuodan, R., Shawli, L.,  Neamatallah, Z. (2022). Physical Therapists’ Knowledge and Attitudes Regarding Artificial Intelligence Applications in Health Care and Rehabilitation: Cross-sectional Study. J Med Internet Res. 24(10):e39565. doi: https://doi.org/10.2196/39565 

Alowais, S.A., Alghamdi, S.S., Alsuhebany, N., Alqahtani, T. Alshaya, A.I., Almohareb, S.N., Aldairem, A.,  Alrashed, M., Bin Saleh, K., Badreldin, H.A., Al Yami, M.S., Al Harbi, S. and  Albekairy, A.M. (2023). Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Medical Education, 23(689). https://doi.org/10.1186/s12909-023-04698-z 

Bock, E. (2024). Topol Discusses Potential of AI to Transform Medicine. NIH Record, LXXVI(24). Retrieved from https://nihrecord.nih.gov/2024/11/22/topol-discusses-potential-ai-transform-medicine

Chustecki, M. (2024). Benefits and Risks of AI in Health Care: Narrative Review. Interact J Med Res, 18;13:e53616. https://doi.org/doi:10.2196/53616

Dai, N., Xu, D., Zhong, X., Li, L., Ling, Q. and Bu, Z. (2014). Build infrastructure in publishing scientific journals to benefit medical scientists. Chin J Cancer Res: 26(1):119–123. https://doi.org/10.3978/j.issn.1000-9604.2014.02.10 

Dilsizian, S.E. and Siegel, E.L (2014). Artificial intelligence in medicine and cardiac imaging: harnessing big data and advanced computing to provide personalized medical diagnosis and treatment. Curr Cardiol Rep. 16(1):441. https://doi.org/10.1007/s11886-013-0441-8. 

Ellis, L.D. (2024). The Benefits of the Latest AI Technologies for Patients and Clinicians. Harvard Medical School. Retrieved from https://postgraduateeducation.hms.harvard.edu/trends-medicine/benefits-latest-ai-technologies-patients-clinicians 

IBM. (2023). AI Healthcare Benefits. Retrieved from https://www.ibm.com/think/insights/ai-healthcare-benefits

Trudinger, M. (2024). Innovation at hand: AI's impact on physiotherapy. Australian Physiotherapy Association, Physiotherapy inmotion. Retrieved from https://australian.physio/inmotion/innovation-hand-ais-impact-physiotherapy 

Vayena, E., Blasimme, A. and Cohen, G. (2018). PLos Med, 15(11):e1002689. https://doi.org/10.1371/journal.pmed.1002689 

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Date written: 27 April 2025
Last update: 2 April 2025