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Healthcare Artificial Intelligence: Advantages and Disadvantages

• By Rich Health News Desk
• June 17, 2025

Artificial intelligence has the ability to help with diagnosis and treatment in a variety of ways, but it also carries a very real risk of harm. Three primary patient safety issues associated with AI in healthcare are outlined in a review by the Panel for the Future of Science and Technology (STOA):

1. False negatives, which are missed disease diagnosis.
2. The treatment that is unnecessary because of false positives.
3. In emergency rooms, inappropriate interventions might result from inaccurate diagnosis or improper intervention priority.

 

These hazards are caused by a variety of causes, including noise and artifacts in AI clinical inputs and measurements, data changes between AI training data and real-world data, and variations in clinical setting and surroundings. The fundamental causes of these hazards may be driven by AI, even if it must be accepted that such dangers exist in healthcare even in the absence of AI.

The possible decrease in personal interaction is one of the main worries. Patient care, especially in the area of mental health, greatly benefits from in-person interactions with medical professionals. These exchanges provide emotional support, empathy, and comprehension—qualities that AI finds challenging to imitate.

 

 

Patients may feel alone, misunderstood, or neglected if AI and automation reduce opportunities for face-to-face communication. This could further deteriorate their mental health, harm their connection with their doctor, and make them less likely to seek further therapy.

 

DISADVANTAGES OF AI IN HEALTHCARE

Some of the main disadvantages of artificial intelligence applications in healthcare are as follows:

AI Can Lead to Discrimination Against Marginalized Groups and Widen Inequalities, but Could Also Improve Access to Healthcare

Artificial intelligence has the ability to improve access and lower treatment barriers. According to research [1] from the Good Things Foundation, National Voices, and Healthwatch, some people—such as those who have caregiving obligations, have restricted mobility, or are immunosuppressed or shielding—may find digital services easier to use.

Additionally, there is evidence that digital mental health services [2] bypass stigma and other obstacles to traditional services. Personal biases and societal injustices are present in every aspect of life, and the healthcare industry is no different. It has been shown that doctors may believe that women overstate their pain and that members of ethnic minorities are more pain-tolerant, which could be harmful to their care.

 

AI could be built in such a way that it is less susceptible to such prejudices, so assisting in the reduction of healthcare inequities. There is increasing evidence, however, that AI and data-driven health [3] technology might result in prejudice against marginalized or underprivileged populations, worsening preexisting bias and structural disparities in healthcare and health.


First, there is an issue with the data used to train AI. Prediction, prevention, triage, diagnostics or prognostics, and decision assistance will all produce unfair results if the datasets used to train AI models are not representative.

 

 

Many AI models rely on electronic patient record systems (EPRs) as a primary source of data; however, this data usually excludes people that do not seek healthcare as frequently. An established illustration of this is how people from ethnic minorities are less likely to have skin malignancies and other skin diseases detected by AI scans

AI informed diagnostic tools because they were trained on data from white patients. The presence of ethnic minorities in skin lesion photos used to train AI systems was the subject of one 2021 study [4]. Only 10 of the 2,436 images with skin color information showed brown skin, and only one showed dark brown or black skin, according to the researchers.

 

None of the 1,585 photos with ethnicity information were taken by people of African, Afro-Caribbean, or South Asian descent.

Additional instances of algorithmic bias can be observed in individuals with neurodevelopmental disorders such as ADHD and autism, as models frequently rely on data from "average" patients, failing to take into consideration the varied needs of these groups. AI should target dual diagnosis, combine mental and physical health data, speed up the utilization of mental health services, and promote treatment adherence by integrating social care in order to address this.

 

AI Could Impact the Doctor-Patient Relationship

AI has the power to either strengthen or weaken the bond between a patient and their doctor. AI's potential for increased precision could lead to better patient outcomes, which could increase patients' confidence in clinical interventions and strengthen the bond between doctors and patients.

By taking over some administrative tasks, AI could free up clinicians' time so they can spend more time with each patient and build strong relationships with them. As a result, there may be increased chances for continuity of care and an improvement in patient-doctor trust and communication.

 

Depending on implementation, however, the time saved could be used to see more patients rather than spend more time with each one. Also, some types of healthcare automation through AI may result in the loss of effective communication, empathy, and one-on-one communication—all of which are essential components of effective patient care—even if productivity increases free up more time for in-person care. AI cannot take the place of general practice's ability to provide continuity of care.

 

 

AI may cause a patient to feel alienated and develop mistrust if it creates a barrier between the patient and the doctor, limiting the doctor's access to the patient. If a patient is diagnosed with a life-limiting condition, for instance, talking to a doctor and asking questions could have a big impact on the patient's wellbeing. Even though AI could provide answers to these queries, talking to a doctor could be consoling and encouraging, especially if a connection has already been established. A patient may become estranged from health services if this is not possible, depriving them of necessary care.

 

Moreover, a shift in the doctor-patient interaction may further entrench health disparities. A positive doctor-patient connection may help restore the trust that has been lost in health services for populations that are known to have reduced trust due to past and current prejudice. From a clinician's point of view, comprehending a patient's requirements may depend on building a rapport and connection with them based on their common humanity. This could be threatened by AI, which would affect the standard of care.

 

 

AI Could Improve Job Quality or Increase Risk of Burnout

According to the 2023 NHS England staff survey [5], about 30% of NHS employees suffer from burnout, indicating that it is a major issue in UK health systems. AI could solve this by lowering the workload that NHS employees endure through the previously mentioned productivity and efficiency techniques. As fewer employees feel compelled to quit or retire for health-related reasons, this may have an impact on employee retention.

 

 

As a result of increased productivity and efficiency, AI may also lessen the administrative load on physicians, freeing them up to focus on aspects of their work that they find more fulfilling and providing opportunities for professional growth.

 

However, artificial intelligence has the potential to create new pressures. Routine work can act as a "buffer" to more demanding or difficult tasks, and employees may find themselves working longer hours or more frequently at the edge of their skill set, which could raise the risk of burnout.

It is important to keep in mind that these results are not always the result of increased productivity; rather, they depend on how the time saved is allocated and how the productivity improvements are realized. For example, doctors may wind up seeing more patients rather than having more time for each patient if improvements are used to reduce healthcare costs by providing more treatment with a comparable amount of staff.

 

Additionally, properly implementing AI solutions is necessary for AI to enhance job experience and workflow efficiency; the difficulties of this should not be understated. When improperly implemented, the wrong technology can unnecessarily increase workloads and have negative effects on productivity.

 

AI May Improve Efficiency but Could Also Increase Healthcare Costs

AI has the potential to reduce the pressure on healthcare system finances and staff time, especially bureaucratic time, enabling them to reallocate resources to more critical areas. AI is "one of the most important ways of delivering" the "stretching productivity ambitions" that underpin the NHS' anticipated workforce expansion outlined in NHS England's Long-term Workforce Plan, for example.

 

 

This can also be viewed as a cost-cutting measure, allowing the savings to be reinvested in other areas, such as fixing staff shortages. Whether or not these productivity gains result in cost reductions depends on how those savings are realized, as was previously said above.

 

The Topol Review estimates a minimal saving of one minute saved per patient from new technologies such as AI, equating to 400,000 hours of emergency department consultation time and 5.7 million hours of GP time. In 2018, the Institute for Public Policy Research estimated that AI and automation could save the NHS in England £12.5 billion per year by freeing up staff time.

 

This can be directly, as a result of tools developed to improve back-office efficiency, streamlining administrative tasks and optimizing allocation of resources. Research by Oxford University found that 44% of all admin work in General Practice can now be mostly or fully automated – freeing up staff time for other activities.

 

Additionally, AI may result in more rapid and effective medical interventions. AI, for example, can be used in radiology to prioritize and flag results that require immediate attention. According to new data from Somerset NHS Foundation Trust, AI software can expedite the diagnostics process for patients, cutting the wait time for a CT scan after a chest x-ray from seven days to less than three days.

 

 

AI might not always lower healthcare expenses, though. According to a recent simulation study on AI in colonoscopy conducted in the USA [6], it may raise costs in the short term by increasing the number of abnormalities found, necessitating intensive surveillance colonoscopies; however, in the long run, it may help lower the incidence of colorectal cancer, which could ultimately result in lower costs. Likewise, a Chinese study on glaucoma screening [7] indicated that it could lower the likelihood of the illness progressing, but this would probably not be enough to pay the high screening costs.

 

Lack of real-world studies contributes to the difficulty in determining the genuine impact of AI on expenses. Thorough large-scale research with long-term follow-up will be necessary to determine whether or not the widespread use of AI in health care actually results in cost savings and/or better patient outcomes.

 

ADVANTAGES OF AI IN HEALTHCARE

As artificial intelligence (AI) systems improve at data sorting, pattern recognition, and prediction, their use in healthcare is probably going to grow. The expansion of AI in healthcare has a number of potential advantages, according to proponents. Numerous claims have been made that AI will lessen the demand on healthcare, help medical personnel better manage the current demand for treatment, and ultimately increase employment quality and healthcare results.

These advantages will be accomplished through three primary mechanisms:

Precision and Personalization

AI can match or beat humans in completing certain healthcare jobs with high accuracy. One well-documented area is accuracy in imaging diagnostic techniques [8]. By combining and analyzing vast volumes of clinical, sociological, genetic, epidemiological, and personal data, AI may also be able to provide more precise understandings of disease and more potent drugs and therapies. 

 

 

At the point of care, this data can then be made instantly accessible to medical professionals to aid in diagnosis and prognosis, including the prescription of medications. This has the potential to improve the accuracy and precision of medical procedures, tailor them for each patient, and base them on the most recent information, recommendations, and best practices.

 

Efficiency and Productivity

AI imaging and clinical decision making make it possible to deliver healthcare therapies more quickly and efficiently. Applying AI technologies to more mundane, daily chores like booking appointments or handling letters can also result in improvements in quality and efficiency. Routine administrative duties can account for as much as 70% of a health practitioner's time, according to McKinsey research [9] included in NHS England's long-term staffing plan.

 

 

Prevention and Early Intervention

Artificial intelligence algorithms may be able to identify patients or patient groups at increased risk of specific disorders by looking at variables including population demographics, disease prevalence, and geographic distribution. 

 

 

AI may improve the effectiveness of preventative medicine if it can offer advantages such as population health risk prediction, surveillance, medicine, and a supply of health information. Prioritizing upstream health and well-being support can reduce the strain on the healthcare system and free up resources for other areas of need.

SOURCE

BMA: Principles for Artificial Intelligence and its Applications in Healthcare

REFERENCES

[1] Impact of Digital Inclusion on Access to Healthcare

[2] An Overview of and Recommendations for More Accessible Digital Mental Health Services by Emily G. Lattie, Collen Stiles-Shields and Andrea K. Graham 

[3] A Knotted Pipeline: Data-driven Systems and Inequalities in Health and Social Care by Mavis Machirori

[4] Characteristics of Publicly Available Skin Cancer Image Datasets: A Systematic Review by David Wen et al

[5] David Oliver: The NHS Staff Survey 2023 has Depressing Findings and Worrying Implications

[6] Benefits and Challenges in Implementation of Artificial Intelligence in Colonoscopy: World Endoscopy Organization Position Statement by Yuichi Mori et al

[7] Health Care Cost and Benefits of Artificial Intelligence-Assisted Population-Based Glaucoma Screening for the Elderly in Remote Areas of China: A Cost-Offset Analysis by Xuan Xia et al

[8] How Artificial Intelligence Is Shaping Medical Imaging Technology: A Survey of Innovations and Applications by Luis Pinto-Coelho

[9] Transforming healthcare with AI: The impact on the workforce and organizations