AI Healthcare Statistics and Facts 2025

AI is seen to have remarkable use in the healthcare industry, ranging from accurate and efficient diagnostics to patient-care and therapy.

Shortages in healthcare are a global problem, with world-known facilities like the NHS being overloaded and facing shortfalls in staffing.

In the US, a study projects that there will be 4 million healthcare workers shorter by 2026. (Mercer, 2021)

Could AI help with this growing problem? Let’s look at some statistics on AI in the healthcare market and find out.

What Is The Market Size of AI in Healthcare?

According to a report by Precendence Research, the AI in healthcare market is set to reach $613.81 billion by 2034.

• The global artificial intelligence (AI) in healthcare Market size is anticipated to reach $613.81 billion by 2034, at a CAGR of 36.83% from 2024 – 2034.

• The North American region has generated the highest revenue share of over 45% in 2023.

• The software segment was the largest revenue holder in 2023 held a 41% share, and is expected to grow at the highest CAGR. (Precedence Research report, 2024)

How Accurate Is AI in Healthcare?

AI has demonstrated high accuracy in diagnostic imaging, with a ROC-AUC of 0.91 and accuracy percentages exceeding 90% in various applications.

Could AI be as good at diagnosing scans as medical professionals?

Let’s look at studies that reveal the accuracy of AI diagnostics.

AI Accuracy in Imaging

AI has seen successful application in imaging diagnostics, having a ROC-AUC area under the curve of 0.91 according to a NCBI study.

In medical imaging for breast cancer, ML algorithms yielded a specificity of 77.3% at a sensitivity of 87% for malignancy prediction. (NCBI, 2023)

AI Accuracy in Precision Medicine

AI has been useful in segmentation tasks as well, picking up 92% of glomeruli in nephrectomy samples.

For the multi-class segmentation of renal tissue, the AI system picked 92% of all glomeruli in 1,819 nephrectomy samples with 10.4% false positives. (NCBI, 2023)

Adoption of AI in Healthcare

Here are some interesting statistics on how AI is being adopted in healthcare.

1. 72% of healthcare leaders surveyed worldwide believed that predictive analytics will have a positive impact on patient health outcomes in clinical settings. (Statista, 2022)

2. 72% also thought that patient experience would be improved by predictive analytics in healthcare. (Statista, 2022)

AI Adoption During the COVID Pandemic

AI can be useful in future pandemics like COVID, in diagnostics as well as mortality prediction.

Here are the finding of an NCBI study on how AI was utilized during the COVID-19 pandemic:

1. A sensitivity of 92.5% and a specificity of 97.9% were achieved to discriminate COVID-19 patients from influenza patients using a computational classification model.

2. An ML random forest model was used to classify COVID-19 clinical types, which achieved >90% predictive accuracy.

3. In the prediction of mortality, the sensitivity and specificity exceeded 90% while the areas under the curves (AUC) exceeded 96%.

For Covid diagnosis from Chest CT,

4. ResNet-101 showed the best performance with 99.51% accuracy, 100% sensitivity, 99.4% AUC, and 99.02% specificity.

5. In summary, the AI model may be as accurate as experienced physicians at diagnosing COVID-19. (NCBI, 2021)

Ways AI Can Help With the Staffing Shortfall

As you have seen staff shortages in healthcare already are a pressing issue that will get worse in the future.

AI can mitigate some of these effects with telemedicine, remote monitoring, predictive analytics and automating tasks.

Telemedicine and Remote Monitoring

It’s projected that 90% of hospitals will use AI-powered technology for early diagnosis and remote patient monitoring by 2025. (Market.US, 2024)

Automating Administrative Tasks

By automating routine processes such as appointment scheduling, billing, and medical coding, the healthcare companies can reduce administrative burden. (Ibex.co, 2024)

The software has the power to review millions of profiles in seconds based on dozens of data points to identify more candidates than manual sourcing could ever allow. (Marwood Group, 2023)

Predictive Analytics for Work

72% of healthcare leaders surveyed worldwide believed that predictive analytics will have a positive impact on patient outcomes in clinical settings.

Additionally, 72% thought that patient experience would be improved by predictive analytics in healthcare. (Ibex.co, 2024)

How Healthcare Professionals Feel About AI: Statistics

Sentiments about AI in the healthcare industry are mixed, with many believing it will enhance productivity, yet concerns remain about job security.

Dr. Ronald Summers, a radiologist and AI researcher at the National Institutes of Health, told AP News that AI tools should be adopted by healthcare organizations right now.

“Some of the AI techniques are so good, frankly, I think we should be doing them now,” said Dr. Ronald Summers. “Why are we letting that information just sit on the table?”

Here are the results of a few surveys.

1. The first survey was done by EY, here are their findings:

• Nearly every respondent in the sector (94%) believes AI will enhance productivity and efficiency, giving them more time on more valuable tasks.

• 83% express concern about AI being used to personalize medical plans or help with diagnoses.

• 91% believe AI needs human supervision, highlighting the importance of a human element in AI applications.

• The takes are more nuanced with 85% believing AI adoption is too slow, with many fearing they’ll fall behind without it. (EY, 2024)

2. In New Jersey, Clare Medical’s Artificial Intelligence diagnostic tool reduces ER visits and hospitalizations by 79.2% in recent large-scale trials. (Get Clare, 2023)

Based on these results Clare will initiate a full-scale launch of the CAID platform and deploy it as a diagnostic tool for its patients.

3. Nearly half (44%) of healthcare workers fear that artificial intelligence could take their jobs, according to a YouGov survey. (YouGov, 2024)

4. In 2022, 60% of psychologists in the US reported having no openings for new patients following the pandemic. (APA, 2022)

5. The US Health Resources and Services Administration estimates that 122 million Americans live in areas with a shortage of mental healthcare providers. (HRSA, 2024)

How Do Patients Feel About AI in Healthcare: Statistics and Facts

Just about a third of all polled respondents say they would be comfortable sharing their mental health concerns with an AI chatbot instead of a human therapist (34%), according to a YouGov survey.

1. The age distribution of people who are comfortable with an AI chatbot, the younger generation of 18-29-year-olds seem to be most open to it.

2. 18-to-29-year-old Americans (55%) are the most comfortable talking about mental health concerns with a confidential AI chatbot.

3. Privacy and data concerns worry nearly half of people (familiar with AI mental health chatbots (46%).

4. The most popular features of AI chatbots are ease of access, anonymity, and non-judgemental interactions. (YouGov, 2024)

Apart from therapy, what are the opinions of Americans on AI in general health?

5. The most popular features of AI chatbots are ease of access, anonymity, and non-judgemental interactions. (YouGov, 2024)

6. 60% of Americans would be uncomfortable with providers relying on AI for their health decisions. (Pew Research, 2023)

Yet, Americans still believe that AI is a net benefit to medicine.

7. A larger share of Americans think the use of AI in health and medicine would reduce the number of mistakes made by doctors (40% vs. 27%). (Pew Research, 2023)

8. Another survey found that while 32% of US adults would be comfortable with AI leading a primary care appointment, only 25% were comfortable with AI-led therapy. (Markets and Markets, 2023)

How Do Parents Feel About Their Child’s Healthcare Being Managed by AI?

The majority of parents reported being comfortable with the AI’s use in a handful of clinical scenarios, according to Academic Pediatrics:

• 77.6% for determining whether antibiotics were required
• 77.5% for accurate radiograph interpretation
• 76.5% for bloodwork

Challenges And Limitations of AI in Healthcare: Stats and Facts

In a review paper that conducted a meta-analysis of 33 PubMed articles, the main challenges were: ethics and data privacy, lack of technological awareness, and the unreliability of AI. (Science Direct, 2024)

To realize the full potential of AI in the healthcare sector, issues of privacy, accountability, intellectual property rights, and transparency must be taken into account as well. (Science Direct, 2024)

Racism embedded in the algorithm’s classification is also a huge concern for minorities.

64% of Black adults feel that bias based on a patient’s race or ethnicity is a major problem in health and medicine. (Pew Research, 2023)

Among those concerned, 51% believe that AI could ameliorate the unfair bias. (Pew Research, 2023)

Statistics on The Future of AI in Healthcare

According to a survey of healthcare provider executives in the United States in 2023:

Around 30% cited adopting artificial intelligence and machine learning for clinical decision support tools as a priority. (Statista, 2023)

Around 25% said an AI use case for predictive analytics and risk stratification was a priority. (Statista, 2023)

AI could also add relevant information when integrated with the standard healthcare procedures, as an example it can read and add to doctor’s notes.

AI predicts cancer patient survival by reading doctor’s notes. (UBC News, 2023)

The model also shows significant accuracy.

The model was shown to predict six-month, 36-month, and 60-month survival with greater than 80 percent accuracy. (JAMA Network, 2023)

Final Thoughts

As you have seen AI can be used to mitigate the shortages that currently befall the global healthcare system.

In the future, according to Mayo clinic, AI could help in:

• Matching patients with optimally selected clinical trials.

• Developing and setting up remote health monitoring devices.

• Detecting health conditions that were imperceptible and symptom-free before.

• Anticipating disease-risk years before the disease presents itself. (Mayo Clinic, 2024)