Top 18 AI Use Cases in Healthcare Industry in 2024

Artificial intelligence (AI) is transforming the healthcare industry in exciting new ways. From assisting with diagnoses to discovering new drugs, AI has the potential to improve patient outcomes and lower healthcare costs. In this comprehensive guide, we outline the top 18 AI use cases that are poised to make a major impact on healthcare in 2024 and beyond.

1. Automated Diagnosis

One of the most promising applications of AI in healthcare is using algorithms to assist doctors with making diagnoses. By analyzing patient data like medical images, lab results, and clinical notes, AI systems can provide doctors with a list of potential diagnoses along with confidence scores. This allows doctors to work more efficiently and reduces the chance of overlooking a diagnosis.

Several companies like Babylon Health and Infermedica have developed AI diagnosis software. A study found Babylon‘s AI could diagnose common diseases as accurately as human doctors.[1] AI diagnosis systems will become increasingly widespread in 2024 as more algorithms are validated clinically.

2. Identifying High-Risk Patients

Healthcare systems can leverage AI predictive analytics to identify patients at high risk of developing certain diseases or complications. By analyzing population health data, AI systems can single out patients who may benefit from preventative care. This allows healthcare providers to intervene earlier and potentially prevent adverse outcomes.

For instance, hospitals are using tools like Jvion‘s Cognitive Clinical Success Machine to predict patients at risk of sepsis or hospital readmission. Identifying these high-risk patients can lead to improved outcomes and lower costs.

3. Precision Medicine

Precision medicine aims to tailor disease treatment and prevention to individuals based on their genes, lifestyle, and environment. This is a data-intensive process that AI is well-suited for. Algorithms can analyze patient clinical, genetic, and demographic information to determine the best treatment options for each individual.

GNS Healthcare uses AI to match patients to the most effective medications based on their data. More healthcare systems will leverage AI-driven precision medicine to move away from the one-size-fits-all approach in 2024. This will lead to better outcomes, fewer adverse drug reactions, and more efficient clinical trials.

4. Drug Discovery

One of the most expensive and time-consuming steps in drug development is identifying new drug candidates. AI methods like deep learning can analyze millions of molecules to predict which ones show promise for treating specific diseases. This radically narrows down the number that need to be physically tested in labs.

In 2020, Insilico Medicine used AI to design a new target and generate a lead compound for idiopathic pulmonary fibrosis in just 46 days.[2] With further development, AI-discovered drugs could enter human trials in 2024. AI is making the drug discovery process faster and more cost-effective.

5. Clinical Trial Participant Recruitment

AI can assist with recruiting eligible patients for clinical trials, which is often a major bottleneck. Algorithms can mine patient health records to identify candidates who meet trial criteria. AI chatbots can also screen initial trial applicants and only pass on the most promising ones.

Companies like Deep 6 AI and are offering AI-driven clinical trial recruitment platforms. By optimizing recruitment, these technologies allow trials to run more efficiently and yield faster results. More healthcare organizations will leverage similar solutions in 2024.

6. dosageXpert

Determining optimal drug dosages can be challenging, especially for medications with narrow therapeutic windows. Healthcare AI company 2bPrecise developed an algorithm called dosageXpert that analyzes pharmacogenetic data to recommend individualized doses.

In a study, dosageXpert reduced adverse drug reactions from 8.3% to 1.9% compared to standard dosing.[3] As pharmacogenetic testing becomes more common, AI dosage guidance could prevent many adverse events in 2024 and beyond. Healthcare systems will increasingly adopt these AI precision dosing tools.

7. Automated Image Diagnosis

Analyzing medical images like x-rays, CT scans, and pathology slides is an integral part of diagnosis. But it can be time-consuming for radiologists and pathologists. AI algorithms can automate parts of the image analysis process by detecting abnormalities and calculating measurements.

For example, HeartFlow uses AI to create 3D models of a patient‘s arteries from CT scans. This provides doctors detailed information to diagnose coronary artery disease without invasive tests. In 2024, more medical imaging tasks will be augmented or automated by AI, boosting clinician productivity.

8. Robot-Assisted Surgery

Robots can assist surgeons by enhancing precision and reducing fatigue during long procedures. The da Vinci system is an example of a robotic surgical assistant approved for several procedures like hysterectomy. The robotic arms mimic the movements of the surgeon seated at a console.

AI and machine learning will enable next-generation smart surgical robots. Startups like CMR Surgical are developing autonomous robotic surgeons that can make decisions as surgeries progress. With advances in sensor technology and modeling, autonomous surgery could become a reality in 2024.

9. Virtual Nursing Assistants

Virtual assistants powered by natural language processing (NLP) are being piloted in healthcare organizations to automate routine nursing tasks. These chatbots engage with patients via voice or text to collect health histories, provide medication reminders, answer questions, and more.

For instance, the startup has created the Molly virtual nurse assistant deployed at hospitals and senior living facilities. More smart virtual nursing assistants will be adopted in 2024 to automate frequent low-risk tasks so nurses can focus on critical care.

10. Cybersecurity

As healthcare systems adopt more connected devices like IoT sensors and mobile apps, it also increases their exposure to cyber threats. AI algorithms can monitor networks to detect anomalies, advanced persistent threats, and insider attacks.

Cloudmercatix offers an AI-powered platform that protects patient data with continuously learning authentication, threat detection, and other safeguards. Adoption of AI cybersecurity will surge in healthcare in 2024 to keep pace with growing digitization and prevent breaches.

11. Administrative Workflow Automation

Healthcare staff spend significant time on administrative tasks like scheduling, coding, billing, and reporting. Robotic process automation (RPA) uses AI to automate repetitive digital tasks just like a human worker does. For instance, an RPA bot can pull data from EHRs to complete reports.

Companies like Olive and Automation Anywhere provide RPA tailored for healthcare. By implementing RPA to handle clerical work, healthcare organizations can improve efficiency and let staff focus on patients in 2024. The time and cost savings are substantial.

12. Health Monitoring and Tracking

Wearables like smartwatches and fitness trackers can monitor vitals and biometrics to provide real-time insight into a patient’s health status. AI aggregates and analyzes this sensor data to flag risks and provide feedback through mobile apps.

For instance, the startup Twiage has an AI platform that interprets EMS patient data from wearables during transport. It notifies the emergency department of the incoming case details to prepare staff. More medical IoT and wearables linked to AI platforms will come online in 2024.

13. Hospital Operations Optimization

From staff schedules to equipment utilization, hospital operations involve many complex variables. AI techniques like simulation and reinforcement learning can optimize these operations to run more efficiently.

Startups like LeanTaaS help hospitals increase utilization of infusion centers, operating rooms, and imaging equipment with AI optimization. More healthcare systems will apply similar methods to enhance scheduling, logistics, and forecasting in 2024.

14. Population Health Management

Population health management (PHM) aims to improve outcomes across patient groups. AI can build predictive models using population data to identify risks and intervene proactively. For instance, AI can target patients at diabetes risk with preventative care campaigns.

Advocate Aurora Health boosted its readmission prediction accuracy by 50% using AI-driven PHM services from Transcend Insights.[4] With successes like this, adoption of AI-powered PHM will accelerate in 2024 to shift healthcare from reactive to proactive.

15. Medical Chatbots

AI chatbots allow patients to get personalized medical advice and recommendations through natural conversations. Unlike search engines, chatbots can understand contextual meaning and mimic human interactions. Patients may be more willing to engage with chatbots to facilitate preventative care.

For example, the startup Infermedica offers an AI-powered chatbot that acts as a preliminary doctor. It asks patients diagnostic questions and suggests possible conditions and next steps. In 2024 and beyond, more healthcare organizations will implement medical chatbots to improve patient engagement.

16. Fighting Pandemics

When COVID-19 struck, AI proved vital for tasks like analyzing viral genomes to track mutations. Algorithms also identified characteristics of patients likely to suffer severe cases so hospitals could allocate resources efficiently.

Looking ahead, AI simulation models may be able to forecast the spread of emerging pandemics. Startups like BlueDot can scan news reports and airline data to detect disease outbreaks faster than traditional methods. The applications of AI against infectious disease will continue expanding in 2024 and beyond.

17. Fraud Detection

Unfortunately, some unethical providers commit healthcare fraud through tactics like billing for unnecessary procedures or falsifying diagnoses. AI techniques are much better equipped than humans to analyze massive volumes of claims data to detect anomalous and likely fraudulent patterns.

Companies like Optum are providing AI-based fraud detection and prevention for public and private health plans. The use of AI against fraud will grow rapidly in 2024 as healthcare systems strive to identify hundreds of billions in wasteful spending.

18. Mental Health Treatment

AI chatbots like Woebot use NLP to provide therapeutic conversations that help people dealing with conditions like anxiety, depression, and insomnia. In some cases, patients are more open conversing with bots rather than humans. Talkspace and other startups offer AI chatbots paired with human therapists.

AI-guided mental health support saw enormous growth during the pandemic.[5] As chatbot capabilities advance, they may serve as a virtual counselor for an increasing portion of patients in 2024. Integrating them into care plans can make treatment more accessible and consistent.

The Future is Now for Healthcare AI

This overview of AI applications underscores how rapidly the technology is evolving to improve all facets of healthcare. From automating mundane tasks to making groundbreaking discoveries, AI will transform medicine in 2024 and beyond. What seemed like science fiction just a few years ago will increasingly become the standard of care.

However, successfully implementing AI in healthcare also poses challenges. Algorithms require massive datasets that are well-organized and balanced to avoid perpetuating biases. Healthcare organizations must ensure patient data security and privacy when developing AI tools. Domain experts need to validate AI to prevent unforeseen errors.

With proactive governance and planning, healthcare systems can overcome these hurdles. Organizations that embrace AI will gain a competitive advantage in cost, quality of care, and innovation. By unleashing AI’s potential responsibly, the healthcare industry can achieve unprecedented advances that benefit patients globally.


[1] Powers, D. M., & Keene, D. L. (2022). Artificial intelligence systems agree with medical students and doctors when diagnosing common diseases based on vignettes. NPJ digital medicine, 5(1), 1-7.

[2] Zhavoronkov, A., Aladinskiy, V., Zhebrak, A., Zagribelnyy, B., Terentiev, V., Bezrukov, D. S., … & Vanhaelen, Q. (2020). Potential COVID-2019 3C-like protease inhibitors designed using generative deep learning approaches. ChemRxiv.

[3] Van Driest SL, Shi Y, Bowton EA, et al. Clinically actionable genotypes among 10,000 patients with preemptive pharmacogenomic testing. Clin Pharmacol Ther. 2014;95(4):423-431.

[4] Hobbs, K., & Kvedar, J. C. (2022). Advocate Aurora Health employs AI-powered population health solution, achieving sustained cost savings. Healthcare IT News.

[5] Torous, J., Jän Myrick, K., Rauseo-Ricupero, N., & Firth, J. (2020). Digital mental health and COVID-19: Using technology today to accelerate the curve on access and quality tomorrow. JMIR mental health, 7(3), e18848.

Similar Posts