Artificial Intelligence (AI) is revolutionizing healthcare, with its global market projected to grow from $14.6 billion in 2023 to $102.7 billion by 2028, representing a remarkable CAGR of 46.7%.
AI is already transforming various areas of healthcare, including medical imaging, oncology, cardiology, radiology, research companions, diagnostics, and administrative services.
Physicians are benefiting from AI by gaining enhanced capabilities in predicting future illnesses, determining survival rates, and prescribing personalized medications. This shift towards AI-driven healthcare emphasizes a proactive and preventive approach.
However, significant challenges, such as patient data privacy and collaboration between AI developers and clinical staff, impede the progress of AI in healthcare. These challenges can be addressed through the development of robust AI algorithms and machine learning models, aided by advancements in technology.
Looking ahead to 2030, AI is poised to access diverse data sources, unveiling disease patterns and facilitating improved treatment and care.
5 Key Areas of AI Transforming Healthcare:
1. AI and Emergency Department
Unnecessary visits to Emergency Departments (ED) by patients who do not require urgent care can lead to numerous issues, including overcrowding, medical errors, prolonged waiting times, and unnecessary tests. Differentiating between emergency and non-emergency cases quickly and accurately is a challenge for doctors.
Fortunately, a study suggests that AI can help address this issue, potentially avoiding up to 35% of ED visits. The cumulative expense of such visits, including unnecessary ones, amounts to approximately $22 billion. By leveraging AI, it is estimated that around 50% of these costs can be effectively saved.
To achieve greater cost savings, the utilization of AI involves the development of robust models capable of processing vast volumes of medical and patient databases. These AI models have predictive capabilities, which can be instrumental in analyzing factors such as Medicare costs and demographic information to enhance efficiency and optimize resource allocation.
2. Connected and Predictive Care
During the COVID-19 pandemic, AI has played a crucial role. It facilitated vaccine development by expediting the process in the absence of extensive clinical trials and studies. Moreover, the integration of the Internet of Medical Things (IoMT) and personal healthcare diagnostic kits and wearables are creating a globally connected healthcare ecosystem.
Wearable devices monitor real-time data such as heart rates, glucose levels, hygiene, depression, moods, and specific diseases like Parkinson’s. AI effectively handles the immense volume of real-time patient data, enabling valuable insights.
AI models encompass a combination of technologies, including Deep Learning, Machine Learning Algorithms, and Natural Language Processing. These models leverage large datasets to generate analytics and intelligence. This diagram helps a better understanding.
AI models revolutionize healthcare by establishing a connected and predictive global system. Their potential extends to averting pandemics, mitigating severe health risks, and enabling early-stage prediction, detection, and treatment.
3. Research and Development
The majority of drugs developed in labs never reach the market. Deloitte’s research reveals that innovator drugs take an average of 10-12 years for approval, with costs exceeding $2 billion. Out of 5,000 drug candidates screened, only 5 proceed to clinical trials, and ultimately, only 1 receives approval for human use. AI is poised to significantly reduce drug development timelines and costs, offering a crucial solution to these challenges.
4. Medical Imaging
AI has revolutionized medical imaging by offering more precise diagnoses and expanding the capabilities of pathologists. With advanced AI, conditions and diseases in patients can now be diagnosed with unprecedented accuracy. Additionally, AI has the potential to provide treatment options, further enhancing patient care.
AI is used widely in three key areas of healthcare.
4.1. Advanced medical imaging
AI-enabled medical imaging can identify medical conditions faster and more accurately. Researchers at Tulane University discovered that AI could detect colorectal cancer more accurately than pathologists. AI-based advanced imaging helps identify the thickening of certain muscle structures or changes in blood flow in the heart. Heart attacks can be predicted in advance.
4.2. Precision Medicine
In a Stanford University study, pathologists trained computers using a machine learning algorithm to differentiate two types of lung cancers more accurately and predicted the patient’s survival rates.
4.3. Risk Prediction
AI helps predict risks of potential conditions and diseases. In an NIH study, physicians were able to detect a risk of heart attack with AI screening techniques and clinical data analysis.
5. Cancer Radiation Therapy
Radiation therapy is a critical cancer treatment that utilizes high radiation beams to destroy cancer cells and shrink tumors. The timeliness of this therapy is crucial. Traditionally, it takes about a week to review clinical data and create optimal treatment plans. However, for some patients, immediate radiation therapy is necessary. Studies show that delaying therapy by a week can increase the risk of cancer recurrence or metastasis from 12~14%.
AI could significantly reduce turnaround times, from weeks to days, by leveraging its analytical capabilities on vast amounts of clinical data. AI provides more accurate dosage recommendations. Recent NIH research indicates that AI can swiftly generate the best treatment plans from patients’ clinical data. Furthermore, it can accurately recalculate dosages before each session, considering the patient’s response from the past and recent dosages.
Conclusion
AI is revolutionizing healthcare, offering unparalleled advantages. Nevertheless, certain challenges are to be addressed, such as risks to patient data privacy, potential neglect of social values by AI systems, and concerns about job losses. However, considering the extensive benefits and advancements it brings to human health and well-being, AI integration in healthcare is inevitable.
References
- Market research report: https://www.marketsandmarkets.com/Market-Reports/artificial-intelligence-healthcare-market-54679303.html
- AI & Emergency Department: https://www.mckinsey.com/industries/healthcare/our-insights/applying-machine-learning-to-help-improve-outcomes-and-identify-care-needs-before-ed-visits
- Advanced medical imaging: https://healthitanalytics.com/features/how-can-artificial-intelligence-change-medical-imaging
- Wearables: https://ordr.net/article/iot-healthcare-examples/
- AI learning: https://blog.sierralabs.com/demystifying-ai-in-healthcare
- Internet of Medical Things: https://link.springer.com/book/10.1007/978-3-030-63937-2
- Computer-aided pathological study of lung cancers (Stanford University): https://med.stanford.edu/news/all-news/2016/08/computers-trounce-pathologists-in-predicting-lung-cancer-severity.html
- Predicting heart attacks (NIH): https://www.nih.gov/news-events/nih-research-matters/artificial-intelligence-predicts-heart-disease-risk-ct-scans
