The introduction of AI in healthcare is advancing medical research and enabling faster, more accurate diagnoses and personalized treatments. This improves patient outcomes.
With AI technology, healthcare systems have also become smarter and more efficient. However, deploying real-world AI applications to production environments can be challenging. For example, almost none of the machine learning tools developed to tackle COVID-19-related challenges had a significant impact.1
Challenges of AI in healthcare
Despite its benefits, integrating AI into healthcare can pose threats to healthcare organizations:
Data collection challenges
Accessing relevant data is a significant challenge for AI in healthcare. Massive datasets are required for effective machine learning (ML) and deep learning (DL) models, but healthcare data is often confidential, and institutions can be reluctant to share it.
Privacy concerns and regulatory constraints further limit the availability of data. The quality of data is another issue, with medical records often being inaccurate and incomplete.
Innovative ways of data annotation can help overcome this challenge. Investing in privacy-enhancing technologies can also help re-assure people of data protection while gathering and working with sensitive medical data.
Ethical challenges
Accountability is the primary ethical dilemma with AI in healthcare. AI systems, particularly in critical areas like medicine, operate as “black boxes,” making it difficult to trace how decisions are made. This raises concerns about responsibility in cases of errors or poor outcomes.
Additionally, the absence of universal ethical guidelines for AI use in healthcare complicates its adoption.
Social challenges
AI’s impact on employment in healthcare has sparked fears of job displacement, leading to skepticism and resistance. There is a common misconception that AI will replace human workers, when in reality, it is more likely to complement and re-engineer existing jobs.
However, exaggerated expectations about AI’s capabilities can lead to disappointment if it doesn’t meet the public’s hopes.
Public dialogue is essential to manage these concerns and foster a balanced understanding of AI’s potential.
What are the best practices of AI in healthcare?
To optimize AI investments in healthcare without wasting funding or building ineffective tools, here are the best practices that healthcare leaders should focus on:
- Making targeted investments in high-value use cases.
- Prioritizing explainable AI systems.
- Testing AI models on medical diagnoses.
- Providing training to healthcare workers and patients.
- Understanding and communicating the benefits of AI clearly for organizational alignment.
Invest in high value use cases of AI in healthcare
It is important to leverage machine learning in areas where it can provide the most effective solutions:
- Good fit for machine learning: Most of these areas that present complex problems and large volumes of related data.
- Good fit for rules-based solutions: Simple problems with known solutions.
- Humans: Domains with limited data and complex problems. However, approaches like few shot learning can change this in the future.
Prioritize leveraging explainable AI systems
A hybrid approach where doctors and the AI tools work together is common for AI implementation. This can be an issue if the doctors do not understand how the system works.
For example, if a mammogram analyzing medical vision system detects cancer and the radiologist does not know how or why that system detected that cancer, it could put lives at risk. For more, read explainable AI.
Test AI models throughly
When integrating AI into healthcare, it is critical to test AI models to ensure their accuracy, reliability, and safety. AI systems are highly capable of identifying patterns in medical data, however are not infallible.
Errors can cause serious problems, especially in scenarios involving patient diagnoses, treatments, or interventions. Here are the some of the key ways to test AI models in healthcare:
- Use diverse and representative data: AI models should be tested on a variety of datasets that reflect the full range of patient demographics, conditions, and medical scenarios.
- Conduct validation testing: Validation test helps ensure the model’s predictions are generalizable and not overly reliant on specific subsets of data.
- Simulate clinical settings: AIModels should be tested in simulated clinical environments that would mimic real-world usage. This would help identify potential pitfalls that might not emerge in controlled lab environments.
- Monitor false positives and negatives: Pay attention to the rates of false positives and false negatives. In healthcare, both types of errors can lead to adverse outcomes. For example, a false positive in cancer screening may lead to unnecessary procedures, while a false negative could delay critical treatment.
Train healthcare workers and patients
Another challenge is the hesitation of healthcare workers in accepting AI. Training and educating healthcare workers can eliminate this misconception.
Patient reluctance is another major challenge in implementing AI in healthcare. At first glance, a robotic surgery might scare the patients, but as they understand and learn the benefits, the hesitations might fade away. Therefore, properly educating the patients is very important to overcome this challenge.
To learn more about the challenges of implementing AI in the healthcare sector and how to overcome them, check out benefits and challenges of AI in healthcare.
Communicate benefits of AI in healthcare
The global market for AI in the healthcare sector has been growing and creating awareness is the first step in broadening the impact of AI initiatives. Here are some ways AI has benefited the medical field:
More accurate and efficient diagnostics
Misdiagnosis is a big issue in the healthcare sector. According to a recent report, around 12 million people in the US are misdiagnosed annually, and 44% of those are cancer patients.2 AI is helping overcome this issue by improving diagnostic accuracy and efficiency.
Digital medical solutions such as computer vision offer accurate analysis of medical imaging, including patient reports, CT scans, MRI reports, X-rays, mammograms, etc., to extract data that is not visible to the human eyes.
While AI can be faster and more accurate than radiologists in analyzing most medical data, it is still not mature enough to completely replace radiologists.
Therefore, MIT has created a machine learning system based on a hybrid approach that can diagnose different types of cancers by analyzing medical reports or refer the task to an expert radiologist.3
Better patient care
As the demand for healthcare facilities increases and the supply remains limited, it becomes harder to maintain good overall patient care.
According to a study, 83% of patients find poor communication as the worst part of the patient experience.4 AI can help overcome this challenge in the following ways:
- Automating patient communication through AI can eliminate time consuming tasks such as appointment management, reminders, and payment issues. The time saved from these tasks can be spent caring for the patients, which is the main purpose of healthcare professionals.
- AI can also quickly analyze data, obtain reports, and direct patients to the relevant doctors.
For more, feel free to check out AI consulting, data science consulting, and healthcare AI consulting.
Improved surgical procedures
AI delivered by healthcare robotics is making surgeries safer and smarter. Robotic-assisted surgery enables surgeons to achieve higher precision, safety, flexibility, and control in complicated surgical procedures.
It also enables remote surgery, which can be performed from anywhere in the world in areas where surgeons are not available. This is also applicable during global pandemics when social distancing is necessary.
Research published by Harvard Medical School5 compared the differences between conventional and robot-assisted prostate cancer surgery. The study found that the robot-assisted patients had:
- Fewer post-surgery complications such as blood clots, urinary infections, and bladder neck contracture.
- Shorter lengths of hospital stay after the procedure,
- Lower pain scores after surgery,
Figure 1: Robotic surgery example.6
More benefits of AI in healthcare
Other benefits of integrating AI technologies to healthcare can be divided into three categories:
For individuals, the benefits of AI in healthcare include automated decision-making, patient monitoring (especially for elderly care), early diagnosis, and the simplification of clinical workflows.
AI technologies can effectively analyze complex, heterogeneous clinical data, while generating valuable insights that assist in medical diagnosis and treatment applications.
It also supports healthcare professionals in managing chronic conditions, reducing unnecessary patient visits, and conserving resources through AI-assisted health coaching, ultimately improving patient care and patient outcomes.
For healthcare organizations, AI offers advantages such as cost reduction, fraud detection, and workflow optimization.
AI helps healthcare providers manage clinical workflows by normalizing and integrating healthcare data, enabling more efficient knowledge management.
Additionally, it helps reduce healthcare costs, optimizes resource allocation, and detects fraudulent claims, thereby contributing to financial savings across the healthcare industry. For instance, AI can assist in revenue cycle management and improve health outcomes by providing insights derived from electronic health records (EHR systems) and patient data.
At the sector level, AI contributes to saving time, reducing resource consumption, and enhancing medical professionals’ training through AI systems.
It can accelerate medical diagnosis, improve clinical decision making, and foster collaborative healthcare delivery by improving data accessibility across healthcare systems.
AI also plays a critical role in healthcare research by supporting innovations such as precision medicine, drug discovery, and clinical trials.
Moreover, AI technology aids in the analysis and generation of medical images and deep learning applications, to support clinical areas like cancer diagnosis and medical imaging. To learn more, check out generative AI in healthcare.
How does AI in healthcare work?
AI in healthcare industry is a broad term that refers to the use of machine learning algorithms to imitate human cognition to analyze, understand and present complex medical data:
Machine learning has transformed healthcare by improving medical diagnosis and treatment through data analysis. Algorithms can identify patterns in clinical data, helping predict outcomes and discover new therapies.
AI technology enables more precise disease diagnosis, personalized treatments, and early detection of potential health issues. Deep learning also plays a key role in tasks like speech recognition, which would enhance the use of AI in clinical settings.
Natural language processing (NLP) enables computers to interpret and use human language and has been transforming healthcare by improving diagnosis accuracy and managing clinical processes.
It helps extract useful information from medical records, identify relevant treatments, and predict potential health risks. NLP also aids clinicians in managing large amounts of complex data more efficiently.
To better understand how AI is changing healthcare check out AI Use Cases in Healthcare Industry, where we list use cases in patient care, medical imaging and diagnostic, research & development, and healthcare management.
Also, you can read intelligent automation in healthcare to explore how you can automate healthcare processes with intelligent bots.
External Links
- 1. Hundreds of AI tools have been built to catch covid. None of them helped. | MIT Technology Review. MIT Technology Review
- 2. “Global Misdiagnosis insides – medical error statistics by countries“. CloudHospital. Retrieved October 4, 2024
- 3. “AI systems that work w/doctors and know when to step in“. MIT CSAIL. Retrieved October 4, 2024
- 4. “How AI is reshaping the future of the patient-provider experience“. MedCity News. Retrieved October 4, 2024
- 5. “Comparing traditional and robotic-assisted surgery for prostate cancer“. Harvard Health. Retrieved October 4, 2024
- 6. “Ireland’s Leading Private Hospital“. Mater Private Network. Retrieved October 4, 2024
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