As the global pandemic highlighted weaknesses in the healthcare sector, many healthcare service providers and governments opted for digital solutions to overcome their challenges. Artificial intelligence (AI) is revolutionizing almost every sector and healthcare digitalization is no exception. However, deploying real-world AI applications to production environments is challenging. For example, almost none of the machine learning tools developed to tackle COVID-19-related challenges had a significant impact.
How should healthcare leaders optimize their AI investments to benefit from new developments without wasting funding or building ineffective tools? They need to
- make targeted investments in high-value use cases
- understand and communicate the benefits of AI clearly in the organization for organizational alignment
- follow best practices such as setting clear milestones in their AI initiatives to maximize the benefits of AI within their organization
What does AI mean for healthcare?
AI in the healthcare sector 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.
Invest in high value AI use cases in healthcare
It is important to use machine learning in areas where it can provide effective solutions.
- Good fit for ML: 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.
To understand how AI is changing healthcare, you can read AI use cases/applications in healthcare, where we list use cases in patient care, medical imaging and diagnostic, research & development, and healthcare management. Also, check our article on intelligent automation in healthcare to explore how you can automate healthcare processes with intelligent bots.
Communicate benefits of AI in healthcare
Healthcare sector is being transformed by AI. The global market for AI in the healthcare sector reached $6 billion in 2021 however most of its current impact is not clear to practitioners. 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. AI is helping overcome this issue by improving diagnostic accuracy and efficiency.
Digital medical solutions such as computer vision, enabled with AI, 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.
Better patient care
As the demand for healthcare facilities increases and the supply remains limited, it becomes harder to maintain good overall patient care.
A recent study showed that 83% of patients find poor communication as the worst part of the patient experience. AI can help overcome this challenge in the following ways:
- Automating patient communication through AI can eliminate tedious tasks such as appointment management, reminders, payment issues, etc. 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.
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 School compared the differences between conventional and robot-assisted prostate cancer surgery. The study found that the robot-assisted patients had:
- Shorter lengths of hospital stay after the procedure
- Lower pain scores after surgery
- Fewer post-surgery complications such as blood clots, urinary infections, and bladder neck contracture
See how robot-assisted surgeries work
Better information sharing
Another benefit of implementing AI in the healthcare sector is its ability to process large amounts of patient data.
For example, 10+% of the US population has diabetes. Tools like the FreeStyle Libre glucose monitoring system powered by AI can allow patients to track their glucose levels in real-time and access reports to manage their progress with doctors and support staff.
To learn about specific use cases of AI in the healthcare sector, check out our comprehensive article.
Follow best practices to overcome the challenges of AI in healthcare
Implementing AI in the healthcare sector is not easy; it requires smart investments and strategic planning. Here are some ways to overcome challenges that healthcare professionals might face while implementing AI in their facilities.
- Prioritize explainable systems: A hybrid approach where doctors and the AI tools work together is common for AI implementation. This can be a problem 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 our research on explainable AI.
- Test throughly: AI-enabled medical diagnoses are accurate; however, not perfect. AI systems can make errors that can lead to dire consequences. Testing your AI models more is a good way of increasing accuracy and reducing false positives.
- Utilize innovative ways of data annotation: Gathering training medical data can be a challenge due to privacy and ethical constraints in the healthcare sector. This process can be expensive and time-consuming, even when automated. Innovative ways of data annotation are helping 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.
- Provide training to healthcare workers: Another challenge is the hesitation of healthcare workers in accepting AI. AI is perceived as a threat to replace human jobs. Training and educating healthcare workers can eliminate this misconception.
- Educate to reduce patient reluctance: 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.
Watch this to get a better idea.
You can also our list of AI and data annotation tools and services:
- AI Consultant
- AI/ML Development Services
- Data Science / ML / AI Platform
- Data Annotation / Labelling / Tagging / Classification Service
- Medical Image Annotation Tool
- Video Annotation Tools
- 43 Healthtech AI vendors by area of focus & geography
- Digitizing Healthcare: Customer-centric Health Services
- Top 4 AI Use Cases in the Pharmaceutical Sector
- Top 7 Computer Vision Use Cases in Healthcare
If you have more questions, feel free to contact us:
Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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