Author: Harshit Gupta, Sanskruti Mishra & Chaitanyakumar
Artificial Intelligence (AI), playing a strong and emerging role in the past few decades. What one doesn’t realize is AI presents itself in many forms that impact lifestyle. All your technical activities on social media, e-mail, car ride services, and online shopping platforms all involve AI algorithms to enhance user experience. One major area where AI is growing fleetly is the medical field; specifically, in diagnostics and treatment operations.
The biggest advantage of AI and healthcare technologies is that it helps to deliver faster and accurate results. PathAI, a number one company within the healthcare sector using AI, is enabling pathologists to form accurate diagnoses by reducing errors.
How is AI Used in Healthcare?
Healthcare facilities that analyze properly and reach conclusions that save patients’ lives. AI in healthcare corporations builds Machine Learning algorithms, NLP-based solutions, and Deep Learning capabilities to analyze huge amounts of data.
For example — analyzing a patient’s anamnesis and taking a look at reports will facilitate corporations to deliver better care by understanding patterns in health. They can identify whether somebody is more prone to some diseases, has any issues which will cause future sicknesses, or shows symptoms of something terminal.
Applications of AI in Healthcare
AI is revolutionizing the healthcare industry with various applications to analyze large datasets of unstructured data, providing doctors efficient insights on any patient’s health, resulting in better and more personalized healthcare delivery outcomes. Here are some applications:
- Drug development:
Medical practitioners and researchers often conduct tests and examinations for developing drugs that will help patients recover better and faster. Machine Learning in healthcare helps researchers in drug development by browsing many data points that assess the feasibility of medicine. It includes identifying the simplest chemicals for the composition of a drug and its impact. Through Machine Learning and AI in healthcare, researchers can prepare different compositions and test their feasibility without risking the lives of human participants. While conducting drug trials, AI machines can also monitor the various effects of the drug on participants and gather the info to supply valuable insights into the method.
- Treatment design:
Artificial Intelligence assists doctors in maintaining every individual patient’s data and reports into large, complex data sets. It also gathers the external test files and other clinical expertise that helps them to understand the case and individually customize the treatment path. Importantly this data is often analyzed in a significantly fast and precise way which is more cost-effective too. These are much better than the conventional analytical methods which reduce speed and improve outcomes. Clinical deciding has always relied on statistical insights. Today however AI gives scientists the facility to uncover complex associations within data sets that cannot be uncovered through equation-based statistical analysis.
- Medical imaging:
Machine learning algorithms can process massive amounts of data, during a heartbeat and supply more accuracy than humans. An AI algorithm could access X-rays and other images for evidence of opacities that indicate a particular disease. The AI system goes through plenty of images and scans of the body to spot any symptom of the disease that’s being diagnosed.
It allows the doctors to start the treatment before anything major takes place. AI systems that aid in detecting cancer are becoming a life-saver for patients everywhere on the planet. They’re increasingly becoming a tool to fight the disease and save more lives.
- Brain-Computer Interfaces:
One of the foremost attention-grabbing advantages of AI within the care market is the usage of Brain-computer interfaces that align with the medical system and permit patients to talk, hear and communicate if they need to lose that ability. The brain-computer interfaces area unit is supported by AI services. They help in decryption, the medical activities related to hand movements, listening, and gestures, assist individuals to communicate identical methods as others do.
- Robotic surgeries:
AI and collaborative robots have revolutionized surgeries in terms of their speed and depth while making delicate incisions. Since robots don’t get tired, the difficulty of fatigue within the middle of lengthy and crucial procedures is eliminated. AI machines are capable of employing data from past operations to develop new surgical methods. The preciseness of those machines reduces the chance of tremors or any unintended or accidental movements during the surgeries.
Threats of AI in Healthcare
- Errors and injuries:
One of the largest risks that AI in healthcare holds is that the AI system might be wrong at times, as an example, if it suggests a wrong drug to a patient or makes a miscalculation in locating a neoplasm in a radiology scan, which could result in the patient’s injury or dire health-related consequences. While errors are made by human medical professionals as well yet what makes this crucial is that an underlying error, an error in an AI system may lead to injuries for thousands of patients.
- Data availability:
Large volumes of data from sources like electronic health records, pharmacy records, insurance claims records, or consumer-generated data like fitness trackers or purchase history are needed to train AI algorithms. However, health statistics are often problematic.
Since the data is fragmented and patients often see different providers or switch insurance companies the data gets complicated and less comprehensible as a result of which the risk of error and the cost of data collection escalates.
- Privacy concerns:
The demand for huge datasets creates incentives for developers to acquire data from a large number of patients. Some patients may be concerned that this data collection would infringe on their privacy, and lawsuits have been filed as a result of data sharing between large health institutions and AI companies.
Patients may see this as a breach of their privacy, particularly if the AI system’s conclusions were made available to third parties like banks or life insurance firms.
- Shifts in the profession:
In the long run, the employment of AI systems could lead to shifts in the medical profession. Particularly in areas like radiology where most of the work gets automated.
This raises the concern that a high degree of employment of AI might lead to a fall in human knowledge and capacity over the years, making providers fail in detecting AI errors as well as in the further development of medical knowledge.
How AI can Improve Healthcare
First of all, AI in the healthcare industry will lead to better decision-making. As a medical facility, it bodes well to process massive amounts of data that can show exactly what’s wrong with a patient’s health, symptoms of emerging diseases, and identify the actual status of a patient’s health. Once medical institutions have such information, they can make decisions on when to start the treatment, proper healthcare plans, and precautions the patient should take.
Until now, AI in healthcare companies works on certain parameters that are used to diagnose, analyze, and generate insights. According to Insider Intelligence, 30% of healthcare costs are associated with administrative tasks. AI can automate some of these tasks, like pre-authorizing insurance, following up on unpaid bills, and maintaining records, to ease the workload of healthcare professionals and ultimately save them money.
AI can analyze big data sets pulling together patient insights and leading to predictive analysis. Quickly obtaining patient insights helps the healthcare ecosystem discover key areas of patient care that require improvement.
Wearable healthcare technology also uses AI to better serve patients. Software that uses AI, like FitBits and smartwatches, can analyze data to alert users and their healthcare professionals on potential health issues and risks. Being able to assess one’s health through technology eases the workload of professionals and prevents unnecessary hospital visits or remissions.
Clinical Collaboration in Development
When a health system decides to adopt AI, the data science teams should involve key stakeholders (e.g., quality improvement team members and clinicians) from the start. Clinicians often don’t join the AI process until after the development stage. As a result, the data scientists don’t glean clinicians’ unique perspectives about what AI models and information would be valuable and practical in a busy clinical setting.
Data scientists and clinicians should work together in the development stage to define their goals for using AI in healthcare and understand how the data can help them achieve that goal. Realizing what data is available, limitations of current data, and potential biases in the data help the AI-adoption team identify opportunities to improve model results through data quality improvement.
The Future of AI in the Healthcare Industry
With the collection of massive amounts of data through wearable devices and out-patients departments, Artificial Intelligence would become inevitable to make sense of all that data. The role of artificial intelligence in the healthcare market would expand beyond generating insights and reach the level of taking actions along with the professionals.
It would also ensure that healthcare facilities reduce the errors and patients are less troubled. Service is another area that would see a major improvement in the coming years with the help of Artificial Intelligence. Robots would be deployed to reach an accident spot or critical zone of patients that require immediate attention.
Conclusion
The healthcare industry has a lot of work to do before it becomes a turn-key consumer experience. However, AI has shown the potential to fast-track this process and deliver a superior patient experience across the care continuum.
While this technology still looms over certain layers of dangers and perils. Artificial Intelligence tools can aid the medical industry in enabling faster service, more accurate diagnosis, and data analytics for detecting trends or genetic information that would expose someone to a particular disease.
Many technologists and AI/ML practitioners strive for a bright future, where the power of AI algorithms benefit billions of common people to improve their basic health and well-being.
References:
[1] https://www.botreetechnologies.com/blog/artificial-intelligence-in-healthcare-industry/
[2] https://www.healthrecoverysolutions.com/blog/the-growth-of-artificial-intelligence-ai-in-healthcare
[4] https://www.brookings.edu/research/risks-and-remedies-for-artificial-intelligence-in-health-care/