While AI has made significant advancements in healthcare, it's important to state that AI cannot fully replace doctors in all aspects of medical practice.
While AI systems can augment and enhance certain tasks, they lack the critical thinking, empathy, and nuanced decision-making abilities that are essential in complex medical situations. Here are some key points to consider:
Diagnostic Assistance: AI can assist doctors by analyzing large datasets and helping identify patterns that may be challenging for humans to detect. This can improve the accuracy of diagnoses, especially for conditions with clear data-driven markers.
Treatment Recommendations: AI can suggest treatment options based on evidence from vast medical literature and patient data. However, the final decision should be made by a qualified medical professional who considers the patient's unique circumstances.
Predictive Analytics: AI can predict patient outcomes and risk factors, allowing doctors to proactively address potential issues. This helps in personalized patient care and resource allocation.
Administrative Tasks: AI can handle administrative tasks, such as appointment scheduling and medical record management, freeing up doctors to focus on patient care.
Repetitive Procedures: In some cases, AI-controlled robotic systems can assist in performing repetitive and precise surgical tasks, reducing the margin for human error.
A team of experts in the Laboratory for Respiratory Diseases at the Catholic University of Leuven, Belgium, trained an AI-based computer algorithm using good quality data. Dr. Marko Topalovic, a postdoctoral researcher in the team, announced that AI was found to be more consistent and accurate in interpreting respiratory test results and in suggesting diagnoses, as compared to lung specialists.
Likewise, Artificial Intelligence Research Centre for Neurological Disorders at the Beijing Tiantan Hospital and a research team from the Capital Medical University developed the BioMind AI system, which correctly diagnosed brain tumor in 87% of 225 cases in about 15 minutes, whereas the results of a team of 15 senior doctors displayed only 66% accuracy.
With further improvements and the support of other advanced technologies like machine learning, AI is getting smarter with time. The introduction of technologies such as deep learning and artificial intelligence in healthcare can help achieve more efficiency and precision. Doctors can now rely on AI to diagnose and suggest treatments while handling multiple patients. If AI keeps getting better at the current rate, we might be looking at a future where patients will no longer need to wait for the doctors and AI would diagnose and prescribe medications correctly.
How Can Artificial Intelligence Help with Healthcare?
Deep learning has made AI capable of learning from new data and produce accurate results. AI has the potential for reducing healthcare costs by a staggering percentage. Accenture has predicted that artificial intelligence could save healthcare organizations $150 billion annually by the year 2026. Hence, multiple tech startups and healthcare organizations are deploying artificial intelligence in healthcare for various applications. Some of these applications are:
Diagnosis of critical disorders such as cancer is a tedious task that involves looking for malignant cells among millions of healthy ones. In fact, the task of diagnosing severe disorders is so meticulous that many patients suffer due to misdiagnosis. According to MedicalXpress, various studies suggest that around 12 million Americans are affected by misdiagnosis, which can lead to serious physical injuries or sometimes even death. But, by using artificial intelligence in healthcare, the situation can be better. AI is capable of image analysis and the diagnosis of serious illnesses. If large amounts of good patient data are provided for training an AI system, it can perform better than humans in the diagnosis of critical disorders. Furthermore, AI can automatically read and interpret image scans without human intervention.
AI-based systems are capable of diagnosing tumor properties with images and eliminate the requirement of tissue samples for diagnoses. Image analysis can prove especially useful in remote areas where doctors are not readily available and treatment can be started after a quick diagnosis. To make the technology even better, tech giant Nvidia is teaming up with several medical organizations to create AI that can train other AI to scan images for malignant tumors.
2. Patient Care
After being diagnosed with any critical disease, a patient needs help in monitoring their condition. Moreover, the patient may have doubts about the symptoms and the treatment, which can only be clarified by a doctor. But, AI-powered chatbots are able to diagnose based on symptoms and suggest an immediate course of action. For example, a London-based online subscription healthcare provider offers an AI-based consultation using patients’ medical history and common medical knowledge. Additionally, the service also provides a live video consultation with a doctor, who can diagnose and write prescriptions or recommend a specialist. Hence, chatbots are capable of diagnosing illnesses and helping patients decide if they need to visit a doctor immediately or not.
3. Drug Discovery
Developing pharmaceutical medications requires decades of clinical trials and billions of dollars. During major epidemics, patients die much before the medicines are even tested. But, with artificial intelligence in healthcare, existing drugs can be scanned and repurposed to fight diseases. During the Ebola outbreak, Atomwise created an AI program that found two medicines which can be used to reduce the effect of Ebola in one day. The ability of AI to find solutions quickly can prove beneficial in saving lives during the time of crisis. Therefore, multiple organizations are teaming up with healthcare experts to invest in developing effective AI systems that can discover drugs for multiple disorders.
4. Predictive Analysis
Another great application of artificial intelligence in healthcare involves predictive analysis for at-risk patients. By collecting vast amounts of patient data, AI can analyze and identify at-risk patients. Moreover, AI and IoT can play a role in transforming healthcare by enabling patients to monitor their condition and notify the patient and the doctors if something seems wrong. For example, IBM Watson Health has created AI-powered Sugar.IQ app, which is a diabetes assistant that monitors the blood glucose levels with the help of a continuous glucose monitoring system. The data displayed on the app can help patients understand and analyze their glucose levels and regulate their food intake and insulin dosages accordingly. Thus, apps and services based on AI and machine learning can help notify medical experts about the health of at-risk patients.
5. Brain Computer Interfaces
Numerous people lose the ability to speak, move, and interact with their surroundings due to accidents or neurological disorders. Researchers have been struggling to figure out a way to recover the abilities of the severely affected patients. But, with artificial intelligence, brain computer interfaces can decipher the neural activities that are related to physical functions. Brain computer interfaces will enable patients to use devices such as smartphones and computers. Patients can communicate with doctors and family members and give feedback for the treatment.
What are the Challenges for Artificial Intelligence in Healthcare?
As advanced as artificial intelligence is right now, the technology still falls short in multiple instances. Currently, artificial intelligence needs tremendous amounts of patient data to work efficiently, which can be inconvenient for mainstream applications of artificial intelligence in healthcare. Furthermore, artificial intelligence can interpret image scans only if the data fed to the system is good quality data and in a large amount. The performance of AI systems depends on the quality of the data fed to the system. Hence, if the quality of data is poor, AI systems will generate inaccurate and biased results. Furthermore, the data used for AI applications needs to be updated constantly with new health cases, and after updating the data the system needs to be tested before undertaking any patient’s diagnosis.
Lack of Regulations
Similar to any major industry, the healthcare industry is highly regulated. But, regulations around artificial intelligence in healthcare are failing to keep up. There is a dire need for regulations to update with modern technology. With better regulations, AI systems have the potential to be used efficiently, without any misuse. And, the concerned authorities need to understand that better regulations will help save more lives and make the healthcare industry more reliable.
If AI makes a mistake in diagnosing a life-threatening disease like cancer, then who takes responsibility for that? What should one do if AI makes a false prediction about a severe illness? Who should an individual turn to when AI misdiagnoses and a patient suffers? Questions like these need to be answered before deploying artificial intelligence in healthcare. Healthcare organizations need to maintain standards to facilitate the safety and reliability of AI. Moreover, independent organizations should monitor the applications of AI and make sure that AI cannot be used unethically.
How Can the Healthcare Industry Prepare for AI?
Healthcare organizations need to realize that AI is going to be widely used in healthcare applications. Healthcare organizations must prepare for the introduction of artificial intelligence. For successful deployment of AI, it is essential that medical practitioners understand the importance of AI and learn about how AI will help the healthcare sector. The patient data stored on the hospital database should be accurate to produce error-free outcomes. And the hospital databases should be updated to integrate with artificial intelligence systems seamlessly. Healthcare organizations need to take appropriate measures to ensure data privacy and secure patient data from unauthorized users. Moreover, healthcare organizations need to create better regulations for cautious and ethical use of artificial intelligence in healthcare.
With the introduction of advanced technologies such as big data and artificial intelligence in healthcare, diagnosis and treatment of severe disorders is becoming quicker and more effective. And, by discovering new drugs the chances of losing loved ones to life-threatening diseases will be reduced. Therefore, healthcare organizations need to see AI as a companion that can make the industry only better.