Healthcare sector is often at the forefront of adopting emerging technologies and yet again it’s embracing the ever-evolving AI to pave the way for advancements. AI brings numerous benefits and applications in healthcare, and it is becoming a helping hand for healthcare professional in providing patient care, disease diagnosis, several treatment plans, drug discovery, and even identifying patient risks.
If you’re a medical specialist or from the biomedical engineering team at a hospital and the current wave of AI and its applications have intrigued you then you must know how you can be a part of this change and bring a change to your department.
Up until now, you must know that Artificial Intelligence helps in automating the repetitive processes that humans would otherwise do. However, other than this, it can also generate valuable insights by analyzing patterns, trends, and anomalies, thus freeing up a caregiver’s time so that they can focus on more complex activities.
You can learn to use AI-powered digital healthcare technologies to streamline administrative tasks and enhance overall operational efficiency. And, in this process, you can be among the first to learn the ins and outs of the ever-emerging AI tech.
Choose from the pool of our courses to get the job roles in healthcare sector. You can transition to Data Science or become a Machine Learning Engineer using Interview Kickstart’s extensive approach. These courses will help you in decision-making and predicting the required goal.
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AIÂ Healthcare Examples
Use of Artificial Intelligence in healthcare is in abundance. It’s encouraging to observe that healthcare consistently leads the charge in adopting emerging technologies. And, this time is no different, healthcare is yet again at the forefront to harness the maximum benefits.
Large Language Models (LLMs) have successfully attracted hospitals as well to implement and use their full potential. There are models that draft responses to patient inquiries. Furthermore, an AI tool also turns a physician-patient conversation into a medical progress note.
Medical or clinical algorithms help healthcare professionals in making informed decisions. These algorithms extend to a wide range of applications, from diagnostic processes to therapeutic management and patient monitoring.
Replacing Manual Functions Using RPA
Healthcare systems low on workforce bandwidth can use AI tools to perform certain tasks like appointment scheduling, patient registration, automating billing and applying appropriate codes by extracting relevant information from medical records.
RPA stands for Robotic Process Automation is a technology that mimics human interactions with digital systems to execute complex and rule-based tasks.
Additionally, hospitals can perform compliance monitoring for regular audits to ensure compliance with healthcare regulations and policies. RPA can also facilitate automated communication with patients through appointment reminders, follow-up messages, and so on.
These examples illustrate that RPA can be leveraged across various areas of healthcare operations to improve efficiency, accuracy, and productivity.
AI-powered personalized treatment plans in healthcare
While RPA takes structured data to replace manual functions, ML and NLP take a step further by taking unstructured data and generating insights. They analyze large datasets, extract insights, do predictive analytics, medical image analysis, and clinical documentation improvement.
Medical Image Analysis AI System in Healthcare
One of the growing AI examples in healthcare is medical image analysis. The AI algorithms can analyze medical images such as MRIs, CT scans, and X-rays to assist radiologists in detecting abnormalities. For example, Google’s DeepMind has developed AI algorithms that can detect early signs of degenerative eye conditions.
It’s interesting to see how AI is used in healthcare across different fleets, thanks to giants that are working with top health agencies to come up with real-life solutions. Take for instance, NVIDIA Corporation launched Nuance Precision Imaging Network, an AI-based cloud platform that delivers patient specific insights and data from diagnostic imaging.
Furthermore, Amazon Web Services (AWS) has gone in an agreement to develop solutions in healthcare and life sciences.
Remote Patient Monitoring
Wearable devices with AI-enabled diagnostic capabilities can detect irregular heart rhythms and alert patients and healthcare providers to potential cardiac issues in real-time. ECG-AI has been combined with wearables to investigate various cardiac pathologies.
FDA recently approved an ECG smartwatch proven to detect AF (Atrial Fibrillation), abnormal heart rate rhythm.
Among other recent developments in AI in healthcare, Microsoft has announced to offer affordable, accessible, and effective healthcare solutions in partnership with Nuance Communications.
A report from GrandViewResearch gives an outlook at the AI applications for the coming years. These AI applications majorly include, robot-assisted surgery, virtual assistants, administrative workflow assistants, diagnosis, clinical trials, connected machines, cybersecurity and so on.
AIÂ in Healthcare Benefits
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AI and its techniques bring a lot of benefits to the medical industry. Artificial Intelligence encompasses a range of interconnected processes, each with its own distinct characteristics. These include Machine Learning (ML), Deep Learning, Robotic process automation (RPA), and Neural Language Processing (NLP).
Gen AI or Generative AI for instance uses deep learning techniques and is contributing to the medical industry in numerous ways. You can unlock the power of Generative AI for efficacy and scalability.
Improved Clinical Shortage
We all have witnessed healthcare systems going in crisis during the COVID-19 pandemic. It was an unsustainable situation that couldn’t be solved through mere workforce hiring alone. So technologies like AI can come to rescue and help in managing tasks so that the available workforce could work on things that require immediate attention.
We need intelligent systems that could introduce greater productivity and alleviate the pressure on people. As per a research paper published by the National Bureau of Economic Research, wider adoption of AI could lead to savings of 5% to 10% in US healthcare spendings.
At the same time, cognitive AI takes the administrative burden off the physician and the members of the care team. Just imagine a hospital that can’t recruit and retain enough staff to perform follow-ups.
Doctors who have used ChatGPT have found it a game changer. They are amazed to see how efficient AI tools can summarize a lot of the data and also offer advancements that could save time.
Aids in Cost-Effectiveness
As per a study published at NCBI, AI enables remote patient monitoring (RPM) for patients in the US. Furthermore, FDA ensures patient safety, effectiveness and transparent AI solutions. This helps in creating a full-fledged remote ecosystem for the patients.
The whole process of remote diagnosis includes a portable device or a sensor that can be remotely used by the patient or some trained assistant, cloud storage for remote monitoring, and an AI algorithm that could effectively classify and aid in decision-making.
This whole process is cost effective not only for the medical industry but for the patients living afar as well.
Saving Time and Resources
As discussed in the examples of AI in healthcare, the AI-powered administrative processes help in saving time and resources, allowing them to focus more on patient care and the tasks that require immediate attention.
The repetitive processes that include scheduling, billing, and managing health records are handled by AI-powered digital devices in the hospitals so that the staff could do other tasks.
Efficiency
Among other benefits of AI in healthcare, accuracy has been significantly enhanced, leading to more precise diagnosis. Accuracy is a critical necessity in sensitive sectors like healthcare where the wellbeing of patients is paramount.
AI systems in healthcare have proven to be accurate in terms of positive prediction.
Studies have shown that the tests done on over thousands of patients, AI yields an accuracy of over 85%. As per a study conducted by the researchers from Duke Health, three AI models trained on data from thousands of surgical cases were 13% more accurate in predicting operating room time needed for each procedure.
Continuous Learning
One of the important benefits of having AI in healthcare is that the systems can continuously learn on their own from the new data and feedback, thus improving the overall performance over time.
By leveraging ML algorithms, healthcare AI applications become more accurate, reliable, and adaptable. This helps the researchers to steer in the direction of advancements of healthcare.
Challenges of AIÂ in Healthcare
While Artificial Intelligence brings many benefits in the healthcare sector, there are challenges that need to be addressed to fully leverage the potential.
The ultimate goal of AI in medical care is to make things more personalized and interactive. But these commitments can lead to unprecedented vulnerabilities as well. Hence ethics of AI in healthcare must be preserved and standard guidelines have to be followed for the moral use.
For instance, confidentiality of medical records is crucial, thus robust scrutiny is required so that no data is compromised in the process of training the AI systems.
Data Collection Concerns
While AI has the potential to make strides in the healthcare sector, there are also risks and potential threats associated with its use. The main threat can enter from the input data which is going to generate predictions and personalized reports for the patients.
AI and ML algorithms need massive amounts of data in order to classify or predict a wide range of tasks. Patient records are confidential and not all institutes are willing to share for training AI models. Secondly, not all the data is readily available, once an algorithm has started using it.
An ML-based system is fed on constant flow of data to train themselves for better output. So, internal corporate reluctance to exchange healthcare data is a big challenge for AI in healthcare.
Additionally, biased software is the result of unbalanced data being input into algorithms. AI replicates the racial, gender, and age prejudice which are already ingrained in our society. Cultural prejudices and misconceptions get embedded in technological development as well which leads to biased outcomes.
Algorithms Developments Concerns
The AI algorithms used in the medical industry are sophisticated but that doesn’t mean they are perfect. Just like any other conventional systems that are deployed in the medical care units, AI systems can produce distorted outcomes even though they are more intelligent.
Too many variables can influence the results, leading the algorithm to make inaccurate predictions. It can be possible that a model was working fine at the time of training but provided inaccurate results when projecting future events.
AI systems have a so-called “black-box†problem, where they sometimes lack the ability to provide convincing explanations for their forecasts. This means these systems can’t defend themselves if the predictions go wrong.
Ethics of AI in Healthcare
The use of AI in healthcare also raises ethical concerns. Another main problem after data privacy and security is accountability. This makes the role of AI in healthcare concerning. While a hospital can point to the makers of those AI-powered systems, ultimately, it boils down to the mistake of a machine which is otherwise considered intelligent after humans.
This problem may not be as concerning as it is in the healthcare sector where the stakes are high. Issue of responsibility becomes much higher when we talk about AI and ethics in healthcare.
So, these are some of the major disadvantages and concerns of AI in healthcare. However, it’s an ever-evolving technology which will continue to bring more benefits in the field of medical sciences.
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