Benefits of AI & Machine Learning in Healthcare: Transforming Patient Care

Artificial Intelligence (AI), one of the most recent advancements in healthcare, is stealing headlines in this rapidly growing digital world. 

With the rise of AI, healthcare organizations are hoping for custom software solutions to meet the specific needs of every patient. There are many benefits of AI that help achieve the quadruple aim of healthcare. This includes:

• enhancing the patient experience, 
• improving population health, 
• reducing costs and 
• Improving providers’ work life.

As healthcare resources are not available 24/7. This could create frustrations and even cause conditions to worsen. That’s why proper application of AI in healthcare is a necessity to bridge the gap between care and accessibility.

What Is Artificial Intelligence?

AI refers to machines designed to perform tasks that typically require human intelligence, such as learning, decision-making, and problem-solving. To put it simply, AI helps computers become “smart” enough to perform tasks without the need of a person at every little step.

What Is Machine Learning?

Machine learning is a subset of AI that uses algorithms to learn and analyze vast amounts of data. Instead of giving instructions on what to do, machines use previously collected data to analyze and process different forms of data, such as text, audio, images, and video, to detect different patterns and improve their performance over time.

Some of the most common types of AI algorithms include:

• Computer vision algorithms: It enable systems to interpret visual data, analyze objects, and even recognize faces.
• Reinforcement learning algorithms: It train systems to make decisions by rewarding them for successful actions.
• Natural Language Processing (NLP) algorithms: It help machines understand and generate human language.
• Deep learning algorithms: It specialize in tasks like image and speech recognition.

By using both AI and machine learning, computers can learn to get better at things, solve problems, and make predictions, all on their own.

Benefits of AI and Machine Learning in Healthcare

1. Improving Diagnostics

Healthcare organizations are using AI for medical diagnosis through things like images, patient histories, and lab results. It gives much quicker and sometimes even more precise results than humans. Through machine learning, AI can identify subtle patterns that might go undetected by humans, helping doctors in accurate and swift diagnoses.

Example: AI algorithms use tools like DeepMind’s eye disease detection software, which can examine retinal images and detect over 50 types of eye conditions.

2. Personalized Treatment Plans

Every patient has unique genetics, comorbidities, and lifestyles. Thus, it’s necessary to make treatment plans that are tailored to individual patients’ characteristics. By analyzing data through machine learning, AI algorithms can assist clinicians to provide treatments that are more likely to work for that specific patient.

Example: IBM Watson for Oncology helps oncologists design personalized cancer treatment plans by analyzing patient data along with medical research and clinical trial info.

3. Predicting Medical Risks

AI doesn’t just help doctors treat health problems. It can also show which illnesses patients might get in the future. AI predictive analytics uses machine learning to get better over time. When it gets new data, it can spot early warning signs and help doctors act early, reducing the risk of bigger issues later.

Example: AI can look at patient data to figure out the chances of heart disease, diabetes, or problems after surgery, letting doctors step in before things get worse.

4. Clinical Decision Support

AI uses machine learning to look at different medical papers, like research papers, past patient records, and clinical trials. By using evidence-based practice, it helps healthcare professionals make informed decisions with confidence.

Example: AI systems can predict possible drug interactions or suggest treatments based on recent medical research.

5. Automating Routine Tasks

It takes a lot of time updating patient records, scheduling appointments, or managing other paperwork. With AI, you can automate these routine tasks. This not just saves you time but also lets the doctors focus more on taking care of the patient.

Example: Nuance’s Dragon Medical, an AI-powered voice assistant tool that can transcribe doctor-patient conversations into electronic health records so doctors don’t have to waste time on paperwork.

6. Remote Monitoring and Telemedicine

Thanks to AI, which made it easy to monitor patients and provide assistance remotely. AI processes the data from devices like telemedicine platforms and wearables to give real-time medical support for the patient. This helps reduce hospital visits and alerts doctors if there’s a critical change in the patient’s health, especially for those with chronic conditions.

Example: Philips’ HealthSuite Digital Platform uses AI to monitor patients’ vital signs remotely. This allows early intervention and reduces hospital readmissions.

7. Facilitating Medical Research

AI makes medical research faster by quickly sorting through big datasets, spotting trends, and connecting dots that would take human researchers years to find. This helps doctors keep up with new discoveries and explore new ways to treat illnesses.

Example: Atomwise, one of the solutions for medtech, uses AI to speed up the process of screening molecular compounds for drug discovery against potential disease.

8. Offering Second Opinions

AI minimizes the chances of misdiagnosis by acting like a ‘second opinion’ to doctors. It compares their diagnosis with the enormous database of medical knowledge and cases. This gives extra confidence to the doctors in making a diagnosis.

Example: PathAI can provide second opinions on pathology slides. This is helping pathologists confirm their diagnoses, especially in some rare or tricky cases.

CHALLENGES WITH AI INTEGRATION IN HEALTHCARE

Using artificial intelligence in medicine comes with a lot of challenges and obstacles. 

1. Ethical Concerns

For AI integration to be successful, it must adhere to the 4 main pillars of medical ethics, i.e., beneficence, nonmaleficence, autonomy, and justice. These ethical concerns arise especially in the areas of decision-making, such as diagnostics and treatment planning.

To deal with ethical concerns and reduce biases in AI systems, it’s necessary to teach healthcare providers about the benefits of AI along with the limitations. AI algorithms must be used fairly by regularly checking for any bias. There must be transparency about how the decision is made and accountability for AI-driven results.

2. Financial barriers

Using AI in healthcare comes with financial challenges too. A huge investment at the start is the main barrier. This includes:

• training staff to use the technology, 
• buying AI systems, 
• Setting up the computers 
• tools to manage data,
• Integrating AI into existing systems
• and regular updating of AI algorithms to ensure lack of bias

To cross these barriers, every healthcare organization using AI must plan strategically, work with collaboration, and make smart investments.

3. Data security

There are always data security and privacy concerns for every healthcare organization using AI. These organizations contain huge amounts of sensitive information, which could be the target for cyberattackers. The chances of data breaches are high in healthcare settings until and unless they incorporate HIPAA-compliant software into their systems.

Healthcare organizations must implement methods like robust encryption techniques, regular audits, access control, and employ training, all to tackle security breaches.

4. Resistance to adoption

For every invention, there are always some groups of people who will be hesitant to bend towards change. A big challenge in incorporating AI as part of the digital transformation in healthcare is its adoption. Healthcare professionals fear their job security and changes in work routine or may not have enough knowledge to feel comfortable using it.

Every healthcare organization using AI must provide solutions like proper education and training of the staff, listen to their concerns, and show how AI will ease their work to overcome this resistance.

5. Insufficient data

Lastly, another hurdle in using AI in healthcare settings is not having enough quality data. AI algorithms need a huge amount of data to make accurate predictions and recommendations about the patient’s health.

To address this challenge, organizations must use machine learning in healthcare to collect, store, and analyze high-quality data. Using tools like wearables and remote patient monitoring devices can help in the quality training of AI algorithms.

AI, ML & GEEKS OF KOLACHI

We are the present as well as the future when it comes to building a healthcare AI app. We use technologies like machine learning, NLP, computer vision, and neural networks to build AI-based software.

As an AI apps provider, we create healthcare software solutions that are designed specifically for the wellness and healthcare industry. With our apps, physicians can monitor a patient’s health status in real-time, leading to more accurate and better care.

Reach out to us for the facilities that use cutting-edge medical technology and expand your healthcare business now.