Resources > BLOG

Types of AI in Healthcare

Types of AI in Healthcare

March 12, 2024

Artificial Intelligence: Empowering Evidence-Based Healthcare

Artificial intelligence (AI) is transforming the healthcare industry, particularly evidence-based healthcare. AI supports the integration of the best available evidence with clinical expertise, offering many opportunities to enhance patient care, improve clinical decision-making, and advance research. In the near future, AI may become a powerful tool for evidence-based healthcare that helps to efficiently identify, analyse, and disseminate scientific evidence, driving better clinical practices and patient outcomes. 

Examining and understanding emerging AI applications, such as predictive analytics, natural language processing, and machine learning, is essential to leveraging AI to enhance patient care and outcomes.

Image Generated by Leonardo AI

Evidence Synthesis: Finding and Using Evidence

One of the most significant contributions of AI in evidence-based healthcare is its ability to streamline and expedite the evidence synthesis process. Natural Language Processing (NLP), a form of AI that excels at deciphering textual data, simplifies arduous processes like compiling, analysing, and integrating research findings that aid in understanding specific medical issues. 

AI algorithms already help sift through vast amounts of medical data and aid in extracting the data  50% faster. This includes clinical trials, observational studies, and patient records that help identify valuable, high-quality evidence. This automated approach saves time and resources and helps healthcare providers access up-to-date and reliable information for their decision-making.

AI also plays a role in detecting research gaps and identifying areas of knowledge that require further exploration. By analysing patterns and trends in existing research, AI can help researchers prioritise studies and allocate resources effectively, promoting the generation of evidence that genuinely informs clinical practice.

Image Generated by Leonardo AI

Predictive Analytics for Patient Outcomes:

Predictive analytics leverages historical data to forecast patient outcomes. This type of AI equips healthcare providers with the ability to anticipate and prevent adverse events. By identifying potential risks early on, medical professionals can intervene proactively, positively impacting patient care.

Personalizing Healthcare and Enhancing Diagnosis

AI is also transforming how we personalise healthcare, making it more patient-centric and tailored to individual needs. Machine learning (ML) algorithms have emerged as invaluable tools in disease diagnosis as the algorithms can analyse vast patient data, including medical history, genetic information, and lifestyle factors, all to identify patterns and predict potential health risks. Early detection and precise diagnostic decisions are becoming increasingly achievable, and the personalised approach allows healthcare providers to intervene and prevent diseases before they occur.

Interpreting complicated medical imaging results, especially with multiple diagnoses, can be complex, but AI is beneficial here too. AI algorithms can analyse images such as X-rays, CT scans, and MRIs, detecting subtle abnormalities and anomalies that human experts might overlook. This leads to earlier detection and treatment, improving patient outcomes and reducing healthcare costs.

Image Generated by Leonardo AI

Robotics in Surgery

The correlation between AI and robotics has redefined surgical procedures. Robots with AI technologies offer unparalleled precision, enhancing surgical outcomes and faster patient recovery. This advancement represents a monumental stride towards evidence-based practices in the surgical domain.

Image Generated by Leonardo AI

Promoting Patient Engagement and Informed Decision-Making

Virtual health assistants are becoming integral components of patient care. These AI-powered assistants engage with patients, offering support, reminders, and healthcare information. The seamless integration of virtual health assistants contributes to evidence-based patient care, fostering improved communication and adherence to treatment plans.

Ethical Considerations and Ensuring Responsible AI Implementation

While the promise of AI in healthcare is immense, it is crucial to acknowledge the challenges it brings. The complexity of integrating AI systems, data security concerns such as patient privacy and the need for ongoing training pose significant hurdles to patients and healthcare professionals. 

The European Union and the United States are actively working on improving clarity and safety in AI. The strategy focuses on boosting Europe's global AI leadership through initiatives like the European AI Strategy, which promotes research, investment, and innovation while addressing potential risks. The proposed AI Act establishes the world's first legal framework for AI, setting clear guidelines for development and use. Finally, the Coordinated Plan on AI fosters collaboration between EU members and stakeholders, solidifying a united front in AI advancement. Ultimately, the EU's approach seeks to ensure AI benefits everyone and shapes a positive future for Europe. 

A Bright Future for AI in Evidence-Based Healthcare

The synergy between AI and Evidence-Based Healthcare guides a new age of patient-centric, data-driven medicine. By harnessing the power of AI, we can improve the accuracy of a diagnosis, personalise treatment plans, and enable patients to take charge of their health. As AI continues to evolve, we can envision a future where healthcare is even more precise, personalised, and informed by the best available evidence, leading to better health outcomes. By navigating the challenges and embracing the opportunities presented by AI, we can collectively contribute to the ongoing transformation that will shape the healthcare industry for generations to come.

Shelby Storme as a freelance digital marketing lead
Shelby Storme Kuhn
Digital Marketing Lead

As a passionate writer with a strong drive for strategic growth, Shelby leverages storytelling techniques to provide value for Evidence Prime's audience.

Laser AI's MSc Pharmacist, Ewelina Sadowska.
Ewelina Sadowska
MSc, Pharmacist

Evidence Synthesis Specialist at Evidence Prime. She is responsible for testing new solutions in Laser AI and conducting evidence synthesis research.

Related webinars:

No items found.

Related blog posts:

Two AI robots analyzing the difference between a scoping review vs systematic review
Attitudes towards AI in Literature Review Software

The results of the survey completed by Evidence Prime regarding attitudes and concerns about AI in literature reviews.

Two AI robots analyzing the difference between a scoping review vs systematic review
Generative AI in healthcare: ChatGPT the future of Systematic Reviews?

Discover the best software for systematic literature reviews and explore the risks of generative AI in healthcare. Learn about chat GPT vs Laser AI.