How AI in Healthcare is gaining momentum and revolutionizing the industry.

As AI systems become more sophisticated, industry leaders are identifying opportunities where AI in Healthcare can be most effective.

A Forrester study found that 86% of health and life sciences decision-makers believe the ability to effectively leverage tech, data, and AI will define success over the next five years. At the same time, 60% said their companies aren't moving quickly enough to adopt AI.

AI is already transforming the healthcare industry and revolutionizing patient care, diagnosis, treatment, and operational efficiency. Organizations that implement AI now will see exciting progress in patient engagement and outcomes while gaining a distinct advantage over those that are slower to adopt new tech.

AI in Healthcare is the application of artificial intelligence technology within the medical field. AI employs machine learning (ML) algorithms to analyze large amounts of patient data, enabling healthcare professionals to make faster, more accurate diagnoses, develop personalized treatment plans, and improve patient care through risk identification and optimized operations. Essentially, AI mimics human intelligence for better medical decision-making.

AI also automates routine tasks, boosts efficiency, and gives healthcare providers the time and resources to focus on patient engagement and deliver personalized care. But it goes beyond simply eliminating tedious paperwork. The latest AI systems are advanced enough to offer entirely new capabilities for healthcare organizations, such as improving clinical trials , medical diagnoses and treatment, and self-care management.

While AI is a broad and ever-growing field, a few specific types of AI are especially relevant in healthcare today.

As the name suggests, generative AI takes a set of data and uses it to create something new when prompted by a human. Humans type prompts that pre-trained large language models (LLMs) use to generate new content. The accuracy and usefulness of any generative AI tool depends on the quality of data provided to the algorithms and the specificity of the prompt.

For example, if you want a generative AI tool to write an email to a patient with post-discharge instructions, the quality of the result will vary based on the data it has to reference. General LLMs will likely produce less specific and less accurate results than those trained on healthcare-specific data. If the prompt is too broad, such as, "Write an email with discharge instructions to a patient," then the resulting email won't be nearly as helpful as one generated by a prompt informed by the patient's medical records.

AI is now more widely used than ever because it has become more accessible to non-AI experts. Although AI has been studied since the 1950s, using and manipulating AI systems previously required specialized knowledge and training. Now, tools like conversational AI allow humans to use everyday language to "talk" to AI — eliminating the need for complicated coding or other specialized knowledge. Rather than requiring humans to be fluent in computer languages, computers can now understand and respond in human languages. With conversational AI, users can pose questions to AI systems as they would to a trusted colleague. This has significantly enhanced efficiencies and access to information for healthcare providers, medical staff, and patients.

AI agents are intelligent systems that serve as digital assistants. Healthcare professionals can use conversational language to ask AI agents questions or have them complete certain tasks. Much like a human assistant, these agents can be integrated into key workflows to handle complex tasks without direct supervision or intervention. Humans can easily build custom agents — using low-code tools like Agentforce — to complete tasks and continuously improve as they learn on the job. In healthcare, AI agents can perform tasks such as generating patient assessments, summarizing a patient's medical history, and helping patients with finding the right providers for their treatments.

Machine learning is often mistaken as synonymous with AI, but it's actually an application of AI. It uses algorithms with rules for how a machine — typically a software system — interprets and understands data. ML algorithms are trained on datasets from which they learn how to find patterns and predict future outcomes.

ML and predictive analytics are transforming healthcare in various ways. This includes simplifying administrative tasks, such as identifying patients likely to miss appointments or be readmitted to the hospital after discharge, which enables healthcare providers to make more informed decisions about patient care and resource allocation. ML and predictive analytics can also forecast diseases, personalize treatments, and optimize healthcare delivery.

The Forrester study also found that 73% of respondents said automating tasks to improve productivity was a top driver of AI in their organizations. Improving productivity can result in quicker and more accurate patient diagnoses, including early disease detection and improved patient outcomes.

While humans excel at creative problem-solving, such as asking pointed questions to reveal hidden information about a patient's history, intelligent machines can find patterns that humans often overlook. Here are two examples of how AI is transforming patient diagnosis and treatment:

Radiology is an area particularly well-suited for AI enhancement. For example, medical professionals can train an algorithm by providing it with thousands of mammogram results. The algorithm can detect patterns in the images and then quickly determine whether a new image contains cancerous cells. AI can flag areas of concern in imaging tests so that humans can conduct further examinations. A study published in the scientific journal Diagnostics found that AI-driven diagnostic systems reduce false positives and improve radiologists' efficiency.

In addition to imaging, AI can also help provide personalized care by using insights from a patient's electronic health records and published literature to create custom treatment plans. While it would take a physician hours to search through scientific journals for relevant information, AI can significantly accelerate the process. AI can be automated to operate 24/7, enhancing productivity — an efficiency that allows healthcare providers to deliver more personalized care plans.

There is still plenty of room for the widespread adoption of AI in Healthcare. When Forrester asked healthcare decision-makers about barriers to AI adoption, 57% cited a lack of internal skills and expertise, 49% blamed a lack of defined budget, and another 49% said they were unsure how to implement AI solutions. Let's explore three ways to address this:

1. Define AI use cases: With so many ways to implement AI in healthcare, organizations must decide where it will best meet their unique needs and goals. Before planning for technology, review data to identify the areas where you may be lacking and need support. For example, if your organization is spending too much time and budget on administrative tasks, consider investing in AI agents to support patient services.
2. Identify cost-saving opportunities: Cost savings can ease decision-makers' concerns about spending their budget on AI tools. AI can support teams in various ways from streamlining operations to reducing errors to analyzing large data sets to uncover patterns and make predictions that improve patient outcomes. With AI handling routine administrative tasks, healthcare professionals are free to focus on high-priority items, boosting overall productivity for organizations.
3. Provide training and resources: Access to training and resources can help overcome resistance to AI. Knowledge of standards and regulations will help healthcare providers as they seek guidance on handling protected health information (PHI). Partnering with a trusted tech software company that prioritizes data safety and security can also help ease anxiety for new AI users.

More than 80% of healthcare leaders believe organizations that effectively adopt AI will become more efficient and agile. This is likely because AI can identify inefficiencies and optimize resource allocation. For example, predictive analytics can forecast patient admission rates and suggest the most efficient schedules to match hospital staff and diagnostic and therapeutic equipment availability with anticipated patient needs.

Another top reason organizations are considering AI is to create more personalized interactions, as 79% of decision-makers believe that organizations adopting AI will improve patient/member communication and experiences. An AI-powered healthcare CRM can unify patient data from multiple sources and help providers create tailored care plans. This includes personalized emails reminding patients about upcoming appointments and other helpful care plan information.

It's not just theoretical or futuristic; healthcare organizations already report improvements in patient engagement and satisfaction. According to a Harvard Business Review Analytic Services report, AI-powered insights and content personalization have helped organizations increase patient and provider satisfaction scores by 30%.

There are several concerns about privacy, security, and data protection for AI in healthcare. The Forrester study found that 61% of decision-makers in healthcare and life sciences believe that ethical and privacy concerns are barriers to AI adoption, while 54% cited the risks of inherent bias or inaccuracies of AI output.

Because AI relies on past data to make predictions and decisions, inaccurate or biased data can lead to flawed decisions that may negatively impact patient care. This is why addressing algorithmic bias is essential for ensuring fair and equitable outcomes. It's also why human involvement is critical to AI implementation and management.

The World Health Organization (WHO) cautions that ethical considerations, regulations, standards, and governance mechanisms must be central in the development and deployment of AI systems . The organization recently stated, "Improved transparency and fail-safes are needed to ensure safety, consistency, and quality in artificial intelligence systems for health, while promoting trust." This is why it's important to select a trusted AI software provider that protects your data and your patients' data while complying with data protection laws.

AI is accelerating drug discovery as researchers use tools to sift through millions of candidates to find the best novel compounds and pinpoint potential drug targets. Generative AI helps connect the dots between different research studies and could reveal opportunities for repurposing well-tolerated drugs in different contexts, potentially speeding up the process of finding effective treatments.

AI also shows great potential for improving clinical trial efficiency and effectiveness. Life science companies can use AI to save time by sifting through previous research and simulating clinical trials. Plus, AI-powered tools can help automate the recruitment and enrollment process while streamlining communication with eligible candidates. AI can also assist in compiling reports and reducing some of the time-consuming aspects of regulatory submissions.

Most organizations are still in the early stages of AI implementation, but momentum is growing. AI isn't going to change the healthcare system overnight. Still, organizations are experiencing incremental improvement, from automating manual administrative tasks to matching eligible participants with trials, personalizing patient communications, and more. With the right technology, training, and strategies, healthcare organizations of all sizes can expect to experience significant benefits from AI.

Learn more about what Healthcare AI can do for your organization.

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