How AI is Helping Doctors: The Benefits of AI in Healthcare

Healthcare leaders contend with regulatory requirements and a lack of data standardization that can make implementing AI challenging. But once implemented, healthcare AI has shown to be a valuable tool for many healthcare organizations.

Forrester found that while 19% of healthcare decision-makers said their department is still learning and gathering information about AI, with no active program in place, only 3% expect to still be in the learning stages within 18 months. And 10% expect to apply AI across their entire enterprise within two years.

With implementation on the rise, we'll explore how AI is helping doctors, improving patient care, and modernizing the healthcare industry.

The Association of American Medical Colleges projects that the United States will face a physician shortage of up to 86,000 physicians by 2036. Advanced AI technologies such as machine learning, natural language processing, and generative AI can help close this gap and reduce the burden on doctors.

At its core, AI takes on the tedious tasks, freeing doctors to focus on what matters most — patient care. It's also introducing new capabilities that enhance diagnostics, streamline workflows, and improve personalized treatment plans. Together, these advancements can help healthcare providers meet growing challenges like rising costs, labor shortages, and increasing demand for personalized care.

Forrester found that 79% of healthcare decision-makers described AI as having a large or transformational impact on provider customer service. They also found that AI is transforming providers' risk management, appointment scheduling, the Internet of Medical Things , and predicting patient outcomes. Meanwhile, AI is revolutionizing operations for medical technology companies and life sciences and pharmaceutical companies by enabling predictive data analysis, drug discovery, and clinical trials.

One of the benefits of AI in healthcare is how it's helping doctors — both directly and indirectly. While industries like manufacturing lean on AI to reduce the physical load, in healthcare, it's more about reducing the mental toll of certain processes. Let's look at some key examples:

• Improves diagnostic accuracy: Machine learning (ML) algorithms can sift through large amounts of data to identify patterns that humans may miss. For instance, radiologists analyze millions of medical images each year, and a type of ML called deep learning has shown exceptional accuracy in analyzing medical images , helping doctors detect conditions earlier and informing preventive care strategies.
• Personalizes patient experiences: AI in medicine has created opportunities for more personalized patient care. One example of this is in oncology, where it can be used to indicate a tumor's exact border. By combining image analysis with patient data, doctors can create personalized treatment plans that accounts for each patient's unique anatomy and disease.
• Makes data-backed predictions: Predictive analytics help doctors choose treatments likely to succeed, while life sciences companies can better predict clinical trial outcomes and identify risk markers.
• Automates administrative tasks: AI can work around the clock, handling time-consuming tasks that help support care delivery. Virtual assistants powered by tools like Agentforce can answer FAQs, remind patients of appointments, summarize a patient's demographics and medical history, and even monitor chronic conditions.
• Accelerates drug discovery: In life sciences and pharmaceuticals, advanced algorithms can streamline everything from supply chain management and order fulfillment to target prioritization and literature review. A Harvard Business Review Analytic Services report adds that AI also supports participant recruitment and regulatory document generation.

• In medicine, people's lives are at stake, and there are some ethical concerns surrounding AI. Here are a few of the most urgent:
• Data privacy and security: Without proper protections, sensitive patient data could be vulnerable to identity theft, insurance fraud, and other risks. Doctors should never use patient data with unsecured AI tools, like publicly available chatbots.
• Inaccuracies: AI "hallucinations"— when systems generate incorrect or misleading information — can result from issues like insufficient training data, incorrect assumptions, or processing errors. While inconvenient in other industries, inaccuracies in medicine could have life-altering consequences.
• Lack of transparency: Some AI systems function as a "black box," offering results without explaining how they were derived. Healthcare organizations must choose solutions that provide citations for AI-generated content to ensure transparency, credibility, and trust.

While skepticism remains, healthcare organizations can take practical steps to build trust and overcome challenges associated with AI.

Salesforce research found that 61% of customers believe rapid AI advancements make it even more essential for companies to build trust. And Forrester adds that even though AI adoption is set to expand in healthcare and life sciences, many organizations are unprepared, especially when it comes to ensuring their data is structured and ready for AI systems.

Here are a few challenges and solutions to consider when implementing AI in a healthcare setting:

Preparing data for AI involves two key steps: ensuring it's accessible from various sources (such as medical devices or patient files) and structuring it in a way AI models can use. For instance, numbers alone don't mean much — but tagging those numbers as blood pressure readings for a specific patient prepares them for meaningful AI analysis.

Partnering with trusted AI vendors can make it easier for healthcare organizations to maintain compliance and build patient trust. Look for vendors that offer strict guardrails to align with regulatory and compliance laws, such as Health Cloud, which has a proven track record of securely handling sensitive data.

Healthcare providers should establish clear governance standards to ensure AI tools are used ethically and safely. Formalizing these rules and sharing them with staff and patients helps reduce bias and reassures those skeptical of AI. Guidelines should include checks and balances, such as requiring human review of AI-generated results — just like a supervisor double-checks their team's work.

Training the people who use AI is just as important as training the AI itself. For example, teaching staff to write precise prompts can help reduce generative AI hallucinations, while training them to recognize false outputs ensures issues are caught before they cause harm. Also, training AI on company-specific data improves accuracy but may introduce biases based on the dataset. Healthcare organizations should be mindful of these biases when making data-driven decisions.

According to Forrester, healthcare is still in the early stages of AI adoption. Only 6% of decision-makers say AI currently performs complex tasks (with human intervention), but this number is expected to grow. Within 18 months, 25% of leaders expect AI to handle complex tasks at their organizations.

As adoption increases, AI has the potential to revolutionize the healthcare industry by improving diagnostic accuracy, accelerating drug discovery, automating patient services-related tasks, and enabling doctors to provide more personalized care and achieve better patient outcomes.

Discover how AI can help your organization with Health Cloud.

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