Top Challenges of AI in Healthcare: What Businesses Need to Resolve

Healthcare providers investing significantly in AI applications for disease diagnosis and enhancing patient care in recent times. The technology is undeniably at the forefront of transformative change in the industry. However, as businesses leverage AI for healthcare, they face substantial challenges that necessitate resolution. There are few key challenges surrounding the implementation of AI in healthcare and the imperative for businesses to address them.

Objectives of AI in Healthcare

Before dissecting the challenges, it’s crucial to understand the primary objectives of integrating AI into the healthcare sector.

  1. Increasing Effectiveness of Diagnostic Processes

AI aims to enhance diagnostic effectiveness, providing a faster and more accurate alternative to human diagnoses. The technology can analyze vast datasets, detecting and diagnosing diseases with minimal risk of error, especially in scenarios with large caseloads and insufficient medical history.

  1. Reducing Overall Healthcare Costs

Efficiency is a core focus, with AI streamlining diagnostic processes, reducing manual labor costs, and consequently lowering the overall cost of healthcare procedures. By expediting diagnosis through image analysis and data interpretation, patients can receive more effective care, minimizing the need for hospital admissions and long waiting periods.

  1. Safer Surgeries

AI’s role in healthcare robotics extends to aiding in surgery, offering less invasive procedures with benefits like reduced blood loss, decreased infection risk, and faster recovery times. Precision in delicate surgeries, facilitated by AI, results in smaller incisions and reduced scarring.

  1. Easy Information Sharing

AI’s ability to rapidly analyze vast amounts of information is crucial for precision medicine. For instance, real-time monitoring systems can assist healthcare professionals in managing urgent conditions like diabetes by swiftly interpreting data from patients’ glucose monitors.

Applying AI in Healthcare: Promising Use Cases

The active testing of AI in medical facilities spans various applications, including diagnosis, symptom prediction, and research such as drug discovery. Some notable use cases include:

  1. Diagnostic Assessment

AI examines extensive data from Electronic Health Records (EHRs), radiography, CT scans, and magnetic resonance images. By identifying patterns and associations across patients, AI aids in early symptom predictions.

  1. Virtual Health Assistants

Virtual health assistants, exemplified by platforms like and AiCure, manage routine tasks, answer patient queries, and offer a personalized experience in healthcare management.

  1. Treatment of Rare Diseases

Platforms like BERG utilize AI to map diseases, accelerating the discovery and development of breakthrough drugs and vaccines, particularly for rare diseases.

  1. Targeted Treatment

AI, especially using technologies like Deep Learning, enables targeted treatment, as seen with companies like BenevolentAI delivering precise treatment to specific patient groups.

  1. Drug Discovery

AI’s neural networks assess drug candidates’ bioactivity, expediting drug discovery by identifying optimal targets for various diseases.

Privacy Concerns With AI in Healthcare

The primary concern surrounding the application of AI in healthcare is privacy, particularly when dealing with sensitive patient data governed by regulations like GDPR and HIPAA. Data breaches, such as the reported over 6 million records breached in the U.S. as of October 2022, highlight the potential risks.

Top Challenges of AI in Healthcare

Beyond privacy concerns, several technical and methodological challenges impede the widespread deployment of AI in clinical practice.

  1. Lack of Quality Medical Data

Clinicians require high-quality datasets for AI model validation, but the fragmentation of medical data across various EHRs poses a challenge. Standardizing medical data is essential to increase the volume of data available for testing AI systems.

  1. Clinically Irrelevant Performance Metrics

The metrics used to gauge AI model success may not directly translate to clinical settings. Bridging the “AI chasm” requires collaboration between developers and clinicians to assess AI models for real-world clinical usefulness using methods like decision curve analysis.

  1. Methodological Research Flaws

Most AI studies in healthcare are retrospective, based on historical patient records. Prospective research, studying current patients over time, is crucial to realizing the true value of AI in real-world settings.

Future of AI in Healthcare

Despite current challenges, the future of AI in healthcare holds great promise. As businesses address data discrepancies, research flaws, and privacy concerns, AI has the potential to advance diagnostics, enable early symptom predictions, and revolutionize drug discovery. Current AI-based systems are already supporting healthcare organizations in streamlining workflows, indicating a positive trajectory for the technology.

Professionals aspiring to build successful careers in this evolving field should stay updated with recent advancements. Emeritus offers a diverse range of online healthcare courses covering the latest industry topics, providing a valuable foundation for those looking to advance their careers in the burgeoning intersection of AI and healthcare. As challenges are overcome, AI’s transformative potential in healthcare will continue to unfold, reshaping the industry and improving patient outcomes.


Related posts

Leave a Comment