How AI is changing the landscape of the healthcare industry

Artificial Intelligence (AI) has revolutionized the healthcare industry, enabling practitioners to leverage innovative data processing techniques and real-world applications of generated insights. By combining methods of gathering data with AI, healthcare providers can offer personalized experiences and automated insights to improve patient outcomes. The recent COVID-19 pandemic has only accelerated the adoption of AI in healthcare, leading to promising discoveries and outcomes.

Furthermore, AI has opened up new opportunities for researchers to conduct millions of experiments by simulating chemistry with computers, speeding up the drug discovery process, and identifying more compounds that could pass regulatory processes. In India, data analytics and AI in healthcare are expected to boost the country's GDP by $25-$30 billion by 2025, making healthcare more accessible to all sections of the population, including the most remote areas.

The health tech industry is experiencing significant breakthroughs in AI, generating renewed interest in its potential to shape the future of healthcare. With the increasing digitization of health records through Electronic Health Records (EHRs), AI implementation has become more feasible and presents significant opportunities to improve patient outcomes. In essence, AI in healthcare is a powerful tool that drives constant improvements across the care continuum, and its potential for transforming the healthcare industry is enormous.

How is AI revolutionizing the healthcare sector?

Increased diagnostic efficiency?

AI improves diagnostic efficiency, aids in automation, and enables early diagnosis through pattern recognition. It also supports remote monitoring via wearable health devices. The global wearable health tech industry is expected to grow at a CAGR of 28.1% from 2022-2030. Early AI adopters have already experienced advancements in healthcare.

Faster drug discovery and development

AI is revolutionizing drug testing by analyzing billions of compounds in just a few weeks, saving years of research time and reducing costs. The healthcare sector benefits greatly from AI, as it enables faster and more efficient medicine development cycles. AI integration with R&D is increasing, as it can quickly analyze large datasets for insights. The pandemic has accelerated the adoption of AI in clinical trials and patient care. By generating high-level cognitive insights faster, AI reduces time-to-market and eases the patient burden

Transformed Drug supply chain

AI is transforming the biopharma supply chain by providing real-time insights into the complex needs of stakeholders such as drug manufacturers, healthcare providers, government agencies, health insurance organizations, and patients. AI solutions enable the analysis of various stages of the supply chain, including inventory consumption and raw material procurement, to optimize the value chain and ensure timely access to therapies for patients. With AI, the biopharma industry is poised to streamline its operations and meet the diverse and time-sensitive needs of all stakeholders.

Empowered patients and improved treatment follow-through

Companies in the healthcare industry are increasingly focusing on building patient-centric AI systems, especially with the rise of telehealth during the pandemic. With patients having access to more channels for managing their health, there is a need for AI-powered tools to help them make sense of their data. One such tool is an intelligent chatbot that provides 24/7 customer service and personalized care. By leveraging AI-powered chatbots, doctors and patients can save time and money. Additionally, AI and machine learning can be used to personalize treatment methods and follow-up procedures for individual patients, ensuring better compliance and treatment outcomes.

Advancements in AI Integration for Healthcare

As AI continues to evolve in healthcare, it's crucial to integrate it seamlessly into the clinical workflow. In India, the healthcare sector is rapidly expanding, with significant investments from both the government and corporations. By utilizing AI and other digital advancements, healthcare providers can prevent disease, accurately diagnose patients, detect changes in medical conditions, and personalize treatment plans. As AI becomes more widely accepted, it can become the go-to option rather than a costly last resort.

Challenges and limitations of AI in healthcare, What Businesses Need to Resolve It?

Privacy Concerns With AI in Healthcare

The healthcare industry faces significant privacy concerns when it comes to utilizing AI. Patient data contains highly sensitive Personally Identifiable Information (PII) such as medical history, identity information, and payment information, which are subject to strict regulatory guidelines provided by GDPR and HIPAA.

The vast amount of data required by most AI systems poses a significant challenge to healthcare AI adoption, as it increases the risk of data breaches. For instance, the HIPAA Journal regularly reports on healthcare data breaches in the U.S., with over 6 million records breached as of October 2022.

Lack of Quality Medical Data

To effectively validate AI models, clinicians need access to high-quality datasets. Unfortunately, the fragmentation of medical data across multiple electronic health record systems and software platforms poses a significant challenge in collecting patient information and images for testing AI algorithms. Additionally, interoperability issues may render medical data from one organization incompatible with other platforms. To address these challenges and increase the amount of data available for testing AI systems, the healthcare sector must focus on standardizing medical data techniques.

Clinically Irrelevant Performance Metrics

The metrics used to measure the success of AI models may not be applicable in clinical settings, resulting in a gap between the technical accuracy of AI tests and their clinical effectiveness, known as the AI chasm. To bridge this gap, developers and clinicians must work together to explore how AI algorithms can improve patient care. One way to achieve this is by using decision curve analysis to assess the accuracy of AI models. By comparing datasets and estimating the likelihood of success in the real world, this method allows them to evaluate the clinical utility of a prediction model.

Methodological Research Flaws

The lack of established methodologies, prospective research, and peer-reviewed studies on AI in healthcare is a major limitation. Most studies conducted to date have been retrospective, relying on historical patient medical records. However, to fully realize the potential of AI diagnosis in real-world healthcare settings, physicians must conduct prospective research by studying current patients over time. To facilitate reliable prospective research, doctors should monitor the health of their patients using a combination of physical examinations, telehealth visits, and remote monitoring technologies such as sensors and trackers.

Future of AI in Healthcare

Despite facing challenges such as data discrepancies, research flaws, and privacy protection concerns, sophisticated AI models hold promise for advancing healthcare through diagnostics, early symptom predictions, and drug discovery. While widespread deployment may not yet be possible, these challenges can be addressed. Additionally, AI-based systems, such as voice assistants, are already assisting healthcare organizations in streamlining their workflow.