Contrary to popular belief, healthcare as an industry has been a fertile adopter of new technologies. History tells us that most of the advancement in healthcare has come from practitioners – physicians trying to diagnose disease and monitoring which therapy is working for their patients, academics trying to analyze formulations and experimenting with mechanism of interaction as well as delivery for more effective therapies, and pharmacists watching the ebb and flow of infections and identifying parameters that impacts spread.
Visiting a modern hospital or a physician’s clinic is enough to see cutting-edge technology in action. Operating theaters and surgical rooms almost make patients feel they’re on a sci-fi movie set. Explanations of how a patient’s own T cell can recognize and attack cancer cells in CAR-T cell therapy is almost like explaining how a Jedi can recognize and fight a Sith lord.
Healthcare already has numerous examples where AI is being leveraged. More active adoption of AI in healthcare is almost the most logical step for the industry to take.
First, let’s take stock of areas where AI is already being used, and then we will discuss a few paths that AI can take to make a greater impact in the healthcare ecosystem.
Some of the use cases where AI is already implemented are:
- Remote patient monitoring: AI is used to track vital signs, such as blood pressure and heart rate, through wearable devices, enabling healthcare professionals to respond quickly to any irregularities.
- Medical devices: The FDA has approved over 900 AI-enabled medical devices in the U.S., and surgeons utilize AI-powered robotic systems for complex procedures, resulting in improved outcomes and shorter recovery times.
- Drug discovery: AI identifies new drug candidates and predicts their efficacy and safety, streamlining the process and enabling faster and more cost-effective market entry.
- Supply chain management: In silico medicine even received the FDA’s first orphan drug designation for an AI-designed drug. AI is also transforming supply chain management by automating order processing and invoicing, improving efficiency and data accuracy.
Various GenAI pilots in pharmaceutical organizations aim to automate tasks such as document drafting and summarization, with scalable solutions emerging across various functions. In clinical development, GenAI optimizes trial protocols and increases patient enrollment, thereby shortening trial durations and streamlining regulatory affairs by managing queries and drafting documents more accurately. In commercial operations, GenAI generates marketing content quickly, speeding up the time to market for new products.
Some of the key challenges that we still face in the healthcare ecosystem, and how AI can play a role in solving these challenges:
Ease of availability of healthcare:
There is an old adage – the future is already here, it’s just not evenly distributed. This is certainly true of healthcare. The existence of technology doesn’t necessarily mean that it is available where it is needed. The West vs. East divide, rich nations vs. poor nations, public healthcare vs. private healthcare, and many other dimensions impact the availability of healthcare.
Bridging these gaps is probably one of the most pressing needs of our time. For healthcare to be easily accessible to everyone, the cost of care has to be drastically reduced. AI can play a crucial role in reducing waste within the ecosystem. AI enables a new capability for the real-time tracking of patients’ health, adherence, real-world evidence, and value-based care.
AI models can optimize the use of critical infrastructure, such as OT and hospital beds, effectively triaging emergency care versus urgent care and routing patients to primary physicians. Advanced algorithms can effectively price therapies to balance the need for adequate returns on investment with the affordability of therapies. We can go on with such examples, but almost every conundrum in today’s healthcare can benefit from AI.
Disease diagnosis:
Early diagnosis of disease is one of the most coveted holy grails of the industry. Many of the life-threatening diseases can be managed, if not cured, if the diagnosis can happen early. Take the example of the chance of survival for lung cancer. When cancer cells are confined to one lung, the chance of survival is 61.2%; this drops to 33.5% as cancer spreads to lymph nodes, and it further drops to 7% if cancer has spread to multiple organs. If we can develop a comprehensive ecosystem where an annual health checkup includes diagnostics powered by AI, along with vital signs, then there is a significantly higher chance of early detection.
Deep learning-powered grading systems assist in identifying, detecting, and grading diseases, such as cancer, from pathology slides, ensuring consistent and accurate assessments. This, combined with the NLP engine that allows for interaction with systems in easy-to-understand language, empowers the patient to understand key health issues and take charge of their wellness.
Precision medicine:
One size doesn’t fit all – these statements highlight the need for precision medicine or personalized medicine for an individual’s health. Traditional medicines aim to treat a broad range of patients with similar symptoms, while personalized medicine focuses on the specific genetic makeup and characteristics of individuals, considering variations in genes, environment, and lifestyle. AI can enhance this approach by assessing a patient’s risk for diseases and identifying trends in data, allowing healthcare professionals to tailor treatments to each individual’s needs.
However, personalized medicine faces challenges such as high costs, labor intensity, and complicated regulatory processes that stem from the small number of qualifying patients. Issues such as data security, accessibility, and the need to demonstrate the superiority of personalized strategies over traditional methods contribute to the complexity.
To address these challenges, a Risk Evaluation and Mitigation Strategies (REMS) contact center can support the safe use of high-risk medications.
Managing health signals:
The global regulatory regime has mandated tracking and reporting of adverse events. Reporting adverse drug reactions in the pharmaceutical industry presents significant challenges, including low reporting rates, difficulties in establishing causality, and the need for centralized reporting systems. Multiple factors can contribute to an adverse event, complicating the isolation of the drug’s role. Furthermore, unclear regulatory guidelines and data silos hinder the process. Establishing a causal relationship, particularly for less common or delayed reactions, can be particularly challenging.
AI can analyze genetic information and biomarkers to create personalized treatment plans, helping to predict patient responses and avoid adverse reactions. This system aids in the early identification of risks and guides necessary modifications. Additionally, automation can reduce manual data processing time by up to 60%, improving regulatory compliance.
Accelerating pace of innovation:
Pharmaceutical companies have set the new benchmark in the industry for bringing innovation to the market. Obviously, the global pandemic was a very different environment, and it will be difficult to get the same level of attention and priority by the global regulatory authorities for every future therapy. Nevertheless, the expectation and need for a faster pace in clinical research remain material.
There are two parts to solve here:
- Integrated systems that provide a uniform view of clinical data
- Redesign of the clinical value chain that leverages the power of AI
An approach for the development cloud that brings quality, regulatory, clinical, and medical on a unified platform has helped tackle the first part. Next-gen clinical processes driven by AI are still lagging behind.
Healthcare efficiency:
Efficient management of health system operations is essential for delivering high-quality healthcare. AI is revolutionizing the allocation of resources, with its ability to predict patient admissions and fine-tune the use of beds, staffing, and medical equipment—ultimately leading to a reduction in waste and increased efficiency.
AI also simplifies a range of administrative tasks, including patient scheduling, billing, and managing electronic health records. This enables healthcare professionals to redirect their focus onto what truly matters: providing exceptional care to patients. A notable example of this is an innovative application, which empowers clinicians to delve into medical records and recommend clinical guidelines. This application not only alleviates administrative burdens but also enriches the overall patient experience.
Hospitals are also venturing into the realm of digital twins—virtual replicas of their physical environments—and leveraging these technologies to improve operational management and decision-making. In the realm of revenue cycle management, a historically time-consuming manual process, the integration of AI is gaining momentum. Healthcare providers are actively exploring AI-driven tools designed to streamline claims management, with keen interest in features such as autonomous coding, automated patient cost estimates, and advanced technology for prior authorization. This shift promises not only to enhance efficiency but also to improve the financial health of healthcare organizations.
In summary, the advancements in AI technology offer unprecedented opportunities to revolutionize healthcare, making it more effective, accessible, and economically sustainable. By promoting the integration of AI through well-designed policies, we can enhance equity, improve care, and ensure that new technologies, treatments, and medicines benefit society.
