The first symptom of tomorrow’s disease may not be felt by the patient; it may be predicted by an algorithm. Historically, the treatment journey begins when a patient notices symptoms, goes to their physician, and receives a diagnosis. This is considered the “Awareness and Recognition” stage of the journey. Artificial intelligence (AI) is redefining this stage by transforming it from a subjective, patient-driven process to one guided by data-driven insights. Today, AI can help identify diseases earlier, prompting patients, providers, and healthcare companies to rethink how they interact with care.
Patients Are Turning to AI Throughout Their Treatment Journey
AI is quickly becoming the first step in a patient’s treatment journey, as patients use it to answer questions about their health. The pandemic introduced patients to virtual visits and AI assistants, increasing their comfort with the use of AI, and AI tools like symptom checkers, virtual assistants, and wearable trackers can help identify symptoms in real time, enabling patients to share their data with their physician and receive a diagnosis, sometimes that same day. These resources can bolster health equity in rural and underserved regions by guiding patients to the appropriate level of care, ultimately reducing access barriers and speeding up diagnosis.
However, patients need to be vigilant when using these tools, as they are not always accurate. It’s crucial that all information is double-checked to prevent unnecessary worry or potential delays in receiving proper care.
The Role of AI in Early Symptom Recognition by Predictive Detection
AI has created an alternative way to diagnose patients through system-driven recognition or predictive detection. Using predictive analytics, alongside genomics and medical imaging, algorithms can detect early signs of chronic or degenerative diseases well in advance. Predictive detection helps healthcare systems identify patients at risk for developing certain diseases before symptoms appear. AI can identify these patients by generating a personalized risk score from a patient’s genetics, medical history, and lifestyle factors. This risk score enables earlier intervention, which may significantly improve the patient’s chances of survival. AI has demonstrated a high accuracy in detecting chronic kidney disease through retinal imaging, supports the prediction of complications in diabetes and hypertension, and can identify early indicators of cardiovascular and neurodegenerative conditions, including Alzheimer’s disease.
However, these predictive models can produce false positives, potentially leading to misdiagnosis or unnecessary tests, which could be costly for patients. This showcases the importance of physician oversight when incorporating these models into patient care.
When AI is integrated into electronic health record (EHR) systems, it can generate valuable patient insights. By quickly evaluating clinical notes, biomarkers, and other diagnostic indicators, AI can turn data into real-time, meaningful intelligence. Allina Health and Kaiser Permanente have successfully incorporated AI into their health systems. Allina Health has reduced 30-day readmissions by over 10%, and Kaiser Permanente incorporates social determinants of health into its predictive models, demonstrating how AI-driven workflows make care more proactive and equitable. That said, when AI is integrated into healthcare systems, accuracy can vary depending on the quality of the data in those systems. At the population level, AI-powered surveillance tracks disease patterns, behaviors, and activity in real-time. Analyzing large-scale data from platforms such as Google Trends and social media enables faster prevention strategies and smarter resource allocation. Nevertheless, this large-scale data collection raises privacy and security concerns.
AI Disadvantages & the Need for Continuous Oversight
While there are many benefits to AI, there are an equal number of drawbacks to consider.
- Symptom checkers and online chatbots can sometimes provide inaccurate information. Ensuring the quality and authenticity of AI-driven health information is critical, and combating health misinformation is one of today’s top priorities.
- AI models can be biased if their training data under-represents minority populations. Over time, these issues may worsen existing health inequities. For example, cardiovascular risk models trained mostly on male patients may underdiagnose women, and dermatology tools may underperform on darker skin tones. Misinformation and potentially biased data can negatively affect individual patient outcomes and reinforce broader health disparities.
- Many deep learning models also lack transparency, which makes it difficult for clinicians to question or understand AI-generated recommendations.
- Other drawbacks include privacy and security concerns, as these AI systems rely on large amounts of sensitive data.
These limitations highlight the ongoing need for human oversight. Physicians must combine their clinical judgement with AI insights to deliver proper care. Continued research and strong oversight will be crucial for AI in proactive health management.
AI is Redefining the Pharma and Biotech Industries
Pharmaceutical and biotech companies can greatly benefit from AI; however, they also need to remain vigilant about the potential risks it entails.
- Commercial Operations: AI-driven market intelligence can help identify patient populations up to 15 times faster than traditional methods, improve forecasting accuracy, expand patient populations, and enhance provider targeting. AI-generated real-world evidence (RWE) can help support value-based pricing, payer negotiations, and operational planning. This enables better resource allocation and greater strategic agility. However, this focus on profitability and AI-generated data could limit patient access, as it doesn’t account for personal circumstances.
- What can you do? Our Spectrum Science Consulting experts can develop an AI commercial roadmap to identify new opportunities in the market and improve sales force effectiveness.
- Research & Development (R&D): AI accelerates target discovery; analyzes genomic, clinical, and real-world evidence; and identifies opportunities for drug repurposing. This can reduce costs and shorten development timelines. However, depending too much on AI predictions could limit innovation by causing researchers to overlook new ideas or treatments that don’t fit existing data.
- What can you do? Spectrum Science can use this AI-generated RWE data to support strategic pricing, market access, and distribution strategies for pharmaceutical and biotech companies.
Winning in pharma and biotech will require the use of AI for optimal data protection, decision-making, and overarching company efficiency. Pharmaceutical and biotech companies must adapt to how predictive analytics and AI are changing the rules of discovery, access, and patient experience, and Spectrum Science is the necessary strategic partner to help clients navigate these rules.