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Healthcare is on the cusp of a technological revolution. According to a recent McKinsey survey, more than 70% of respondents from healthcare organizations say they are pursuing or have already implemented generative AI (genAI) capabilities. While the market size of generative AI in the healthcare sector was estimated at $1.8 billion in 2023, it is expected to be approximately $22.1 billion by 2032.
Below, we dive into some of the top ways generative AI is transforming the healthcare sector.
Clinical Productivity: Less Paperwork, More Patient Care
Over half of healthcare providers report that excessive documentation is one of the factors contributing to burnout. Generative AI offers a lifeline by automating tedious administrative tasks, freeing up time for what truly matters—patients.
Consider this: 1 in 5 doctors now uses AI for daily tasks like drafting patient letters, and nearly 30% of these doctors have leveraged it to generate documentation after appointments. In addition, about 15,000 doctors and assistants at more than 150 health systems recently began using a new AI feature in MyChart to draft replies to patient messages.
Even more remarkably, a 2023 study found that healthcare professionals preferred ChatGPT’s responses over real physicians’ answers on Reddit’s AskDocs subreddit 79% of the time. Why? Because AI’s responses were not only higher quality but also more empathic.
Drug Discovery: Revolutionizing the Race for Cures
Developing a new drug typically takes over a decade and billions of dollars. AI is poised to reshape the drug development market, with an estimated total addressable market of around $50 billion for AI-enabled drug development. Generative AI models simulate the effects of different compounds, dramatically reducing the time and cost of discovering new drugs. As of December 2023, around 70 drugs developed with assistance from generative AI were in clinical trials.
Diagnostics and Treatment Plans: Smarter, Faster Decisions
Researchers are developing generative AI models to enhance the quality of medical images, such as MRI scans. Furthermore, a recent survey reported that 28% of the 20% of doctors that use generative AI tools at work have used it to suggest different diagnoses, and a quarter of them said they have used AI to suggest treatment options.
Health tech startup Dandelion Health launched a data library to advance insights into GLP-1 drugs, which help regulate blood sugar and appetite to treat diabetes and obesity. AI’s ability to synthesize vast amounts of medical data means faster, more accurate diagnoses and tailored care plans, ensuring better outcomes for patients.
Consumer Health and Wellness: Empowering Individuals Through AI
GenAI is also making strides in the consumer health and wellness space, offering personalized and accessible tools for individuals to manage their health proactively. Tools like ChatGPT and Claude are being used by individuals to understand results of medical tests and get recommendations. The weight-loss app Noom recently introduced a voice-to-text and picture-based calorie tracker, enabling users to log meals more efficiently.
Rune Labs, a precision neurology software and data company, introduced new generative AI clinical reports that provide a comprehensive, monthly outlook of disease progression along with personalized educational content to help patients, caregivers, and clinicians. The data used includes patient-reported outcomes, such as symptoms or medication side effects, activities recorded by Apple Fitness or logged on the StrivePD app (including walking, yoga, and stretching), and free text notes recorded by the patient, such as blood pressure readings, sleep logs, and mood changes. GenAI’s ability to synthesize diverse data points into actionable insights empowers patients to take a more active role in their health.
Mental Health: Personalized Support Anytime, Anywhere
Generative AI is reshaping mental health services by offering 24/7 support through AI-based chatbots, such as Woebot and even ChatGPT. IWill GITA, an app powered by Azure OpenAI, provides mental health support for mild cases of anxiety and depression to people in India through the use of cognitive behavioral therapy principles.
AI can also assist therapists by generating personalized therapy plans and analyzing patient data to detect early signs of mental health issues. One example is Upheal, which runs in a therapist’s browser or mobile device and listens to sessions, records session notes, and even suggests treatment plans. Woebot’s platform also collects patient-reported data and helps providers determine treatment plans.
Education and Training: Redefining How We Learn Medicine
Generative AI is revolutionizing medical education and training by providing innovative tools for learning and skill development. AI-powered simulations create realistic virtual patients, enabling medical students and professionals to practice diagnosing and treating a variety of conditions in a risk-free environment.
A new study from the Yale Center for Healthcare Simulation is exploring how emergency physician residents, internal medicine residents, and medical students interact with a new generative AI tool, GutGPT. This tool uses retrieval-augmented generation, so GutGPT can source its answers from trusted and citable information, like clinical guidelines, pathways, and journal articles. During the simulation, participants are asked to treat hypothetical patients with a gastrointestinal bleeding disorder using synthetic patient data.
Additionally, genAI can support continuous learning by summarizing vast volumes of medical literature and recent research, helping practitioners stay updated on the latest advancements in their fields. AI-driven platforms can also generate case studies, quizzes, and interactive scenarios tailored to specific specialties, fostering deeper understanding and retention of complex medical concepts.
Research Advancements: Unlocking New Possibilities
Generative AI accelerates healthcare research by analyzing large datasets and synthesizing information on emerging treatments and therapies. For example, AI can generate comprehensive overviews on complex topics like the use of ketamine for treatment-resistant depression, as shown in AlphaSense’s Generative Search query below. This ultimately helps researchers, clinicians, and policymakers quickly access the latest findings, clinical trial data, and efficacy insights.
Generative AI and the Future of Healthcare
Generative AI is changing the game in healthcare. By tackling long-standing challenges like documentation overload, drug discovery bottlenecks, and the mental health crisis, this technology is reshaping how we deliver care, conduct research, and train the next generation of healthcare professionals. However, the journey is not without challenges. As we embrace these groundbreaking tools, we must also navigate the ethical complexities of patient data privacy, bias in algorithms, and the need for transparency.
Looking ahead, the potential of generative AI in healthcare feels limitless. Future innovations could include personalized medicine powered by genAI models trained on individual genomic data, real-time diagnostics using wearable devices, and AI-assisted surgical procedures enhanced by generative visualizations. The next decade will likely redefine the intersection of technology and medicine.
ABOUT THE AUTHOR
Sarah Hoffman
Director of Research, AI
Sarah Hoffman is Director of Research, AI at AlphaSense, where she explores artificial intelligence trends that will matter most to AlphaSense’s customers. Previously, Sarah was Vice President of AI and ML Research for Fidelity Investments, led FactSet’s ML and Language Technology team and worked as an Information Technology Analyst at Lehman Brothers. With a career spanning two decades in AI, ML, natural language processing, and other technologies, Sarah’s expertise has been featured in The Wall Street Journal, CNBC, VentureBeat and on Bloomberg TV, and she was named a Rising Star for WatersTechnology’s Women in Technology and Data Awards in 2018. Sarah holds a master’s degree from Columbia University in computer science with a focus on natural language processing, and a B.B.A. from Baruch College in computer information systems. Sarah is based in New York.
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