From AI-powered diagnostics to personalized medicine, we asked twenty four healthcare executives their insights on trends and technologies that will transform patient care in the coming year.
Ashley Barrow, Founder and CEO of RE-Assist
In 2025, Artificial Intelligence and the T.E.A.M. (Transforming Episode-Based Accountable Management) model will redefine care coordination, creating a smarter, faster, and more equitable healthcare system.
Primary care will evolve to include more longitudinal services, emphasizing sustained patient engagement and proactive management of chronic conditions. AI-powered tools will play a key role, identifying high-risk patients, closing care gaps, addressing Social Determinants of Health (SDOH) barriers, and streamlining resource allocation across providers to optimize care delivery.
Nontraditional providers, such as community health workers and paramedics, will take on expanded roles, equipped with AI-driven workflows. This will broaden access to care and create a more inclusive network.
Finally, new payment models and billing codes will help break down barriers, ensuring underserved communities receive the essential care they need.
Amanda Barefoot, MHA, Managing Director, Health Care & Life Sciences Solutions
AI’s intention is to alleviate provider burden, but law making could increase it. We are on the cusp of some challenging situations with lawmaking related to AI. If we aren’t careful, too much red tape and legal policy is going to create additional administrative drain on providers. There is a lot of chatter about patients getting to choose if their doctor uses ambient listening or not. No longer will they be able to simply see a patient if they have to check individual patient preferences for what AI augmentation technologies can and cannot be used.
Payers tap technology to help strengthen the public health framework. After years in a state of public health emergency due to COVID-19, we will see payers and public health vow to keep communication more open than it has been in the past. Data sharing is key to this effort, and increases in interoperability are enabling payers and public health to speak the same language. Advancements in technology, such as AI-driven analytics and real-time data exchanges, will further streamline collaboration and decision-making. Expect to see more shared accountability evolve through the standardization of metrics and shared investments to make healthier populations.
Dr. Caroline Carney – President of Behavioral Health and Chief Medical Officer at Magellan Health
Most AI projects never really get off the ground. They begin with a “Let’s see if this works” philosophy and often consume massive amounts of resources before ultimately being shelved.
There is no question AI will contribute to improving mental and behavioral health in 2025, and reduce administrative burden to the system. However, we must be as mindful of its environmental toll as its potential efficiencies. Younger generations are already thinking more broadly about the immense resources required to power AI and whether it is worth the global impact. Therefore, going forward, mental health AI solutions will increasingly need to meet a two-point litmus test:
Are they designed with an endpoint in mind, by including clinicians in each step of the way?
Are clinical outcomes measured and found to be reliable and valid?
Do clients receiving services through AI understand when responses are AI-generated or therapist-generated?
Does AI support people wherever they are in their mental health journey?
Do the solutions take into account the comorbid conditions that often accompany behavioral health issues?
Working backward from a well-defined goal allows us to create AI tools that effectively empower people’s mental health journeys. Consider all the potential benefits of mental health solutions that start with a clear endpoint in mind (e.g., suicide prevention), incorporate available research in an area, and guide people to successful outcomes based on their unique needs. AI should be considered early in its role in caring for those seeking services—we need to fully understand the impacts before fully embracing in care.
Joe DeVivo, CEO at Butterfly Network
In 2025 we will see AI emerge as a transformative force in healthcare, acting as an amplifier of human capabilities, rather than a replacement. The past year has been pivotal in educating the industry to the potential of AI, and we’re now poised to see real-world applications that will significantly impact patient care. AI tools will enhance clinicians’ ability to process vast amounts of data, identify trends and present anomalies, ultimately making them more productive and effective in their diagnoses. This shift will not only simplify complex tasks but also democratize access to advanced medical technologies like ultrasound imaging, enabling more healthcare professionals to use these tools with confidence. As we move forward, the integration of AI into everyday practice will drive innovation and improve patient outcomes across the board.
Jeff Elton, CEO of ConcertAI
There will continue to be an increase in the integration of AI in daily workflows and decision-making as AI increases in accuracy and efficiency. 2025/2026 will see the enormous potential of AI as a ‘decision augmentation’ of expert humans. This will come from context-sensitive solutions, LLMs, that can align other LLMs to collect, analyze, and recommend options to clinical teams that are aligned to that specific decision and the unique characteristics of that patient. This needs to and will happen, as there are not enough staff and specialists to provide the needed care.
Elliott Green, CEO & Co-Founder of Dandelion Health
In 2025, two specific topics will be top of mind specific to AI in healthcare. First, the pressing need for AI validation and related best practice approaches must be fleshed out next year. Without this, trust in AI won’t improve and, in turn, concerns related to bias and efficacy of AI will hold back broader adoption. Also, with a new Administration at the helm, it remains to be seen how AI regulation will be approached. Clarity on AI regulations in healthcare from our new Administration – including best practice approaches to addressing potential bias in the data being used to train and build AI algorithms – will also be mission critical to safeguarding AI, and as part of broader efforts to build transparency and trust in the use of AI in healthcare and the life sciences.
Aashima Gupta, Global Director, Global Healthcare Solutions, Google Cloud
The healthcare industry is on the cusp of a transformative era, driven by the rapid adoption of generative AI. With over 60% of healthcare and life sciences executives already leveraging AI in production, we are witnessing a significant shift towards AI-powered solutions. As we look ahead to 2025, AI will continue to reshape the industry, enabling more efficient operations, improved patient outcomes, and accelerated medical advancements.
In 2025, we’ll see a new comprehensive view of patient health with a surge in multimodal AI solutions. By harnessing the power of multimodal AI, healthcare providers can unlock valuable insights from a diverse range of data sources, including medical images, patient records, and genetic information. This will help empower clinicians to make more accurate diagnoses, develop personalized treatment plans, and ultimately improve patient outcomes.
AI agents are revolutionizing healthcare operations by streamlining workflows and reducing administrative burdens. This will help empower healthcare professionals to focus on providing high-quality care and improving patient outcomes.
Bharath Kakarla, Senior Vice President of Engineering, Intus Care
AI’s transformative potential, especially with copilot capabilities, is set to reshape healthcare by alleviating critical challenges such as administrative burnout and compliance issues. By integrating AI into healthcare systems, providers are empowered with enhanced diagnostic accuracy and personalized patient care, allowing them to focus on what truly matters—patient outcomes. Meanwhile, expanding the care continuum beyond traditional settings underscores the need for robust, secure interoperability platforms. These platforms will enable seamless data exchange across diverse systems, fostering a cohesive healthcare ecosystem. This is especially important as the healthcare system serves a growing number of older, vulnerable patients. The synergy between AI and interoperability will usher in a new era of value-based care, ensuring that healthcare delivery is both efficient and patient-centered.
Ryan Johnson, Chief Product Officer at CallRail
The healthcare industry is undergoing immense changes thanks to AI advancements and this momentum will only continue as we head into 2025 with the tech enabling more personalized experiences and enhancing areas like mental healthcare.
In the year ahead, we can expect healthcare providers to identify further use cases for AI with a large push around enhancing customer care – this growing demand for personalized experiences place conversational AI at the forefront of the healthcare industry’s 2025 priorities.
For example, customers calling a provider will no longer need to do the typical process of “press 2 to talk to a nurse.” Instead, we’ll start to see the industry move toward having the initial interaction be AI prompting callers to share the reason behind their call, allowing AI to gather insights to more accurately identify the right person to connect a caller with or answer to provide. This will transform mental healthcare with AI applications expanding offerings to chatbots and virtual assistants that can provide immediate support and potentially identify individuals at risk.
Alongside enhancing patient care, AI will also empower healthcare providers’ marketing strategies by allowing them to engage with potential leads through personalized multi-channel marketing. This will help prospects book appointments or get connected to the right person while taking the burden off medical office personnel.
Ariel Katz, CEO & Co-Founder at H1
Expanded Use Cases for AI in Clinical Trial Operations
In 2025, we will see companies leverage AI for new use cases to streamline operations, reduce timelines and costs, and improve the likelihood of successful trials. As more clinical ops teams embrace AI, barriers to clinical trial participation will be reduced. AI’s capabilities to integrate and analyze diverse data sources will lead to more precise patient matching and trial design. Its advanced analytics will predict drop-out rates and forecast adverse effects, and tools like chatbots and personalized apps will enhance engagement and adherence throughout the trial. Once trials are underway, teams will use AI to analyze results, make real-time adjustments based on incoming data, and eventually draft regulatory submissions.
Charlie Lougheed, CEO and Founder of Axuall
The AI naysayers of 2024 will be proven wrong. We’re already seeing AI make meaningful strides in reducing documentation workload for healthcare providers. Meanwhile, reducing administrative costs through AI-enabled automation will continue to gain momentum, especially given the lower risk and regulatory burden compared to clinical decision support use cases. While healthcare has always moved slower than other industries, it has hit a capacity limit in costs and resources that will demand additional incorporation from AI technologies.
Christopher McSpiritt, head of life sciences at Domino Data Lab
AI Will Accelerate Clinical Trial Recruiting for Faster Medical Innovation
By 2025, AI models will harness electronic health records (EHRs) and real-time patient data to streamline the recruitment process for clinical trials, enabling the identification of eligible candidates with unmatched accuracy and speed. Evidence of this transformation is already emerging, with AI-driven platforms significantly reducing recruitment timelines by efficiently matching patients to trial criteria. These advancements promise to reshape the recruitment landscape, enhancing trial effectiveness and accelerating the pace of medical innovation.
Ashish Nagar, CEO of Level AI
Generative AI will revolutionize contact centers by providing agents with real-time, context-aware assistance, leading to 10x improvements in response times and accuracy. AI-powered quality assurance will become the norm, enabling 100% of contact center interactions to be analyzed and scored automatically. Organizations that don’t take this step will be rapidly left behind by their competitors who do.
Nish Parekh, Senior Vice President, Chief Product Officer at Omnicell
AI and machine learning are poised to be the game-changers in pharmacy operations over the next few years. The market for AI in healthcare could grow to $17.2 billion by 2032 and these technologies will automate routine tasks, help predict patient needs, and drive operational efficiency, allowing pharmacy teams to focus on higher-value tasks that impact patient care. The true promise of AI in pharmacy is accuracy, efficiency, and the ability to optimize inventory management and operations, making care safer, ensuring a stable medication inventory, and ability to support more personalized therapies. This will redefine how pharmacies operate and interact with patients.
Bhargava Reddy, Chief Business Officer of Oncology at Qure.ai
Healthcare AI will help close the Lung Cancer screening gap challenge in 2025. The current landscape reveals a concerning statistic: merely 5.8% of eligible former smokers in America undergo CT-based lung cancer screening. This substantial screening gap presents a critical opportunity for AI-powered solutions to expand the detection network by analysing CT images faster or finding nodules in Chest X-rays which could be potentially missed.
Clarissa Riggins, Chief Product Officer, Experian Health
I think it’s safe to say the adoption of AI in healthcare will be much slower than we had hoped for. Experian Health’s latest Claims Survey found providers are quite hesitant to move forward with technological advancements such as automation and AI. Over the last year, we’ve expected organizations to evaluate their largest pain points and integrate advanced technologies, but 28% of survey respondents have not even considered introducing automation technology to their systems. There’s a general acknowledgement that the current state of claims technology is insufficient to address existing revenue cycle demands, but little is being done to implement the required change the industry needs.
Given the ongoing challenges within the revenue cycle industry, leading health systems are outsourcing RCM to support financial sustainability and growth. I suspect we will continue to see this trend increase, alongside the hopes that healthcare organizations increasingly turn to automation and AI to streamline processes and reduce administrative burdens. I expect more sophisticated systems will be used to provide accurate price transparency, helping providers comply with regulations and offer clearer pricing to patients. AI-based tools are a promising way to reduce RCM costs and improve both the employee and patient experience. Embracing these technologies will be key for revenue cycle leaders to stay ahead in an evolving landscape.
Steve Rowe, healthcare lead at 3Pillar
The AI Arms Race in Revenue Cycle Management
Providers and insurance companies have long been at odds regarding claims and revenue cycle management, with providers seeking maximum reimbursement to cover costs, and insurers focused on controlling expenses by denying claims. This tug-of-war has led to increasing administrative burdens, with providers resubmitting claims and insurers balancing cost control with maintaining provider relationships. Now both sides are turning to AI to better make their case and gain an advantage over the other. Likely this arms race will cancel each other out and not actually reduce administrative friction, however providers and payers that don’t leverage AI will be at a significant disadvantage.
Matt Ryan, Chief Technology Officer at Kythera Labs
Applying AI and Machine Learning technology to healthcare analysis is well underway, we are actively tracking their progress and impact on the healthcare landscape in 2025. These technologies, if leveraged correctly, will mean better patient experiences, optimized healthcare delivery, and enhanced organizational sustainability, making a real difference in both individual lives and the healthcare system as a whole. But we need collaborative efforts among healthcare systems, technology companies, and data platforms to harness the diversity of healthcare data in the marketplace and drive equitable solutions. In terms of efficiency, generative AI is an output multiplier, making it possible for one person to accomplish more faster. However, prioritizing patient privacy, building trust in AI models, and addressing regulatory challenges will be critical to ensuring these innovations benefit all populations equitably. As the focus shifts to more personalized and efficient care in 2025, AI and machine learning models will aim to bridge gaps in access and improve patient outcomes.
Shashank Saxena, a Managing Partner at Sierra Ventures
LLMs will transform knowledge work. LLMs are no longer just tools to fix broken workflows, as RPA once was. They are becoming central intelligence systems, capable of ingesting data, generating outputs, and redefining productivity. This shift positions LLMs as the cornerstone of GenAI’s value.
Dr. Scott Schell, Chief Medical Officer, Cognizant
AI will move beyond the niche to become more central in clinical care applied alongside human expertise
A persistent misconception in healthcare is that AI can replace human expertise, which can cause an overreliance on systems, insufficient oversight and suboptimal outcomes. While AI excels at processing vast datasets and identifying patterns that might elude
human eyes, it lacks the contextual understanding and clinical judgment necessary for complex patient care. In 2025, healthcare organizations will leverage the technology as an augmentation tool that supports, rather than replaces, clinicians. AI will serve
as a force multiplier, allowing healthcare professionals to focus on high-complexity tasks and direct patient care while it handles more routine, data-driven tasks.
As such, I predict AI will break out of its niche applications for healthcare —such as radiology, pathology, and administrative tasks—and fully integrate into clinical workflows. AI’s role will expand significantly in areas like patient management, decision
support systems (CDSS), population health, and personalized medicine. Clinicians will increasingly depend on AI for real-time decision-making and predictive analytics, marking a shift from early-stage pilots to daily use in care delivery. This will be significant
for patient care, with AI helping healthcare professionals move from reactive to proactive (and preventive) treatment models. The demand for professionals skilled in AI and data science will also increase as these fields become central to innovation in life sciences.
Luis E. Taveras, Ph.D., Senior Vice President & Chief Information Officer, Lehigh Valley Health Network
Not all offerings will deliver meaningful impact; we anticipate that maybe 5-10% of the solutions will have real, measurable value in healthcare. Some vendors may rebrand existing tools as AI solutions with minimal changes, just because they added a simple analytics function. These vendors will do this in an effort to stay up-to-date with technology. Extensive and proper due diligence by true technologist, clinical, and business experts is required to be a smart consumer of the few valuable AI solutions in the marketplace.
Erik Terjesen, Partner at Silicon Foundry
AI-driven solutions will continue to be in focus for pharma and life sciences companies as they seek to streamline their development processes and shrink time to market
As major pharma companies monitor how emerging AI/ML solutions can augment their existing processes, organizations should evaluate whether it is better to invest in building their own internal AI tool for clinical protocol design or opt for a third party SaaS solution Several factors need to be considered
Using Emerging Tools to enhance internal AI capabilities based on an emerging AI/ML company’s core technology presents a long term strategic play with more flexibility and potential for tailored solutions that align closely with a company’s specific needs This approach provides an extended runway for innovation and future scalability, but requires capable in house talent to effectively customize the tools, potentially stretching a company’s resources and leading to operational inefficiencies as internal capacities are strained
Eric Walk, Chief Medical Officer, PathAI
In 2025, I expect the adoption of digital pathology to continue to accelerate, driven by its ability to enhance workflows, improve diagnostics, and support advancements in biomarker discovery, drug development, and novel diagnostics. The integration of AI-powered solutions with image management systems will expand across laboratories of all types, from large reference labs to community practices. Partnerships will play a pivotal role in this growth, making AI solutions more accessible within an increasingly digital ecosystem. Platforms like PathAI’s AISight® will continue to evolve, offering interoperable solutions that empower institutions of all sizes to adopt cutting-edge AI technologies without the complexity and cost of standalone systems.
Don Woodlock, Head of Global Healthcare Solutions at InterSystems
Artificial intelligence is poised to revolutionize every aspect of healthcare in the near future, and 2025 will be the year we begin to notice adoption at a much higher rate. While AI is already streamlining tasks like patient communications in online portal and call centers, we’re on the cusp of even more transformative applications. The early success of ambient listening – the AI both transcribes and extracts relevant information from live conversations for making prescriptions or lab orders – is generating significant excitement among physicians who report increased efficiency and greater patient-centered interactions.