Future Health Index 2025

Building trust in healthcare AI

Perspectives from patients and professionals

India report

Commissioned by Philips

Contents

• Ten years of the Future Health Index
• Foreword
• Research premise
• Chapter 1: Unleashing the transformative power of AI
• Chapter 2: Caution meets optimism: Enhancing AI trust and acceptance
• Recommendations
• Appendices
• Research methodology
• Glossary of terms

Ten years of the Future Health Index

Over the past decade, the Future Health Index has examined the role of technology in some of the biggest trends health systems have faced. Initially a benchmark of connected care adoption around the world, the Future Health Index has evolved to look at how technology can shape the future of health, based on the perspectives of healthcare leaders, professionals and patients in countries with varying demographics and health systems.

2016: Integrating services, data and systems for a healthier population

2017: Delivering connected care more effectively

2018: Harnessing data and telehealth to deliver better outcomes

2019: Leveraging divergent views on digital health technologies

2020: Empowering the next generation of healthcare professionals to transform care; The impact of COVID-19 on younger healthcare professionals

2021: Building resilient and sustainable futures

2022: Using advanced analytics to enhance care delivery

2023: Innovating new models of care delivery

2024: Addressing staff shortages with AI-enabled innovation and new care delivery models

Foreword

Healthcare is at a crossroads. Staff shortages, skyrocketing costs, and systemic inefficiencies are stretching the system to its limits – and patients are feeling the impact. Our 2025 Future Health Index – the 10th anniversary edition of our global healthcare survey and the largest of its kind – shows that patients may wait almost two months or more for specialist care in over half the countries surveyed. Without decisive action, a projected shortfall of 11 million health workers by 2030 will leave millions without timely access to essential care.

Amid these challenges, artificial intelligence (AI) has emerged as a powerful accelerator – and perhaps our most compelling opportunity – to meet rising healthcare demands as populations age. Consider how quickly technology has evolved in the past five years and how far it could advance in the next five. We imagine that by 2030, AI could automate much of the 'silent' administrative work done by healthcare professionals, augmenting their clinical capacity significantly, without lengthening their workday.

Our survey shows healthcare professionals recognize AI's potential: not just to reclaim time lost to administrative tasks, but to diagnose diseases more precisely, reduce avoidable hospital readmissions, and improve patient outcomes. Complementing these findings, another study suggests broader adoption of today's AI technology could lead to savings of $200 to $360 billion in healthcare spending annually in the US alone.

Yet while AI is advancing fast, public trust is lagging behind. The 2025 Future Health Index reveals a critical gap: most healthcare professionals are optimistic about AI improving healthcare, yet many patients remain skeptical – especially when their health is on the line. And despite their optimism, most healthcare professionals still have important concerns about bias and liability. Without trust, the full promise of AI in healthcare cannot be realized.

Building trust requires a responsible, people-centered approach: one that puts collaboration at the heart of AI innovation. AI must enhance - not erode - the trusted relationships between patients and healthcare professionals. It must deliver tangible benefits, be anchored in robust safeguards, and operate within clear, consistent regulatory frameworks. Only then can AI earn the trust it needs to drive meaningful transformation in healthcare.

That doesn't mean slowing down AI innovation – it means accelerating it in the right direction, bringing life-saving AI solutions to more people, faster, while fostering trust. To achieve this, we must act together across disciplines, institutions and borders. Our report offers critical insights to drive that collaboration. We call on healthcare leaders everywhere to join us in translating insight into action, shaping a future where technology and trust go hand in hand to deliver better care to more people.

Quote: "As AI transforms healthcare, trust and innovation must go hand in hand to bring life-saving solutions to more patients and providers, faster – and with the right safeguards.”

Shez Partovi, Chief Innovation Officer, Chief Business Leader Healthcare Informatics

Carla Goulart Peron, Chief Medical Officer

Research premise

This is the largest global survey of its kind, analyzing the priorities and perspectives of healthcare professionals and patients.

In its 10th edition, the Future Health Index 2025 investigates how artificial intelligence (AI) can empower healthcare professionals to deliver better care for more people.

This report highlights key challenges impacting healthcare professionals today, revealing their sentiments on the rise of AI and identifying critical gaps that must be addressed to enhance their confidence in integrating AI into patient care.

We also examine the patient perspective, assessing their comfort with AI in healthcare and identifying opportunities to strengthen their trust in technological advancements.

For this year's Future Health Index, we conducted proprietary quantitative research involving over 1,900 healthcare professionals and over 16,000 patients across 16 countries.

Countries surveyed: Canada, United States, Brazil, United Kingdom, Netherlands, Germany, France, Spain, Saudi Arabia, South Africa, India, Indonesia, China, Japan, South Korea, Australia.

16 countries

1,900+ healthcare professionals

16,000+ patients

Chapter 1: Unleashing the transformative power of AI

While healthcare systems are already benefiting from AI implementation, faster adoption is necessary to further enhance the experience for both staff and patients. Our survey reveals that Indian healthcare professionals are optimistic about AI's potential to improve patient outcomes.

High optimism about healthcare AI

AI adoption in healthcare in India is accelerating rapidly, with the market expected to reach $1.6 billion by the end of this year. This growth is fueled by both public and private sector investments in various aspects of healthcare, from diagnostics and treatment to public health data initiatives.

Another factor that may be contributing to the fast pace of AI adoption in India is positive attitudes towards the technology. Indian patients are significantly more likely than their global peers to feel optimistic about healthcare AI (79% and 59%, respectively). Healthcare professionals' optimism about the potential for AI in healthcare is also high at 76%, in line with the global average of 79%. This confidence in AI is a clear sign of the increasing trust in digital transformation as a driver of quality healthcare, and an important prerequisite for driving further acceptance of the technology.

76% of healthcare professionals are optimistic that AI could improve patient outcomes.

79% of patients are optimistic that AI can improve healthcare.

Delivering better care for more people with AI

Reflecting the high levels of optimism about AI, the Future Health Index 2025 reveals that there is a clear appetite for AI in healthcare among medical personnel. India's healthcare professionals believe that, when implemented correctly, AI can help them improve access to clinical research and automate repetitive tasks. They also expect it will allow their departments to expand capacity to serve more patients, help triage patients more effectively and reduce administrative burden.

AI can also help improve the patient experience. The majority of Indian healthcare professionals believe AI can help reduce appointment wait times and increase face-to-face time with patients. Additionally, almost two thirds believe AI can shorten procedure times, improving patient throughput and clinician efficiency. The findings highlight the importance of AI for reducing the administrative burden for healthcare professionals and delivering better care for more people.

How healthcare professionals say AI can positively impact their departments

Clinical excellence and innovation

• Improve access to clinical research: 87%
• Accurate and timely medical interventions: 72%

Patient access and experience

• Reduce wait times for patients: 78%
• Triage patients to the right care setting: 76%
• Expand capacity to serve more patients: 75%
• Increase face-to-face time spent with patients: 60%

Operational efficiency and workflow optimization

• Automate repetitive tasks: 80%
• Improve patient throughput: 77%
• Reduce overtime: 77%
• Shorten procedure times: 65%

Medical staff recognize the value of AI in supporting staff development.

Nearly all healthcare professionals (93%) see AI as a tool to upskill less experienced workers. Three out of four respondents (75%) say AI-supported training is helpful for less experienced staff. With specialist doctors in short supply – especially in underserved rural and urban areas – AI can help less experienced employees perform at higher proficiency levels and improve access to quality care. Another 3 in 4 healthcare professionals (76%) say AI will help ensure greater consistency in the quality of patient examinations.

Recognizing the urgency of capitalizing on these opportunities, India's healthcare professionals warn of the cost of not adopting AI more quickly. They worry, more than their global peers, that slower implementation could lead to increased clinician burnout from non-clinical tasks and missed opportunities for early intervention.

Healthcare professionals think slower implementation of AI will lead to:

• Increased clinician burnout from non-clinical tasks: 61%
• Missed opportunities for early intervention: 57%
• Increasing backlog of patients: 47%

From sick care to healthcare: AI's transformative potential

While AI offers huge potential to improve care and drive efficiencies, its greatest impact may be in preventing the need for some types of care altogether. As chronic disease rates rise and costs soar, there is a need to embed AI in proactive care.

Efforts are underway to expand the reach of patient care and detect health issues or deteriorating conditions earlier. In the 2024 Future Health Index, we found healthcare leaders in India invested in a wide range of remote patient monitoring solutions to support many clinical areas.

This year's findings show India's healthcare professionals are embracing this shift. They believe AI-powered predictive analytics can reduce hospital admissions and save lives through earlier interventions.

82% of healthcare professionals believe AI and predictive analytics could save lives by enabling early interventions.

76% of healthcare professionals believe digital health technologies including AI and predictive analytics will reduce hospital admissions in the future.

Chapter 2: Caution meets optimism: Enhancing AI trust and acceptance

For AI to gain widespread adoption in healthcare, trust is essential. Most patients and healthcare professionals are optimistic about AI and comfortable with its use across a range of tasks. However, both groups have some reservations about using digital technologies in healthcare.

Doctors, nurses and patients all comfortable with AI in operational and clinical use

AI comfort and confidence among patients and healthcare professionals

In line with their general optimism, healthcare professionals are confident in the use of AI to effectively support almost all facets of healthcare, from logistical tasks to supporting diagnostic decision-making and treatment planning and monitoring.

Patients, too, feel confident with the use of AI. In fact, patients in India are around 10 percentage points more likely than the global average to be comfortable with all AI healthcare use cases, sometimes outpacing their doctors and nurses in terms of comfort and confidence.

Healthcare professionals' confidence in AI:

• Scheduling appointments: 90%
• Documenting medical notes: 88%
• Processing test results or scans: 85%
• Supporting diagnostic decision-making: 84%
• Helping to create tailored treatment plans: 81%
• Checking in patients: 83%
• Triaging patients to prioritize urgent cases: 85%

Patient comfort with AI:

• Scheduling appointments: 83%
• Documenting medical notes: 82%
• Processing test results or scans: 82%
• Supporting diagnostic decision-making: 79%
• Helping to create tailored treatment plans: 77%
• Checking in patients: 74%
• Triaging patients to prioritize urgent cases: 83%

Patients express fewer concerns about the use of digital technologies in their care

Most patients support increased use of digital technologies, if it improves access to care and benefits patients like them. However, more than 3 in 5 patients are concerned that more technology could reduce face-to-face time with their doctors.

84% of patients welcome the use of more technology in healthcare if it helps improve care for patients like them.

63% of patients worry that relying more on technology means less face time with their doctors.

Country comparison of patient concerns:

• Australia: 61%
• Brazil: 51%
• Canada: 57%
• China: 56%
• France: 54%
• Germany: 49%
• India: 63%
• Indonesia: 52%
• Japan: 49%
• The Netherlands: 56%
• Saudi Arabia: 49%
• Spain: 49%
• South Africa: 46%
• South Korea: 55%
• UK: 51%
• United States: 43%

Collaborating to create effective tools

While broadly optimistic, healthcare professionals have some worries over the practical use of digital technologies in healthcare, including AI. With half of healthcare professionals involved in developing these technologies, only 43% feel they are designed with their needs in mind. This figure is in line with the global average of 38%.

Our survey reveals that a partnership approach could help address this issue. The vast majority (84%) of healthcare professionals say they would be more confident using AI tools developed in partnership with a technology company they trust.

Uncertainty around legal accountability related to AI also remains a concern for 86% of healthcare professionals. Greater clarity about the extent to which they may be held responsible compared to, for example, the developer or institutions, would be helpful for them. In addition, a majority (67%) of healthcare professionals are worried about data bias in AI widening existing disparities in healthcare outcomes.

5 in 10 healthcare professionals are actively involved in the development of new technologies at their organizations.

4 in 10 healthcare professionals believe new technologies are designed to meet their needs.

Enhancing trust for the future

To further strengthen trust in AI, our research highlights key factors, including the need for clear guidelines for AI usage and legal liability, and scientific evidence of its effectiveness. Healthcare professionals also call for ongoing monitoring and evaluation of AI systems to ensure continued effectiveness.

Interestingly, reassurance about job security ranks lowest on their list, suggesting that healthcare professionals largely view AI as a valuable enhancement to their skills, rather than a threat to their profession. The overall sentiment is clear: with the right guardrails in place, the future of AI-enabled healthcare is bright and offers the potential for improved care, efficiency and better patient outcomes.

What healthcare professionals need to build trust in AI

• Clear guidelines for AI usage and limitations: 45%
• Clarity on legal liability when using AI: 44%
• Scientific evidence supporting AI's effectiveness: 41%
• Continuous monitoring and evaluation of AI systems: 37%
• Reassurance about data security: 31%
• Transparency about the basis of AI recommendations: 30%
• A reliable IT/support helpdesk: 27%
• Addressing data biases and data quality issues: 23%
• Reassurance for staff about job security: 20%

Recommendations

How to build trust in healthcare AI with patients and professionals?

1. Put people first in AI design: AI must be designed around the needs of both patients and healthcare professionals. Involving the right stakeholders from the beginning and throughout the process is essential for building trust and acceptance. Solutions should seamlessly support patient health routines and integrate into healthcare workflows and IT infrastructures, creating a frictionless experience for healthcare professionals and improving patient outcomes.
2. Enhance human-AI collaboration: AI's true potential lies in enhancing healthcare professionals' abilities and empowering patients and caregivers to manage health and well-being. While AI agents may handle certain tasks autonomously, human supervision remains essential when health is at stake. Healthcare professionals play a critical role in building patient trust through transparent communication about the role of AI, supported by comprehensive training starting from the beginning of their education.
3. Demonstrate efficacy and fairness: Both healthcare professionals and patients want assurance that AI works as intended, while regulators require evidence that it meets safety and performance standards. Consistent performance across relevant patient groups and clinical contexts is essential, along with safeguards against bias to support non-discriminatory outcomes. Using representative, high-quality data sets during development and validation can help mitigate biases and ensure fair outcomes for every patient.
4. Enable innovation with clear guardrails: To accelerate the delivery of potentially life-saving AI to patients, regulations should evolve to balance speed of innovation with safeguards that protect patients and build trust. Global harmonization of regulatory frameworks can reduce complexity and enable faster access to innovation without compromising on patient safety. Approaches like regulatory sandboxes can enable the responsible development and monitoring of AI, while maintaining consistent application of medical device regulations.
5. Build strong cross-sector partnerships: In healthcare, no one can go at it alone. Close collaboration across all ecosystem players – including healthcare organizations and professionals, patient groups, payors, policymakers, regulators, researchers and the health tech industry – is crucial for driving innovation and creating solutions that meet stakeholder needs and build trust. Aligned goals and incentives, including payment models, are essential to focus on what matters most: improving the health and well-being of patients and healthcare professionals.

Appendices

Research methodology

Two quantitative surveys* were carried out by Accenture Song, the world's largest tech-powered creative group employing a methodology of online (CAWI) surveying.

The surveys were conducted from December 2024 to March 2025 in 16 countries (Australia, Brazil, Canada, China, France, Germany, India, Indonesia, Japan, Netherlands, Saudi Arabia, Spain, South Africa, South Korea, the United Kingdom and the United States).

Survey 1: 1,926 healthcare professionals participated in a 15-minute online survey

• Healthcare professionals were a mix of doctors (including surgeons), nurses and physician assistants.
• Respondents worked across a range of specialities in private and public health systems.

Survey 2: 16,144 patients aged 18 and older participated in a 10-minute online survey

• Respondents were broadly representative across age and gender within their specific countries.
• 99% of respondents had seen a doctor in the last two years.

Where relevant, the surveys were translated into the local language. In some instances, certain questions needed to be adjusted slightly for relevance within specific countries. Care was taken to ensure the meaning of the question remained as close to the original English version as possible.

In both instances – healthcare professionals and patients - sample sizes were weighted to ensure representative results at the global level.

*Two separate surveys were conducted, but for ease, data is referred to as coming from a 'survey' in the report.

**Survey data is representative of Mainland China only and does not include Taiwan or Hong Kong.

Weighting

Weighting is a statistical technique used to adjust the sample data to ensure it accurately represents the larger population. This process is vital when certain groups are over- or under-represented in the sample compared to their actual proportions in the population.

• Enhances accuracy: Weighting corrects any biases that may arise due to unequal sample sizes across markets.
• Ensures representation: It ensures that the insights obtained reflect the demographics and characteristics of the entire population more accurately.
• Allows comparability: By weighting the data, we can make fair comparisons across different markets and demographics, leading to more reliable conclusions.

The tables below show both the unweighted and weighted sample sizes as well as the estimated margin of error*** at the 95% confidence level.

Please note that this report utilizes weighted data for both healthcare professional and patient surveys to provide insights that are representative across the diverse markets analyzed.

Healthcare professionals survey:

Patient survey:

*** Estimated margin of error is the margin of error that would be associated with a sample of this size for the respondent population in each country.

Glossary of terms

• Artificial intelligence (AI): An AI system is a machine-based system that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments. Different AI systems vary in their levels of autonomy and adaptiveness after deployment.
• Artificial intelligence (AI) algorithms: AI algorithms instruct a computer on how to make decisions, execute a function, or perform some other task independently.
• Artificial intelligence (AI) hallucinations: Responses produced by AI systems that are misleading, inaccurate or nonsensical but are presented as fact.
• Automation: The use of technology and software solutions to perform tasks and processes with limited human involvement. It may involve the application of digital tools, machines, and computer systems to streamline and optimize various aspects of healthcare delivery, administration, and management.
• Data: Used here to refer to a variety of clinical and/or operational information amassed from numerous sources including, but not limited to, electronic medical records (EMR), medical devices and workflow management tools.
• Data bias: A flaw that occurs when certain elements of a dataset are missing, underrepresented or overrepresented.
• Digital health technology: A variety of technology that transmits, shares, and/or analyzes health data. The technology can take a variety of forms, including, but not limited to, home health monitors, digital health records, equipment in hospitals/healthcare facilities, and health or fitness tracker devices.
• Generative AI: AI systems that can create original content in response to a user's prompt or request.
• Healthcare leader: A C-suite or senior executive working in a hospital, medical practice, imaging center/office-based lab, or urgent care facility who is a final decision-maker or has influence in making decisions.
• Healthcare organization: The hospital or healthcare facility for or in which the healthcare professional works.
• Healthcare professional: Individuals who are directly involved in providing healthcare services to patients (including doctors, nurses, surgeons, specialists, technologists, technicians, etc.).
• Out-of-hospital care: Medical services provided outside of traditional hospital settings, such as at home, clinics, ambulatory care centers, or other community locations, either in person or virtually.
• Patient throughput: The efficiency at which a patient moves through a healthcare facility from arrival to discharge.
• Predictive analytics: A branch of advanced analytics that makes predictions about future events, behaviors, and outcomes.
• Remote patient monitoring: Technology that remotely tracks and diagnoses the health of patients.
• Specialist: A doctor or other healthcare professional who is trained and licensed in a specific area of practice. Examples of specialists include oncologists (cancer specialists) and cardiologists (heart specialists).
• Staff: This refers to all employees within a healthcare organization, including healthcare professionals, IT, financial services, administrative support, facilities, etc.
• Workflows: A process involving a series of tasks performed by various people within and between work environments to deliver care. Accomplishing each task may require actions by one person, between people, or across organizations – and can occur sequentially or simultaneously.

PHILIPS

www.philips.com/futurehealthindex-2025

The Future Health Index is commissioned by Philips. In its 10th edition, the Future Health Index 2025 investigates how innovative technologies, particularly AI, can empower healthcare professionals to deliver better care for more people. Two quantitative surveys were carried out among over 1,900 healthcare professionals and over 16,000 patients in 16 countries (Australia, Brazil, Canada, China, France, Germany, India, Indonesia, Japan, Netherlands, Saudi Arabia, Spain, South Africa, South Korea, the United Kingdom and the United States). The surveys were conducted from December 2024 to April 2025.