Feature
Artificial intelligence (AI) has the potential to re-inspire the health care industry. AI may improve health care efficiency, allowing doctors more patient time, employ wearable tools for personalized medical histories and provide additional diagnostic support, among many other applications. It also may act as intellectual support such that a doctor and patient can have a second opinion to help discover the actual diagnosis. McKinsey projects that 15% of current health care work may be AI-automated by 2030. With a global health care industry of more than $10 trillion, AI-enabled health care could have dramatic economic and societal impacts.
Here, we examine various modern applications of AI in health care overall and in three stages of the patient journey: pre-care, care and post-care.
Systemic Changes: AI as a Health Care Trend Accelerant
Accelerated Drug Discovery
New drug development often requires reformulating and re-engineering molecular compounds in creative ways. Google DeepMind’s AlphaFold provides the ability to identify structures for protein folding, and several AI-developed compounds are already in FDA testing. Even before testing, AI’s ability to simulate various outcomes and combinations can improve pre-trial efficacy and prototyping. For example, an auto-trial method uses language models to help define human subject criterion.
Digitized Medical Records
Sometimes medical records are more electronic than digital — storage and accessibility determine the value of data. Software can lack configurations or structure for the complexity of patient care, leaving doctors to find alternative, non-digital solutions. The multimodality of AI models and tools, such as ChatGPT, enable rapid classification, interpretation and digitization of a variety of non-structured information.
More Precise Medicine
If it’s a cold season, someone with a cold likely has a cold. But what if it’s related to a genetic predisposition or abnormality they have? Maybe they’ll have an individualized drug response. This individualization is a key part of a broad trend toward precision medicine for patients. The predictive abilities of AI and the access of higher levels of generalized reasoning make an AI-precision medicine convergence likely.
Increased Data Synthesis
Data collection requirements for doctors have steadily increased over the years, so much so that there is now an abundance of data. One of the major opportunities for using the pattern recognition and predictive summarization abilities of generative AI is to make sense of the overwhelming data that confronts many health care providers. By prioritizing data synthesis over data production doctors can focus more on patient end care.
Proactive Population Health Management
Many health issues occur as a result of poor health habits. Holistic health equity is increasingly possible through the use of wearable devices. Various wearable technologies have already been shown to improve the monitoring of existing health needs, such as infant mortality. Apple devices have built-in anti-sedentary warnings, such as “Time to Stand,” and future applications can likely proactively alert based on baseline trends.
AI in Pre-Care: Before the Patient Arrives
AI-Assisted Scheduling and Reception
Coordinating the logistics of scheduling and sign-in for patients can require an extensive back-of-house operation. Private practice and hospital staff can get squeezed by the complexities of patient operations. Automating and assisting with the scheduling and patient logistics can help reduce burnout for doctors and allow office staff to reorient around the patient experience.
Care: When the Patient is Receiving Health Support
Nuanced Disease Detection
The detection of cancer and other diseases has been a core focus area of AI with varying success. A new AI algorithm from Stanford researchers can identify skin cancer, and Google hopes to identify COVID-19 and tuberculosis by analyzing the sound of a cough. Smart stethoscopes may detect heart failure. Deep learning can identify hidden patterns and consistent trends in ways that an individual human doctor can’t.
Improved Test Interpretation
Using AI to understand and analyze test results is a significant opportunity. Radiology is an often-cited focus area — identifying abnormalities with image recognition could help avoid misdiagnosis. High workload regularly gives radiologists burnout. Early AI radiology applications suggest improvements. In some instances, 44% of tasks can be reduced by using AI to assist in interpretation. Similar opportunities are likely possible across other interpretative roles.
Second Opinion Diagnosis
Asking for a second opinion often requires navigating a complex web of referrals and introductions. Software may provide the ability to provide an on-demand second opinion. IBM’s Watson Health is an early example of a second opinion model, with others being developed. The extent to which such second opinions occur may be a result of culture, as discussions around the ethics and importance of using such advanced models have been happening.
Extensible Robotic Surgery
As AI models have enabled software chatbots, they’ve also improved the types of models available for use in robots. Many surgeries already occur with the surgeon controlling robots remotely with strong results. AI models improve the ability of machines to communicate using natural language, which may open up additional opportunities for more minute and technically complex surgeries too precise for humans.
Post-Care: Supporting Patient Health Recovery
Doctor Dictation and Documentation
Medical charting and dictation looms over the patient care experience for doctors. While dictation technology for doctors is common, AI’s fast multimodality provides new abilities and opportunities. AI-assisted ambient listening technology can create notes in real-time, depending on the patient-doctor context. In one such implementation, the Atlantic Health System saw mass physician adoption of this real-time transcription.
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Mental Health Therapy
Ongoing mental health therapy uses a defined structure of approach, whether guided by the therapist or by the patient. “Talking it out” is a clear application of using chatbots for mental health support. Woebot is one of the most notable applications of this space, but researchers warn of overreach given the sensitivity of mental health. While therapy isn't an officially supported use of base models, like ChatGPT, OpenAI employees have unofficially shared testimonials of ChatGPT providing therapeutic support.
Ongoing Health Monitoring
Maintaining regimens and adhering to prescriptions is a critical part of patient wellness. A research meta analysis before the roll out of large language models (LLMs) found that remote patient monitoring (RPM) was effective but has had challenges of inaccuracy and physician burnout. LLM-supported RPM is an opportunity for patients to get on-demand responsive and interactive health monitoring.
AI-Health Care Integration Requires Human-Centered Considerations
AI implementation in health care requires navigating cultural tensions and workplace precedents. Patient data and security are in tension with AI models’ insatiable desire for data. Problem understanding and analytical discipline are essential for valuable insights. Considerations of bias and ethics are also essential. And perhaps most importantly, the ability to effectively share expertise of AI-health care integration will help not just the local systems, but refine the overall health care industry.
