The main challenge is knowing how and where to start. Since generative AI has such a broad set of potential applications and MedTech is a highly regulated industry where people’s lives are at stake, some companies have adopted a wait-and-see approach. However, such inaction would risk putting companies at a long-term disadvantage. Generative AI is projected to grow faster in health care than any other industry, with a compound annual growth rate of 85% through 2027, reaching a total market size of $22 billion.

Today, hospital providers and administrative staff are required to complete dozens of forms per patient, not to mention post-visit notes, employee shift notes, and other administrative tasks that take up hours of time and can contribute to hospital employee burnout. Physician groups also contend with the burdens of this administrative work.

With clinician oversight, Gen AI has the potential to generate discharge summaries or instructions in a patient’s native language to better ensure understanding and compliance; synthesize care coordination notes or shift-hand-off notes; and create checklists, lab summaries from physician rounds, and clinical orders in real time. Gen AI’s ability to generate and synthesize language could also improve electronic health records (EHRs). While EHRs allow providers to access and update patient information, they typically require manual inputs and are subject to human error. Gen AI is being actively tested by hospitals and physician groups across everything from prepopulating visit summaries in the EHR to suggesting changes to documentation and providing relevant research for decision support.

Some health systems have already integrated this system into their operations as part of pilot programs. One such startup that has entered the MedTech space is Navina, a clinician first AI platform intended to ingest and interpret  the patients’ health history from EHR, claims, HIE and more, giving clinicians an effective way to quickly understand and explore patients’ histories. By eliminating endless data and document hunting we relieve the burden on physicians, reduce preparation time and the risk of redundant tests and procedures. Securing $44 million in investment already, Navina was founded by Ronen Lavi and Shay Perera, who previously led the Israel Defense Forces’ AI lab, where they say that they built AI “assistant” systems for analysts suffering from data overload.

Macro Level View of Generative AI in MedTech

Outside of streamlining EMR procedures and processes, nearly half of startups were focused on prevention, wellness, diagnosis, and/or detection of disease – not treatment.

According to MedTech Europe, AI tools could protect 400,000 lives, save 200 billion euros, and free up 1.8 billion working hours every year. Given predictions like these, it is unsurprising that the MedTech industry is so keen to invest in the development of artificial technology.

Here in the United States the Mayo Clinic recently started applying AI to electrocardiogram (ECG) data captured by smartwatches. They have developed an iPhone app that uses ECG data to identify patients with weak heart pumps. Another company designing AI-based solutions, Aidoc, can analyze X-ray images and flag positive cases of pneumothorax, or a collapsed lung.

Meanwhile, the vast majority (75%) of AI-enabled devices and platforms are designed for radiology.  Radiology, in particular , is a science made for AI given the volume of highly structured data it generates. AI algorithms can improve image quality and perform initial triage to reduce potential errors or inconsistencies that can occur through human analysis of medical images.

The benefits of Generative AI far exceed its current applications and usages. AI has the potential to touch nearly every aspect of the medical field, providing boundless possibilities and job growth as tech continues to seamlessly integrate itself into modern medicine. Below is a quick analysis of just how Generative AI can streamline the flow of information from provider to patient and from physician back to healthcare provider.

AI for private payers

• Healthcare management: synthesize clinical notes for care managers; synthesize medical and referral information; generate care plans and summaries for members
• Member services: create custom coverage summaries for specific benefits questions, online and via call-center contacts; generate call scripts and other content for outbound nonclinical communications; deploy adaptive chatbots and smart routing to help answer service questions for members and providers; suggest clinicians based on parameters for example coverage, location, preferences, and conditions.
• Provider relationship management: compare plan/product features and networks; generate standard communications for example, welcome letters, reports, new-member needs, and claims denials; summarize gaps in provider directories for example, update open panel; generate reports and observations for providers and vendors on performance and gap closures.
• Corporate functions: generate HR self-serve functions for example first-line interactions/responses and onboarding videos; synthesize requests for proposals (RFPs) and generate responses; draft vendor communications; automate accounting by extracting relevant numbers; generate reports in standard formating; internally summarize updated risk/legal processes as regulation changes; provide large-scale coverage updates for policyholders as regulations change; expedite redetermination processes with enhanced recurring eligibility screening summaries; generate reports and KPIs across functions
• Claims management: generate summaries of manual and denied claims issues and sources to determine solutions; aggregate information for complex claims to reduce processing time; auto-generate summaries and outcomes for prior authorization requests; draft responses to appeals and grievances inquiries.
• Marketing and sales: analyze consumer distribution to develop personalized plans/products; analyze customer feedback by summarizing and extracting themes from online text/images; improve sales support/chatbots to help potential members understand coverage options and choose plans accordingly; create “first draft” materials and product overviews for brokers by product and line of business (LOB), employers, and Affordable Care Act and Medicare Advantage members within guidelines and needed reviews.

AI for hospitals and physician groups

• Continuity of care: summarize discharge information and follow-up needs for post-acute care; generate care summaries for referrals; synthesize specialist notes for primary-care physician team.
• Reimbursement: develop prior authorization documentation for payers; generate a list of current conditions and potential codes based on voice, electronic medical records (EMRs), text, and other data; create care management summaries identifying coding errors across claims; automate coding and checks based on physician notes.
• Clinical operations: generate post-visit summaries and instructions; generate and synthesize care coordination notes, changes in EMRs, dictations, and messages; generate workflow materials and schedules for processes and locations; create educational materials on disease identification and management; develop personalized training journeys for clinicians across types and synthesize requirements of programs.
• Corporate functions: IT – develop code, assist cybersecurity test-case generation and quality assurance); procurement – draft RFPs, contracts, generate reports and KPIs, draft vendor communications, create purchase orders based on supply levels; talent –  assist hiring, generate offer letters and packets, create customized standard operating procedures, create education for new hires, customize onboarding, develop chatbots to address IT and HR questions); finance (generate financial reports; other – generate reports for legal, compliance, and regulatory departments.

Best Practices for AI MedTech Startups

The MedTech space seems to be expanding at an exponential rate. Much of the industry and the overwhelming majority of startups are focused on long term growth. Of the 15,500 MedTech companies actively operating in the US today, 94% are either pre-revenue or have no revenue at all. Meanwhile, Only 130 companies reported revenues of $100M or more. Additionally, 82% of MedTech companies had fewer than 20 employees, according to analysis of 2020 data by Macro Policy Advisors for AdvaMed. Naturally, Generative AI should be integrated into the formation and future of the company as the technology itself becomes further embedded in the medical and healthcare space.

From a bird’s eye view, the MedTech field’s implementation may seem to be based purely on speculation and prognostication toward future growth. However, there has been discernible and concrete data regarding the integration of AI and data analysis to support its versatility in an industry only beginning to adopt a new technology at its infancy. According to a McKinsey analysis, MedTech companies that have started to implement commercial AI use cases are already witnessing significant business benefits and commercial growth. These early-adopter companies are reaping multiple improvements, including a 1.5- to 2.0-fold increase in customer funnel metrics, such as the number of identified leads; 50 percent higher proposal conversion rates; and up to 10 percent increases in incremental revenue.

The general consensus among MedTech executives found that more than 75% of those surveyed expect expanding care settings and delivery models to “significantly re-orient their company’s long-term strategy.” In response, many firms are merely identifying and reappropriating team members to create digital health teams specializing in AI. In another recent survey conducted by Accenture, the Dublin based consulting firm contacted 150 global MedTech senior executives and analyzed over 100 M&A deals as well as more than 600 product launches from a subset of MedTech companies that took place between January 2019 and May 2022. The results clearly indicated the MedTech industry will be better served by “upskilling and reskilling existing staff” while blending and “incubating external talent.”

This begs the question – where are MedTech companies pulling talent from in order to meet the market demand for AI experts and specialists?

MedTech Fields Expected to Grow Along With The Proliferation of AL and LLM in Healthcare

Technical teams tend to lead commercial AI projects in MedTech companies. The assumption is that product managers, data scientists, and data engineers have the talent to deliver the best solutions. However, there is a wide array of growing sectors within the MedTech field to focus on if you are looking to transfer your skills across industries.

Healthcare AI Prompt Engineer: crafts and refines AI prompts and conversational interfaces that drive healthcare interactions such as clinical documentation, patient communications, and interactions with payers.

• Education and Mindset: Bachelor’s, Master’s, or Ph.D. in Computer Science, Data Science, Computational Linguistics, or a related field with a focus on AI and NLP. A background in healthcare data, applicants must understand healthcare-specific language and terminology and be familiar with clinical workflows. Exceptional problem solver. Strong communicator who works effectively across interdisciplinary teams.
• Skills: Proficiency in programming languages used in AI and NLP. Works effectively with database management systems like SQL, and understands healthcare data standards like SNOMED CT and LOINC.

Healthcare AI Project Manager: plans and executes AI projects that harness interdisciplinary teams. Provides the data and analysis necessary to make data-driven decisions.

• Education and Mindset: Bachelor’s or Master’s degree in Project Management, Healthcare Management, Computer Science, or a related field. Problem solver, critical thinker. Excellent communication and leadership abilities.
• Skills: Leverages project management software, data analytics and data visualization tools, and collaboration and communication tools.

Healthcare AI Ethics, Governance, and Compliance Specialists: ​​AI will present huge challenges for the professionals who ensure that healthcare operations comply with all relevant laws, regulations, and ethical standards. These professionals will need to account for AI in compliance audits, risk assessments, and internal investigations.

• Education and Mindset: Law degree or a Bachelor’s or Master’s Degree in Healthcare Administration. Deep working knowledge of regulations that govern data, such as HIPAA.
• Skills: Exceptional analysis and problem solving skills. Attention to detail. Strong organization and communication skills.

Healthcare Cybersecurity and Data Privacy Specialists: Think healthcare information security is challenging now? Strap in for a much bumpier ride. Back in the good old days, cybercriminals needed to know how to code or how to buy pre-packaged codes in order to launch attacks on healthcare systems. Now they are asking AI to do these dark arts for them. Now more than ever, healthcare needs professionals who can prevent such attacks and resolve them when they happen. Many healthcare organizations boosted their 2022 cybersecurity budgets by 15%. Annual cybersecurity spend will continue to climb as systems feed machine learning the data it needs, as some experts predict.

• Education and Mindset: Bachelor’s or Master’s degree in Cybersecurity, Information Technology, Computer Science, or a related field. Relevant certifications such as CISSP. Strong analytical and problem-solving skills. Passion for learning about evolving cybersecurity threats and adapting practices to combat those threats.
• Skills: Proficiency in security tools and technologies, including firewalls, intrusion detection/prevention systems, antivirus software, and endpoint security solutions.

An Imperfect Technology in its Infancy

Not all Generative AI technology is made equal when it comes to MedTech. In a recent study published earlier this month, AI software ChatGPT provided incorrect or incomplete information when asked about drugs, and in some cases invented references to support its answers, two evaluative studies found according to findings presented at the American Society of Health-System Pharmacists (ASHP) at their mid-year meeting.

In the first 39 questions sent to a drug information service for pharmacists, ChatGPT provided no response, an inaccurate response, or an incomplete response to 74% of them, Tina Zerilli,, PharmD, of Long Island University in Brooklyn, New York, and colleagues reported.

Moreover, in instances where the artificial intelligence (AI) chatbot did provide a response with references, it did so each time citing references that were fabricated, with URLs that led to nonexistent studies.

In this new technology available to the public, there is ample room for improvement. However, these technologies exist outside of the MedTech space, aggregating information across deficient vetting systems to properly handle pharmaceutical sourcing. That is another reason why AI systems in the MedTech space need unassailable recruitment practices and a growing talent pool across industries to correct and bolster confidence in this invaluable technology.

At Worky, we believe the future for medicine and health providers in the twenty-first century will be driven by opportunity and a willingness for AI and LLM growth over the coming years. We are always available to help with talent on-demand, strategic hiring planning, and AI consultative services and troubleshooting.

We are merely at the forefront.