Top 5 Jobs in Healthcare That Are Most at Risk from AI in Israel - And How to Adapt

Israel's healthcare system is already mid‑transform: with longitudinal electronic health records in place since the 1990s and a booming med‑AI sector, hospitals and startups are using models that can flag brain‑scan anomalies in seconds and run rapid digital pathology reads - changes that make roles in radiology, routine pathology and some administrative tasks prime for automation while creating demand for AI‑literate clinicians and reviewers.

Sheba Medical Center's drive to become an AI hospital - building an AI Center, Sheba AI Academy and Project K - illustrates how clinical workflows, telehealth and predictive care are converging in Israel, where AI startups and R&D labs push hybrid human+AI solutions that augment specialists rather than simply replace them.

For healthcare workers and managers facing this shift, practical reskilling matters: Nucamp's AI Essentials for Work bootcamp teaches workplace AI tools and prompt‑writing in 15 weeks to help staff adapt and lead the change (Register for Nucamp AI Essentials for Work bootcamp).

“AI is no longer an abstract concept - it is a tangible force transforming healthcare. With the AI Center, Sheba AI Academy, and Project K, we are not only embracing AI but embedding it into the very fabric of hospital care.”

Selection of the top five jobs focused on demonstrable risk in Israel's unique ecosystem: priority went to roles exposed to routine, high‑volume tasks that AI already performs elsewhere, plus local evidence of deployment or pilots.

Sources were weighted for Israeli relevance (Sheba Medical Center's

Digital Health

platform and accelerated digital pathology work are treated as high‑signal, see the Sheba profile), real‑world implementation (Aidoc's clinical radiology platform is now in six of Israel's largest hospitals), and scalable, patient‑facing use cases such as CLEW's AI tele‑ICU predictive analytics that have been deployed in Israeli centres.

Context from leading international adopters helped gauge trajectory and workforce impact - especially findings about limited employer training (only ~24% of staff reported employer AI training in recent surveys) and the clear operational ROI vendors report - so jobs with fast, automatable workflows (imaging reads, routine pathology, certain admin tasks) ranked highest.

The methodology therefore combined local deployment evidence, task routineness, documented workflow impact, and workforce‑training gaps to highlight where reskilling will matter most.

Read the Sheba report, Aidoc implementation notes, and CLEW tele‑ICU coverage for the primary evidence base: Sheba Medical Center Digital Health report on AI in Israeli healthcare, Aidoc clinical radiology AI implementation in six major Israeli hospitals, and CLEW AI tele-ICU deployments in Israeli hospitals (COVID-19 support).

Medical administrative roles - receptionists, patient‑service reps and secretaries - sit squarely in the Taub Center's high‑exposure zone: the study flags financial and administrative occupations (including telephone customer service and secretarial work) as especially vulnerable to AI replacement, and notes a sharp rise in exposure between 2023–2024 that already correlates with higher unemployment risk; in practical terms, routine scheduling, repeat call‑handling and standard data‑entry workflows are the most at risk in Israeli clinics and HMOs.

The disruption has a clear social angle too: women (and particularly women in the Arab sector) face disproportionately higher exposure, so any reskilling push must be equity‑aware.

For staff and managers, the immediate takeaway is concrete: prioritize training that moves people from repeatable front‑desk tasks into roles that require judgment or AI supervision, and consult local guidance on workplace AI adoption and data practices to design those pathways (see the Taub Center analysis and Nucamp AI Essentials for Work syllabus - practical guide to using AI in Israeli healthcare for next steps).

“Following our previous study, in which we conducted an initial mapping of exposure to artificial intelligence in the labor market, this study emphasizes the intensification of the technology and shows that in 2024 there was a surge in AI exposure in Israel, especially in occupations at high risk of replacement.”

Clinical coders and medical billers in Israel are squarely in AI's sights: Tel Aviv–based Nym Health - now with offices in Tel Aviv and New York - has pushed automated coding to ~98% accuracy and deployment in nearly 100 hospitals, demonstrating how natural‑language AI can scrub claims, cut denials and speed revenue‑cycle workflows; that same shift speaks directly to Israel's documented need for consistent health‑data coding across hospitals and clinics (Nym Health automated clinical coding system achieves ~98% accuracy, Study on health‑data coding challenges in the Israeli healthcare system).

In practice this means high‑volume abstraction work - routine code assignment, claim scrubbing and eligibility checks - can be handled by AI integrated into EHRs while human coders migrate toward quality‑assurance, complex case review and compliance monitoring; vendors and analyses show the biggest wins come from pairing NLP‑driven suggestions with coder oversight rather than full replacement (AI‑driven medical billing and revenue‑cycle management automation analysis).

The “so what” is simple: teams that learn to validate and interpret AI suggestions become the safety net - turning a paper‑heavy coding queue into a searchable shortlist in seconds, and making human judgment the premium skill.

“(Mistakes) can send you to jail because in their eyes you've committed fraud. Even if you didn't intend to commit fraud, you took money you weren't entitled to receive because you overcharged the federal government through their own code,”

Medical transcriptionists and clinical documentation specialists in Israel are already feeling the nudge from ambient AI scribes that promise big efficiency gains - after 2.5 million uses in one year The Permanente reported roughly 15,000 hours saved, and international reviews point to reduced after‑hours EHR work and better patient–physician presence when tools are well implemented (AMA report on AI scribes saving 15,000 hours and restoring the human side of medicine).

Israeli relevance is clear: recent JMIR analysis (with an author from Holon Institute of Technology) frames these systems as transformative but cautions about accuracy, language/style issues, hallucinations and the need for diligent clinician oversight - concerns that are especially salient in multilingual Israeli settings and fragmented EHR environments (JMIR study: ambient AI scribes in health care - accuracy, language, and clinician oversight).

The practical takeaway: routine transcription and template filling will likely be automated, while human roles should pivot to high‑value review, quality assurance, exception handling and privacy governance; teams that train on validation workflows and data de‑identification best practices will turn documentation automation into safer, time‑backed patient care rather than risky note‑bloat (Nucamp AI Essentials for Work syllabus: data privacy & de-identification best practices), making human judgment the premium skill.

Routine radiology reads and imaging technicians in Israel are already on the frontline of automation: AI tools that analyze CTs and flag critical findings are being integrated into everyday workflows, cutting review time from days to minutes and nudging routine, high‑volume reads toward algorithmic triage.

Startups and hospitals point to real gains - Aidoc's clinical platform is now running in six of Israel's largest medical centers and can prioritize urgent cases like intracranial hemorrhage and pulmonary embolism for faster clinician review (Aidoc clinical platform implemented in six major Israeli hospitals), while national surveys highlight medical imaging as a top AI growth area (Startup Nation Central overview of AI in medical imaging in Israel).

At Assuta, pairing on‑premises compute and federated learning with innovators has meant AI can surface dangerous findings so quickly that staff have been able to call patients while they were on their way home and redirect them back for treatment - making human oversight, triage judgment and federated‑privacy skills the most durable competencies for radiology teams (Assuta and NVIDIA partnership on AI in radiology).

“We saw the impact right away,” said Dr. Michal Guindy, head of medical imaging and head of RISE at Assuta.

Histopathology technicians and pathologists in Israel are already seeing routine reads transformed by AI tools that scan digitized slides and flag actionable biomarkers in minutes - Sheba Medical Center's partnership with Imagene, for example, applies an algorithm directly to conventionally stained biopsy images to accelerate non‑small cell lung cancer profiling and cut diagnostic time from weeks to minutes (Sheba Medical Center Imagene AI cancer diagnostics platform); wider Digital Health initiatives at Sheba show the same drive to embed AI into clinical workflows (Sheba Medical Center Digital Health AI initiatives).

At the same time, Israeli innovators such as Nucleai are turning biopsy images into spatial biomarker maps that help pathologists interpret complex cellular interactions and support trial selection and precision therapeutics (Nucleai spatial biomarker mapping with AI).

The practical reality for labs is clear: routine, high‑volume pattern recognition is becoming algorithmic, while human experts will focus on validating AI flags, integrating spatial insights into reports, and ensuring diagnostic decisions remain clinically sound - what used to take weeks can now arrive in minutes, but only with pathologist oversight.

“We have reached another significant milestone in digital pathology with this ability to detect biomarkers by AI,”

Practical next steps for Israeli healthcare workers and employers start with clarity: the Taub Center found exposure to AI jumped by at least five percentage points between 2023–24 and now flags that roughly 30% of workers may gain from AI while about 23% face significant risk - women and workers in the Arab sector are disproportionately exposed - so action must be immediate, targeted and equitable (Taub Center study: AI and the Israeli labor market).

For employers, that means auditing which roles are routine‑heavy (scheduling, claim coding, routine reads) and investing in short, role‑focused training to pivot people into oversight, validation and exception‑handling duties; for clinicians and staff, prioritize learning AI validation, prompt design and data de‑identification so models become productivity partners instead of replacement threats.

A practical option is a 15‑week, workplace‑focused course that teaches prompt writing, AI tools for common tasks and data‑privacy workflows - Nucamp's AI Essentials for Work syllabus is built for this purpose and can serve as a ready reskilling pathway (Nucamp AI Essentials for Work syllabus (15‑week AI for Work)).

The “so what” is stark: with exposure rising fast, a single targeted reskilling push can turn at‑risk queues into high‑value AI‑supervision roles rather than layoffs, preserving jobs and speeding safer patient care.

“Following our previous study, in which we conducted an initial mapping of exposure to artificial intelligence in the labor market, this study emphasizes the intensification of the technology and shows that in 2024 there was a surge in AI exposure in Israel, especially in occupations at high risk of replacement. Women, and in particular those from the Arab sector, are especially exposed. This is no longer about the distant future, it is about a change taking place here and now.”